Feng Zhao

Consolidation of applications in cloud computing environments presents a significant opportunity for energy optimization. As a first step toward enabling energy efficient consolidation, we study the inter-relationships between energy consumption, resource utilization, and performance of consolidated workloads. The study reveals the energy performance trade-offs for consolidation and shows that optimal operating points exist. We model the consolidation problem as a modified bin packing problem and illustrate it with an example. Finally, we outline the challenges in finding effective solutions to the consolidation problem.

Virtualization is often used in cloud computing platforms for its several advantages in efficiently managing resources. However, virtualization raises certain additional challenges, and one of them is lack of power metering for virtual machines (VMs). Power management requirements in modern data centers have led to most new servers providing power usage measurement in hardware and alternate solutions exist for older servers using circuit and outlet level measurements. However, VM power cannot be measured purely in hardware. We present a solution for VM power metering, named Joulemeter. We build power models to infer power consumption from resource usage at runtime and identify the challenges that arise when applying such models for VM power metering. We show how existing instrumentation in server hardware and hypervisors can be used to build the required power models on real platforms with low error. Our approach is designed to operate with extremely low runtime overhead while providing practically useful accuracy. We illustrate the use of the proposed metering capability for VM power capping, a technique to reduce power provisioning costs in data centers. Experiments are performed on server traces from several thousand production servers, hosting Microsoft's real-world applications such as Windows Live Messenger. The results show that not only does VM power metering allows virtualized data centers to achieve the same savings that non-virtualized data centers achieved through physical server power capping, but also that it enables further savings in provisioning costs with virtualization.

This paper addresses reliable and accurate indoor localization using inertial sensors commonly found on commodity smartphones. We believe indoor positioning is an important primitive that can enable many ubiquitous computing applications. To tackle the challenges of drifting in estimation, sensitivity to phone position, as well as variability in user walking profiles, we have developed algorithms for reliable detection of steps and heading directions, and accurate estimation and personalization of step length. We've built an end-to-end localization system integrating these modules and an indoor floor map, without the need for infrastructure assistance. We demonstrated for the first time a meter-level indoor positioning system that is infrastructure free, phone position independent, user adaptive, and easy to deploy. We have conducted extensive experiments on users with smartphone devices, with over 50 subjects walking over an aggregate distance of over 40 kilometers. Evaluation results showed our system can achieve a mean accuracy of 1.5m for the in-hand case and 2m for the in-pocket case in a 31m×15m testing area.

Energy consumption in hosting Internet services is becoming a pressing issue as these services scale up. Dynamic server provisioning techniques are effective in turning off unnecessary servers to save energy. Such techniques, mostly studied for request-response services, face challenges in the context of connection servers that host a large number of long-lived TCP connections. In this paper, we characterize unique properties, performance, and power models of connection servers, based on a real data trace collected from the deployed Windows Live Messenger. Using the models, we design server provisioning and load dispatching algorithms and study subtle interactions between them. We show that our algorithms can save a significant amount of energy without sacrificing user experiences.

This paper describes information-based approaches to processing and organizing spatially distributed, multimodal sensor data in a sensor network. Energy-constrained networked sensing systems must rely on collaborative signal and information processing (CSIP) to dynamically allocate resources, maintain multiple sensing foci, and attend to new stimuli of interest, all based on task requirements and resource constraints. Target tracking is an essential capability for sensor networks and is used as a canonical problem for studying information organization problems in CSIP. After formulating a CSIP tracking problem in a distributed constrained optimization framework, the paper describes information-driven sensor query and other techniques for tracking individual targets as well as combinatorial tracking problems such as counting targets. Results from simulations and experimental implementations have demonstrated that these information-based approaches are scalable and make efficient use of scarce sensing and communication resources.

Ch 1 Intro. Ch 2 Canonical Problem: Localization and Tracking Ch 3 Networking Sensor Networks Ch 4 Synchronization and Localization Ch 5 Sensor Tasking and Control Ch 6 Sensor Network Database Ch 7 Sensor Network Platforms and Tools Ch 8 Application and Future Direction

Web 2.0 is an emerging paradigm for applications and user interactions. In this article, Aman Kansal, Suman Nath, Jie Liu, and Feng Zhao from Microsoft Research discuss the development of SenseWeb, a peer-produced sensor network environment, used for everyday life decisions.

Anomalies of the omnipresent earth magnetic (i.e., geomagnetic) field in an indoor environment, caused by local disturbances due to construction materials, give rise to noisy direction sensing that hinders any dead reckoning system. In this paper, we turn this unpalatable phenomenon into a favorable one. We present Magicol, an indoor localization and tracking system that embraces the local disturbances of the geomagnetic field. We tackle the low discernibility of the magnetic field by vectorizing consecutive magnetic signals on a per-step basis, and use vectors to shape the particle distribution in the estimation process. Magicol can also incorporate WiFi signals to achieve much improved positioning accuracy for indoor environments with WiFi infrastructure. We perform an in-depth study on the fusion of magnetic and WiFi signals. We design a two-pass bidirectional particle filtering process for maximum accuracy, and propose an on-demand WiFi scan strategy for energy savings. We further propose a compliant-walking method for location database construction that drastically simplifies the site survey effort. We conduct extensive experiments at representative indoor environments, including an office building, an underground parking garage, and a supermarket in which Magicol achieved a 90 percentile localization accuracy of 5 m, 1 m, and 8 m, respectively, using the magnetic field alone. The fusion with WiFi leads to 90 percentile accuracy of 3.5 m for localization and 0.9 m for tracking in the office environment. When using only the magnetism, Magicol consumes 9 × less energy in tracking compared to WiFi-based tracking.

Mobile applications often need location data, to update locally relevant information and adapt the device context. While most smartphones do include a GPS receiver, it's frequent use is restricted due to high battery drain. We design and prototype an adaptive location service for mobile devices, a-Loc, that helps reduce this battery drain. Our design is based on the observation that the required location accuracy varies with location, and hence lower energy and lower accuracy localization methods, such as those based on WiFi and cell-tower triangulation, can sometimes be used. Our method automatically determines the dynamic accuracy requirement for mobile search-based applications. As the user moves, both the accuracy requirements and the location sensor errors change. A-Loc continually tunes the energy expenditure to meet the changing accuracy requirements using the available sensors. A Bayesian estimation framework is used to model user location and sensor errors. Experiments are performed with Android G1 and AT&T Tilt phones, on paths that include outdoor and indoor locations, using war-driving data from Google and Microsoft. The experiments show that a-Loc not only provides significant energy savings, but also improves the accuracy achieved, because it uses multiple sensors.

Automated and scalable approaches for understanding the semantics of places are critical to improving both existing and emerging mobile services. In this paper, we present [email protected] (CSP), a framework that exploits a previously untapped resource -- opportunistically captured images and audio clips from smartphones -- to link place visits with place categories (e.g., store, restaurant). CSP combines signals based on location and user trajectories (using WiFi/GPS) along with various visual and audio place "hints" mined from opportunistic sensor data. Place hints include words spoken by people, text written on signs or objects recognized in the environment. We evaluate CSP with a seven-week, 36-user experiment involving 1,241 places in five locations around the world. Our results show that CSP can classify places into a variety of categories with an overall accuracy of 69%, outperforming currently available alternative solutions.

Exploiting the increasingly wide use of Light-emitting Diode (LED) lighting, in this paper, we study the problem of using visible LED lights for accurate localization. The basic idea is to leverage the existing lighting infrastructure and apply trilateration to localize any devices with light sensing capability (e.g., a smartphone), using LED lamps as anchors. Through the design of Epsilon, we identify and tackle several technique challenges. In particular, we establish and experimentally verify the optical channel model for localization. We adopt BFSK and channel hopping to enable reliable location beaconing from multiple, uncoordinated light sources over the shared optical medium. We handle realistic situations towards robust localization, for example, we exploit user involvement to resolve the ambiguity in case of insufficient LED anchors. We have implemented the Epsilon system and evaluated it with a small scale hardware testbed as well as moderate-size simulations. Experimental results confirmed the effectiveness of Epsilon: the 90th percentile accuracies are 0.4m, 0.7m and 0.8m for three typical office environments. Even in the extreme situation with a single light, the 90th percentile accuracy is 1.1m. We believe that visible light based localization is promising to significantly improve the positioning accuracy, despite few open problems in practice.

Existing graph few-shot learning (FSL) methods usually train a model on many task graphs and transfer the learned model to a new task graph. However, the task graphs often contain a great number of isolated nodes, which results in the severe deficiency of learned node embeddings. Furthermore, in the training process, the neglect of task information also constrains the model’s expressive ability. In this brief, we propose a novel metric-based graph few-shot learning approach via restructuring task graph (GFL-RTG). To solve the problems above, we innovatively restructure the task graph by adding class nodes and a task node to the original individual task graph. We first add class nodes and determine the connectivity between class nodes and others via their similarity. Then, we utilize a graph pooling network to learn a task embedding, which is regarded as a task node. Finally, the new task graph is restructured by combining class nodes, task node, and original nodes, which is then used as input to the metric-based graph neural network (GNN) to conduct few-shot learning. Our extensive experiments on three graph-structured datasets demonstrate that our proposed method generally outperforms the state-of-the-art baselines in few-shot learning.

This paper presents a class of signal processing techniques for collaborative signal processing in ad hoc sensor networks, focusing on a vehicle tracking application. In particular, we study two types of commonly used sensors—acoustic-amplitude sensors for target distance estimation and direction-of-arrival sensors for bearing estimation—and investigate how networks of such sensors can collaborate to extract useful information with minimal resource usage. The information-driven sensor collaboration has several advantages: tracking is distributed, and the network is energy-efficient, activated only on a when-needed basis. We demonstrate the effectiveness of the approach to target tracking using both simulation and field data.

We present a web service based approach to enable an evolutionary sensornet system where additional sensor nodes may be added after the initial deployment. The functionality and data provided by the new nodes is exposed in a structured manner, so that multiple applications may access them. The result is a highly inter-operable system where multiple applications can share a common evolving sensor substrate. A key challenge in using web services on resource constrained sensor nodes is the energy and bandwidth overhead of the structured data formats used in web services. Our work provides a detailed evaluation of the overheads and presents an implementation on a representative sensor platform with 48k of ROM, 10k of RAM and a 802.15.4 radio. We identify design choices that optimize the web service operation on resource constrained sensor nodes, including support for low latency messaging and sleep modes, quantifying trade-offs between the design generality and resource efficiency. We also prototyped an example application, for home energy management, demonstrating how evolutionary sensor networks can be supported with our approach.

A great opportunity exists to fuse information from populations of privately-held sensors to create useful sensing applications. For example, GPS devices, embedded in cellphones and automobiles, might one day be employed as distributed networks of velocity sensors for traffic monitoring and routing. Unfortunately, privacy and resource considerations limit access to such data streams. We describe principles of community sensing that offer mechanisms for sharing data from privately held sensors. The methods take into account the likely availability of sensors, the context-sensitive value of sensor information, based on models of phenomena and demand, and sensor owners' preferences about privacy and resource usage. We present efficient and well-characterized approximations of optimal sensing policies. We provide details on key principles of community sensing and highlight their use within a case study for road traffic monitoring.

Location knowledge of nodes in a network is essential for many tasks such as routing, cooperative sensing, or service delivery in ad hoc, mobile, or sensor networks. This paper introduces a novel iterative method ILS for node localization starting with a relatively small number of anchor nodes in a large network. At each iteration, nodes are localized using a least-squares based algorithm. The computation is lightweight, fast, and any-time. To prevent error from propagating and accumulating during the iteration, the error control mechanism of the algorithm uses an error registry to select nodes that participate in the localization, based on their relative contribution to the localization accuracy. Simulation results have shown that the active selection strategy significantly mitigates the effect of error propagation. The algorithm has been tested on a network of Berkeley Mica2 motes with ultrasound TOA ranging devices. We have compared the algorithm with more global methods such as MDS-MAP and SDP-based algorithm both in simulation and on real hardware. The iterative localization achieves comparable location accuracy in both cases, compared to the more global methods, and has the advantage of being fully decentralized.

We present a framework called Semantic Streams that allows users to pose declarative queries over semantic interpretations of sensor data. For example, instead of querying raw magnetometer data, the user queries whether vehicles are cars or trucks; the system decides which sensor data and which operations to use to infer the type of vehicle. The user can also place constraints on values such as the the amount of energy consumed or the confidence with which the vehicles are classified. We demonstrate how this system can be used on a network of video, magnetometer, and infrared break beam sensors deployed in a parking garage with three simultaneous and independent users.

Many networked embedded sensing and control systems can be modeled as hybrid systems with interacting continuous and discrete dynamics. These systems present significant challenges for monitoring and diagnosis. Many existing model-based approaches focus on diagnostic reasoning assuming appropriate fault signatures have been generated. However, an important missing piece is the integration of model-based techniques with the acquisition and processing of sensor signals and the modeling of faults to support diagnostic reasoning. This paper addresses key modeling and computational problems at the interface between model-based diagnosis techniques and signature analysis to enable the efficient detection and isolation of incipient and abrupt faults in hybrid systems. A hybrid automata model that parameterizes abrupt and incipient faults is introduced. Based on this model, an approach for diagnoser design is presented. The paper also develops a novel mode estimation algorithm that uses model-based prediction to focus distributed processing signal algorithms. Finally, the paper describes a diagnostic system architecture that integrates the modeling, prediction, and diagnosis components. The implemented architecture is applied to fault diagnosis of a complex electro-mechanical machine, the Xerox DC265 printer, and the experimental results presented validate the approach. A number of design trade-offs that were made to support implementation of the algorithms for online applications are also described.

Fueled by the widespread adoption of sensor-enabled smartphones, mobile crowdsourcing is an area of rapid innovation. Many crowd-powered sensor systems are now part of our daily life -- for example, providing highway congestion information. However, participation in these systems can easily expose users to a significant drain on already limited mobile battery resources. For instance, the energy burden of sampling certain sensors (such as WiFi or GPS) can quickly accumulate to levels users are unwilling to bear. Crowd system designers must minimize the negative energy side-effects of participation if they are to acquire and maintain large-scale user populations.

Sensor-enabled smartphones are opening a new frontier in the development of mobile sensing applications. The recognition of human activities and context from sensor-data using classification models underpins these emerging applications. However, conventional approaches to training classifiers struggle to cope with the diverse user populations routinely found in large-scale popular mobile applications. Differences between users (e.g., age, sex, behavioral patterns, lifestyle) confuse classifiers, which assume everyone is the same. To address this, we propose Community Similarity Networks (CSN), which incorporates inter-person similarity measurements into the classifier training process. Under CSN every user has a unique classifier that is tuned to their own characteristics. CSN exploits crowd-sourced sensor-data to personalize classifiers with data contributed from other similar users. This process is guided by similarity networks that measure different dimensions of inter-person similarity. Our experiments show CSN outperforms existing approaches to classifier training under the presence of population diversity.

Significant opportunities for power optimization exist at application design stage and are not yet fully exploited by system and application designers. We describe the challenges developers face in optimizing software for energy efficiency by exploiting application-level knowledge. To address these challenges, we propose the development of automated tools that profile the energy usage of various resource components used by an application and guide the design choices accordingly. We use a preliminary version of a tool we have developed to demonstrate how automated energy profiling helps a developer choose between alternative designs in the energy-performance trade-off space.

RACNet is a sensor network for monitoring a data center's environmental conditions. The high spatial and temporal fidelity measurements that RACNet provides can be used to improve the data center's safety and energy efficiency. RACNet overcomes the network's large scale and density and the data center's harsh RF environment to achieve data yields of 99% or higher over a wide range of network sizes and sampling frequencies. It does so through a novel Wireless Reliable Acquisition Protocol (WRAP). WRAP decouples topology control from data collection and implements a token passing mechanism to provide network-wide arbitration. This congestion avoidance philosophy is conceptually different from existing congestion control algorithms that retroactively respond to congestion. Furthermore, WRAP adaptively distributes nodes among multiple frequency channels to balance load and lower data latency. Results from two testbeds and an ongoing production data center deployment indicate that RACNet outperforms previous data collection systems, especially as network load increases.

Crowd-enabled place-centric systems gather and reason over large mobile sensor datasets and target everyday user locations (such as stores, workplaces, and restaurants). Such systems are transforming various consumer services (for example, local search) and data-driven organizations (city planning). As the demand for these systems increases, our understanding of how to design and deploy successful crowdsensing systems must improve. In this paper, we present a systematic study of the coverage and scaling properties of place-centric crowdsensing. During a two-month deployment, we collected smartphone sensor data from 85 participants using a representative crowdsensing system that captures 48,000 different place visits. Our analysis of this dataset examines issues of core interest to place-centric crowdsensing, including place-temporal coverage, the relationship between the user population and coverage, privacy concerns, and the characterization of the collected data. Collectively, our findings provide valuable insights to guide the building of future place-centric crowdsensing systems and applications.

We present Travi-Navi - a vision-guided navigation system that enables a self-motivated user to easily bootstrap and deploy indoor navigation services, without comprehensive indoor localization systems or even the availability of floor maps. Travi-Navi records high quality images during the course of a guider's walk on the navigation paths, collects a rich set of sensor readings, and packs them into a navigation trace. The followers track the navigation trace, get prompt visual instructions and image tips, and receive alerts when they deviate from the correct paths. Travi-Navi also finds the most efficient shortcuts whenever possible. We encounter and solve several challenges, including robust tracking, shortcut identification, and high quality image capture while walking. We implement Travi-Navi and conduct extensive experiments. The evaluation results show that Travi-Navi can track and navigate users with timely instructions, typically within a 4-step offset, and detect deviation events within 9 steps.

Diversity in training data density and environment locality is intrinsic in the real-world deployment of indoor localization systems and has a major impact on the performance of existing localization approaches. In this paper, through micro-benchmarks, we find that fingerprint-based approaches are preferable in scenarios where a dense database is available; while model-based approaches are the method of choice in the case of sparse data. It should be noted, however, that practical situations are complex. A single deployment often features both sparse and dense sampled areas. Furthermore, the internal layout affects the propagation of radio signals and exhibits environmental impacts. A certain number of measurement samples may be sufficient for one part of the building, but entirely insufficient for another. Thus, finding the right indoor localization algorithm for a given large-scale deployment is challenging, if not impossible; there is no one-size-fits-all indoor localization approach.

The ubiquity of the lighting infrastructure makes the visible light communication (VLC) well suited for mobile and Internet of Things (IoT) applications in the indoor environment. However, existing VLC systems have primarily been focused on one-way communications from the illumination infrastructure to the mobile device. They are power demanding and not applicable for communication in the opposite direction. In this paper, we present RetroVLC, a duplex VLC system that enables a battery-free device to perform bi-directional communications over a shared light carrier across the uplink and downlink. The design features a retro-reflector fabric that backscatters light, an LCD modulator, and several low-power optimization techniques. We have prototyped a working system consisting of a credit card-sized battery-free tag and an illuminating LED reader. Experimental results show that the tag can achieve 10kbps downlink speed and 0.5kbps uplink speed over a distance of 2.4m. We outline several potential applications and limitations of the system.

We present Travi-Navi-a vision-guided navigation system that enables a self-motivated user to easily bootstrap and deploy indoor navigation services, without comprehensive indoor localization systems or even the availability of floor maps. Travi-Navi records high-quality images during the course of a guider's walk on the navigation paths, collects a rich set of sensor readings, and packs them into a navigation trace. The followers track the navigation trace, get prompt visual instructions and image tips, and receive alerts when they deviate from the correct paths. Travi-Navi also finds shortcuts whenever possible. In this paper, we describe the key techniques to solve several practical challenges, including robust tracking, shortcut identification, and high-quality image capture while walking. We implement Travi-Navi and conduct extensive experiments. The evaluation results show that Travi-Navi can track and navigate users with timely instructions, typically within a four-step offset, and detect deviation events within nine steps. We also characterize the power consumption of Travi-Navi on various mobile phones.

Intelligentization is the development trend of the future automobile industry. Intelligentization requires that the dynamic control of the vehicle can complete the trajectory tracking according to the trajectory output of the decision planning the driving state of the vehicle and ensure the driving safety and stability of the vehicle. However, trajectory limit planning and harsh road conditions caused by emergencies will increase the difficulty of trajectory tracking and stability control of unmanned vehicles. In view of the above problems, this paper studies the trajectory tracking and stability control of distributed drive unmanned vehicles. This paper applies a hierarchical control framework. Firstly, in the upper controller, an adaptive prediction time linear quadratic regulator (APT LQR) path following algorithm is proposed to acquire the desired front-wheel-steering angle considering the dynamic stability performance of the tires. The lateral stability of the DDAUV is determined based on the phase plane, and the sliding surface, in the improved sliding mode control (SMC), is further dynamically adjusted to obtain the desired additional yaw moment for coordinating the path following and lateral stability. Then, in the lower controller, considering the slip and the working load of four tires, a comprehensive cost function is established to reasonably distribute the driving torque of four in-wheel motors (IWMs) for producing the desired additional yaw moment. Finally, the proposed control algorithm is verified by the hardware-in-the-loop (HIL) experiment platform. The results show the path following and lateral stability can be coordinated effectively under different driving conditions.

This article overviews the information-driven approach to in ad hoc networks. The main idea is for a network to determine participants in a sensor collaboration by dynamically optimizing the information utility of data for a given cost of communication and computation. A definition of information utility is introduced, and several approximate measures of the information utility are developed for reasons of computational tractability. We illustrate the use of this approach using examples drawn from tracking applications.

The development of lightweight sensing andcommunication protocols is a key requirement for designing resource constrained sensor networks. This paper introduces a set of efficient protocols and algorithms, DAM, EBAM, and EMLAM, for constructing and maintaining sensor aggregates that collectively monitor target activity in the environment. A sensor aggregate comprises those nodes in a network that satisfy a grouping predicate for a collaborative processing task. The parameters of the predicate depend on the task and its resource requirements. Since the foremost purpose of a sensor network is to selectively gather information about the environment, the formation of appropriate sensor aggregates is crucial for optimally allocating resources to sensing and communication tasks.This paper makes minimal assumptions about node onboard processing and communication capabilities so as to allow possible implementations on resource-constrained hardware. Factors affecting protocol performance are discussed. The paper presents simulation results showing how the protocol performance varies as key network and task parameters are varied. It also provides probabilistic analyses of network behavior consistent with the simulation results. The protocols have been experimentally validated on a sensor network testbed comprising 25 Berkeley MICA sensor motes.

The tradeoff between performance and scalability is a fundamental issue in distributed sensor networks. In this paper, we propose a novel scheme to efficiently organize and utilize network resources for target localization. Motivated by the essential role of geographic proximity in sensing, sensors are organized into geographically local collaborative groups. In a target tracking context, we present a dynamic group management method to initiate and maintain multiple tracks in a distributed manner. Collaborative groups are formed, each responsible for tracking a single target. The sensor nodes within a group coordinate their behavior using geographically-limited message passing. Mechanisms such as these for managing local collaborations are essential building blocks for scalable sensor network applications.

Geocentric Web interfaces such as Microsoft Virtual Earth and Google Maps are useful for visualizing spatially and geographically related data such as driving directions, directory entries, and weather and traffic conditions, to name a few. The desire to add useful information to these interfaces has led developers to create custom applications that overlay housing prices, crime rates, bus locations, and other data on top of browsable maps. These applications are possible due to useful APIs that Google Maps and Microsoft Virtual Earth publish to overlay location data on maps. We envision a new class of applications that relies on real-time sensor data and its mash-up with the geocentric Web to provide instantaneous environmental visibility and timely decision support.

Correlation is widely used as an effective similarity measure in matching tasks. However, traditional correlation based matching methods are limited to the short baseline case. In this paper we propose a new correlation based method for matching two images with large camera motion. Our method is based on the rotation and scale invariant normalized cross-correlation. Both the size and the orientation of the correlation windows are determined according to the characteristic scale and the dominant direction of the interest points. Experimental results on real images demonstrate that the new method is effective for matching image pairs with significant rotation and scale changes as well as other common imaging conditions.

Sensor convergence on the mobile phone is spawning a broad base of new and interesting mobile applications. As applications grow in sophistication, raw sensor readings often require classification into more useful application-specific high-level data. For example, GPS readings can be classified as running, walking or biking. Unfortunately, traditional classifiers are not built for the challenges of mobile systems: energy, latency, and the dynamics of mobile.

Data Centers are energy consuming facilities that host Internet services such as cloud computing platforms. Their complex cyber and physical systems bring unprecedented challenges in resource managements. In this paper, we give an overview of the resource provisioning and utilization patterns in data centers and propose a macro-resource management layer to coordinate among cyber-and-physical resources. We review some existing work and solutions in the field and explain their limitations. We give some future research directions and the potential solutions to jointly optimize computing and environmental resources in data centers.

MusicalHeart is a biofeedback-based, context-aware, automated music recommendation system for smartphones. We introduce a new wearable sensing platform, Septimu, which consists of a pair of sensor-equipped earphones that communicate to the smartphone via the audio jack. The Septimu platform enables the MusicalHeart application to continuously monitor the heart rate and activity level of the user while listening to music. The physiological information and contextual information are then sent to a remote server, which provides dynamic music suggestions to help the user maintain a target heart rate. We provide empirical evidence that the measured heart rate is 75% -- 85% correlated to the ground truth with an average error of 7.5 BPM. The accuracy of the person-specific, 3-class activity level detector is on average 96.8%, where these activity levels are separated based on their differing impacts on heart rate. We demonstrate the practicality of MusicalHeart by deploying it in two real world scenarios and show that MusicalHeart helps the user achieve a desired heart rate intensity with an average error of less than 12.2%, and its quality of recommendation improves over time.

Scalable and comprehensive testing of mobile apps is extremely challenging. Every test input needs to be run with a variety of contexts, such as: device heterogeneity, wireless network speeds, locations, and unpredictable sensor inputs. The range of values for each context, e.g. location, can be very large. In this paper we present Caiipa, a cloud service for testing apps over an expanded mobile context space in a scalable way. It incorporates key techniques to make app testing more tractable, including a context test space prioritizer to quickly discover failure scenarios for each app. We have implemented Caiipa on a cluster of VMs and real devices that can each emulate various combinations of contexts for tablet and phone apps. We evaluate Caiipa by testing 265 commercially available mobile apps based on a comprehensive library of real-world conditions. Our results show that Caiipa leads to improvements of 11.1x and 8.4x in the number of crashes and performance bugs discovered compared to conventional UI-based automation (i.e., monkey-testing).

Web browsing is a key application on mobile devices. However, mobile browsers are largely optimized for performance, imposing a significant burden on power-hungry mobile devices. In this work, we aim to reduce the energy consumed to load web pages on smartphones, preferably without increasing page load time and compromising user experience. To this end, we first study the internals of web page loading on smartphones and identify its energy-inefficient behaviors. Based on our findings, we then derive general design principles for energy-efficient web page loading, and apply these principles to the open-source Chromium browser and implement our techniques on commercial smartphones. Experimental results show that our techniques are able to achieve a 24.4% average system energy saving for Chromium on a latest-generation big.LITTLE smartphone using WiFi (a 22.5% saving when using 3G), while not increasing average page load time. We also show that our proposed techniques can bring a 10.5% system energy saving on average with a small 1.69\% increase in page load time for mobile Firefox web browser. User study results indicate that such a small increase in page load time is hardly perceivable.

As the number of connected devices explodes, the use scenarios of these devices and data have multiplied. Many of these scenarios, e.g., home automation, require tools beyond data visualizations, to express user intents and to ensure interactions do not cause undesired effects in the physical world. We present SIFT, a safety-centric programming platform for connected devices in IoT environments. First, to simplify programming, users express high-level intents in declarative IoT apps. The system then decides which sensor data and operations should be combined to satisfy the user requirements. Second, to ensure safety and compliance, the system verifies whether conflicts or policy violations can occur within or between apps. Through an office deployment, user studies, and trace analysis using a large-scale dataset from a commercial IoT app authoring platform, we demonstrate the power of SIFT and highlight how it leads to more robust and reliable IoT apps.

Distributed embedded systems such as wireless sensor and actuator networks require new programming models and software tools to support the rapid design and prototyping of sensing and control applications. Unlike centralized platforms and Web-based distributed systems, these distributed sensor-actuator network (DSAN) systems are characterized by a massive number of potentially failing nodes, limited energy and bandwidth resources, and the need to rapidly respond to sensor input. We describe a state-centric, agent-based design methodology to mediate between a system developer's mental model of physical phenomena and the distributed execution of DSAN applications. Building on the ideas of data-centric networking, sensor databases, and proximity-based group formation, we introduce the notion of collaboration groups, which abstracts common patterns in application-specific communication and resource allocation. Using a distributed tracking application with sensor networks, we'll demonstrate how state-centric programming can raise the abstraction level for application developers.

This paper presents a scalable distributed algorithm for computing and maintaining multi-target identity information. The algorithm builds on a novel representational framework, Identity-Mass Flow, to overcome the problem of exponential computational complexity in managing multi-target identity explicitly. The algorithm uses local information to efficiently update the global multi-target identity information represented as a doubly stochastic matrix, and can be efficiently mapped to nodes in a wireless ad hoc sensor network. The paper describes a distributed implementation of the algorithm in sensor networks. Simulation results have validated the Identity-Mass Flow framework and demonstrated the feasibility of the algorithm.

Networked embedded systems such as wireless sensor networks are usually designed to be event-driven so that they are reactive and power efficient. Programming embedded systems with multiple reactive tasks is difficult due to the complex nature of managing the concurrency of execution threads and consistency of shared states. This paper describes a globally asynchronous and locally synchronous model (TinyGALS) for programming event-driven embedded systems. Software components are composed locally through synchronous method calls to form modules, and asynchronous message passing is used between modules to separate the flow of control. In addition, a guarded yet synchronous model (TinyGUYS) is designed to allow thread-safe sharing of global state by multiple modules without explicitly passing messages. This programming model is structured such that all asynchronous message passing code and module triggering mechanisms can be automatically generted from a high-level specification. We have implemented the programming model and code generation facilities on a wireless sensor network platform known as the Berkeley motes. As an example, we have redesigned a multi-hop ad hoc communication protocol using the TinyGALS model.

Mobile phones have two sensors: a camera and a microphone. The widespread and ubiquitous nature of mobile phones around the world makes it attractive to build a large-scale sensor network using the phones as its sensor nodes. There are several interesting challenges in realizing such a system, such as providing efficient methods for the sensor nodes to make their data available to the network, allowing the sensor network applications to access the data from potentially disconnected and highly mobile devices, ensuring that privacy constraints are met, and allowing application developers to program the sensor network as required to build new applications. We demonstrate an initial system prototype that addresses some of these concerns.

Indoor physical analytics calls for high-accuracy localization that existing indoor (e.g., WiFi-based) localization systems may not offer. By exploiting the ever increasingly wider adoption of LED lighting, in this paper, we study the problem of using visible LED lights for accurate localization. We identify the key challenges and tackle them through the design of Pharos. In particular, we establish and experimentally verify an optical channel model suitable for localization. We adopt BFSK and channel hopping to achieve reliable location beaconing from multiple, uncoordinated light sources over shared light medium. Preliminary evaluation shows that Pharos achieves the 90th percentile localization accuracy of 0.4m and 0.7m for two typical indoor environments. We believe visible light based localization holds the potential to significantly improve the position accuracy, despite few potential issues to be conquered in real deployment.

Advances and standards in Internet of Things (IoT) have simplified the realization of building automation. However, non-expert IoT users still lack tools that can help them to ensure the underlying control system correctness: user-programmable logics match the user intention. In fact, non-expert IoT users lack the necessary know-how of domain experts. This paper presents our experience in running a building automation service based on the Salus framework. Complementing efforts that simply verify the IoT control system correctness, Salus takes novel steps to tackle practical challenges in automated debugging of identified policy violations, for non-expert IoT users. First, Salus leverages formal methods to localize faulty user-programmable logics. Second, to debug these identified faults, Salus selectively transforms the control system logics into a set of parameterized equations, which can then be solved by popular model checking tools or SMT (Satisfiability Modulo Theories) solvers. Through office deployments, user studies, and public datasets, we demonstrate the usefulness of Salus in systematically debugging the correctness of IoT control systems for building automation.

Wireless ad hoc sensor networks have the advantage of spanning a large geographical region and being able to collaboratively detect and track non-local spatio-temporal events. This paper presents a dual-space approach to event tracking and sensor resource management in sensor networks. The dual-space transformation maps a non-local phenomenon, e.g., the edge of a half-plane shadow, to a single point in the dual space, and maps locations of distributed sensor nodes to a set of lines that partitions the dual space. The detection problem becomes finding and tracking the cell that contains the point in the arrangement defined by these lines. This mechanism can be effectively used for power management of the sensor network - nodes that will not be immediately visited by an event can be turned off to save energy required for sensing, processing, and communication. The approach has been successfully demonstrated on a laboratory testbed built using the UC Berkeley motes sensors. An implemented application of detecting and tracking light shadow edges moving over a sensor field is described.

This volume contains the Proceedings of the 2nd International Workshop on Information Processing in Sensor Networks (IPSN 2003). The workshop was held at the Palo Alto Research Center (PARC), Palo Alt

This paper investigates the problem of designing decentralized representations to support monitoring and inferences in sensor networks. State-space models of physical phenomena such as those arising from tracking multiple interacting targets, while commonly used in signal processing and control, suffer from the curse of dimensionality as the number of phenomena of interest increases. Furthermore, mapping an inference algorithm onto a distributed sensor network must appropriately allocate scarce sensing and communication resources. We address the state-space explosion problem by developing a distributed state-space model that switches between factored and joint state spaces as appropriate. We develop a collaborative group abstraction as a mechanism to effectively support the information ow within and across subspaces of the state-space model, which can be efficiently supported in a communication-constrained network. The approach has been implemented and demonstrated in a simulation of tracking multiple interacting targets.

This paper describes the architecture and programming model of a semantic-service-oriented sensor information system platform. We argue that the key to enabling scalable sensor information access is to define an ontology and associated sensor information hierarchy for interpretation of raw data streams. The ontological abstraction allows a sensing system to optimize its resource utilization in collecting, storing, and processing data. We describe the SONGS architecture that uses an automatic service planning to convert declarative user queries into a service composition graph, and performs compile-time and run-time optimizations for resource-aware execution of the service composite in a sensor network, building on the sensor information hierarchy. We motivate and demonstrate the SONGS platform using a parking garage example

We present Cypress, a novel framework to archive and query massive time series streams such as those generated by sensor networks, data centers, and scientific computing. Cypress applies multi-scale analysis to decompose time series and to obtain sparse representations in various domains (e.g. frequency domain and time domain). Relying on the sparsity, the time series streams can be archived with reduced storage space. We then show that many statistical queries such as trend, histogram and correlations can be answered directly from compressed data rather than from reconstructed raw data. Our evaluation with server utilization data collected from real data centers shows significant benefit of our framework.

Embedded systems with heterogeneous processors extend the energy/timing trade-off flexibility and provide the opportunity to fine tune resource utilization for particular applications. In this paper, we present a resource model that considers the time and energy costs of run-time mode switching, which considerably improves the accuracy of existing models. Given an application, the software partitioning problem then becomes an optimization over energy cost given deadline constraints, which can be formulate as an integer linear programming (ILP) problem. We apply the resource modeling and software partitioning techniques to a multimodule embedded sensing device, the mPlatform, and present a case study of configuring the platform for a real-time sound source localization application on a stack of MSP430 and ARM7 processor based sensing and processing boards.

Local search engines are very popular but limited. We present Hapori, a next-generation local search technology for mobile phones that not only takes into account location in the search query but richer context such as the time, weather and the activity of the user. Hapori also builds behavioral models of users and exploits the similarity between users to tailor search results to personal tastes rather than provide static geo-driven points of interest. We discuss the design, implementation and evaluation of the Hapori framework which combines data mining, information preserving embedding and distance metric learning to address the challenge of creating efficient multidimensional models from context-rich local search logs. Our experimental results using 80,000 queries extracted from search logs show that contextual and behavioral similarity information can improve the relevance of local search results by up to ten times when compared to the results currently provided by commercially available search engine technology.

The ever increasing popularity of social networks and the ever easier photo taking and sharing experience have led to unprecedented concerns on privacy infringement. Inspired by the fact that the Robot Exclusion Protocol, which regulates web crawlers' behavior according a per-site deployed robots.txt, and cooperative practices of major search service providers, have contributed to a healthy web search industry, in this paper, we propose Privacy Expressing and Respecting Protocol (PERP) that consists of a Privacy.tag -- a physical tag that enables a user to explicitly and flexibly express their privacy deal, and Privacy Respecting Sharing Protocol (PRSP) -- a protocol that empowers the photo service provider to exert privacy protection following users' policy expressions, to mitigate the public's privacy concern, and ultimately create a healthy photo-sharing ecosystem in the long run. We further design an exemplar Privacy.Tag using customized yet compatible QR-code, and implement the Protocol and study the technical feasibility of our proposal. Our evaluation results confirm that PERP and PRSP are indeed feasible and incur negligible computation overhead.

Visual thinking plays an important role in scientific reasoning. Based on the research in automating diverse reasoning tasks about dynamical systems, nonlinear controllers, kinematic mechanisms, and fluid motion, we have identified a style of visual thinking, imagistic reasoning. Imagistic reasoning organizes computations around image-like, analogue representations so that perceptual and symbolic operations can be brought to bear to infer structure and behavior. Programs incorporating imagistic reasoning have been shown to perform at an expert level in domains that defy current analytic or numerical methods. We have developed a computational paradigm, spatial aggregation, to unify the description of a class of imagistic problem solvers. A program written in this paradigm has the following properties. It takes a continuous field and optional objective functions as input, and produces high-level descriptions of structure, behavior, or control actions. It computes a multi-layer of intermediate representations, called spatial aggregates, by forming equivalence classes and adjacency relations. It employs a small set of generic operators such as aggregation, classification, and localization to perform bidirectional mapping between the information-rich field and successively more abstract spatial aggregates. It uses a data structure, the neighborhood graph, as a common interface to modularize computations. To illustrate our theory, we describe the computational structure of three implemented problem solvers -- KAM, MAPS, and HIPAIR --- in terms of the spatial aggregation generic operators by mixing and matching a library of commonly used routines.

We present mPlatform, a new reconfigurable modular sensornet platform that enables real-time processing on multiple heterogeneous processors. At the heart of the mPlatform is a scalable high-performance communication bus connecting the different modules of a node, allowing time-critical data to be shared without delay and supporting reconfigurability at the hardware level. Furthermore, the bus allows components of an application to span across different processors/modules without incurring much overhead, thus easing the program development and supporting software reconfigurability. We describe the communication architecture, protocol, and hardware configuration, and the implementation in a low power, high speed complex programmable logic device (CPLD). An asynchronous interface decouples the local processor of each module from the bus, allowing the bus to operate at the maximum desired speed while letting the processors focus on their real time tasks such as data collection and processing. Extensive experiments on the mPlatform prototype have validated the scalability of the communication architecture, and the high speed, reconfigurable inter-module communication that is achieved at the expense of a small increase in the power consumption. Finally, we demonstrate a real-time sound source localization application on the mPlatform, with four channels of acoustic data acquisition, FFT, and sound classification, that otherwise would be infeasible using traditional buses such as I2C.

We study the problem of concurrently supporting multiple radios with different capabilities and interfaces on a single sensor node platform. Through a detailed experimental study on hardware multi-radio platforms, using the two representative radio technologies 802.15.4 and 802.11, we identify bottlenecks and design tradeoffs that are usually overlooked and that, as we show, have a significant impact on the sensor network's performance and energy efficiency. Our findings are threefold. We show that a proper pairing of processor and radio is crucial for taking the full advantage of the energy efficiency of higher bandwidth radios. The processor/radio pairing affects the energy balance of a sensor node, thus making the design of dynamic switching among multiple radios more challenging. Second, we demonstrate and quantify the impact of network traffic on energy consumption of a sensor node while varying network parameters, and illustrate the deficiency of existing energy-optimizing protocols. Our results indicate that by properly adjusting network parameters, such as packet size and transmission period, energy savings of up to 50% can be achieved under heavy network traffic conditions when a CSMA-based MAC is used. We conclude by presenting a set of guidelines for designing and implementing energy efficient multi-radio platforms.

The Internet of things (IoT) has been considered as one of the promising paradigms that can allow people and objects to seamlessly interact. So far, numerous applications and services have been proposed, such as retrieval service. The retrieval, however, faces a big challenge in IoT because the data belongs to different domains and user interaction with the surrounding environment is constrained. This paper proposes Acrost, a retrieval system based on topic discovery and semantic awareness in IoT environment. The initial contents with interesting information is obtained through the combination of two topic centric collectors. The metadata is extracted by aggregating regular expression-based and conditional random fields-based approaches. Moreover, the semantic-aware retrieval is achieved by parsing the query and ranking the relevance of contents. In addition, we present a case study on academic conference retrieval to validate the proposed approaches. Experimental results show that the proposed system can significantly improve the response time and efficiency of topic self-adaptive retrieval manner.

This paper proposed an initiative optimization operation strategy and multi-objective energy management method for combined cooling heating and power U+0028 CCHP U+0029 with storage systems. Initially, the initiative optimization operation strategy of CCHP system in the cooling season, the heating season and the transition season was formulated. The energy management of CCHP system was optimized by the multi-objective optimization model with maximum daily energy efficiency, minimum daily carbon emissions and minimum daily operation cost based on the proposed initiative optimization operation strategy. Furthermore, the pareto optimal solution set was solved by using the niche particle swarm multi-objective optimization algorithm. Ultimately, the most satisfactory energy management scheme was obtained by using the technique for order preference by similarity to ideal solution U+0028 TOPSIS U+0029 method. A case study of CCHP system used in a hospital in the north of China validated the effectiveness of this method. The results showed that the satisfactory energy management scheme of CCHP system was obtained based on this initiative optimization operation strategy and multi-objective energy management method. The CCHP system has achieved better energy efficiency, environmental protection and economic benefits.

Fine operating rules for security control and an automatic system for their online discovery were developed to adapt to the development of smart grids. The automatic system uses the real-time system state to determine critical flowgates, and then a continuation power flow-based security analysis is used to compute the initial transfer capability of critical flowgates. Next, the system applies the Monte Carlo simulations to expected short-term operating condition changes, feature selection, and a linear least squares fitting of the fine operating rules. The proposed system was validated both on an academic test system and on a provincial power system in China. The results indicated that the derived rules provide accuracy and good interpretability and are suitable for real-time power system security control. The use of high-performance computing systems enables these fine operating rules to be refreshed online every 15 min.

This paper presents a computational method for automatically analyzing qualitative behaviors of complex dynamical systems in phase space. To demonstrate this method, a program called MAPS has been constructed that understands qualitatively distinct features of a phase space and represents geometric information about these features in a dimension-independent description, using deep domain knowledge of dynamical systems theory. Given a dynamical system specified as a system of governing equations, MAPS incrementally extracts the qualitative information about the system in terms of a qualitative phase-space structure describing steady-state behaviors, stabilities, and transient properties. MAPS generates a high-level symbolic description of the system sensible to human beings and manipulable by other programs, through a combination of numerical, combinatorial, and geometric computations and spatial reasoning techniques. MAPS has successfully demonstrated its power in a difficult engineering domain of nonlinear control design.

Networked embedded systems are composed of a large number of components that interact with the physical world via a set of sensors and actuators, have their own computational capabilities, and communicate with each other via a wired or wireless network. Such systems are best modeled by distributed hybrid systems that capture the interaction between the physical and computational components. Monitoring and diagnosis of any dynamical system depend crucially on the ability to estimate the system state given the observations. Estimation for distributed hybrid systems is particularly challenging because it requires keeping track of multiple models and the transitions between them. This paper presents a particle filtering based estimation algorithm for a class of distributed hybrid systems. The hybrid estimation methodology is demonstrated on a cryogenic propulsion system.

Although multicore smartphones have become increasingly mainstream, it is unclear whether and how smartphone applications can utilize multicore CPUs to improve performance. In this paper we study the performance of mobile applications using multicore CPUs, in terms of power and computation cost. Using Web browsing as an example, our preliminary measurement results show that even large applications like Web browsers with multi-threading acceleration cannot fully utilize the multicore CPUs. Furthermore, we find that the existing CPU power models on smartphones are ill-suited for modern multicore CPUs. We develop a new CPU power model with a high accuracy, 95.6% on average. Our work helps to better understand the performance of multicore smartphones and paves the way towards better CPU power management on multicore smartphones.

The proliferation of location-based services and applications calls for provisioning of location service as a first class system component that can return accurate location fix in short response time and is energy efficient. In this paper, we present the design, implementation and evaluation of WheelLoc - a continuous system location service for outdoor scenarios. Unlike previous localization efforts that try to directly obtain a point location fix, WheelLoc adopts an indirect approach: it seeks to capture a user mobility trace first and to obtain any point location by time- and speed-aware interpolation or extrapolation. WheelLoc avoids energy-expensive sensors completely and relies solely on commonly available cheap sensors such as accelerometer and magnetometer. With a set of novel techniques and the leverage of publicly available road maps and cell tower information, WheelLoc is able to meet those requirements of a first class component. Experimental results confirmed the effectiveness of WheelLoc. It can return a location estimate within 40ms with an accuracy about 40 meters, consumes only 240mW energy, and effectively strikes a better energy-accuracy tradeoff than GPS duty-cycling.

Taking advantage of widely deployed access points (AP), WiFi fingerprint based localization is of importance in indoor internet-of-things (IOT) environments.Nevertheless, spatio-temporal variation is one of its intractable problems, indicating severely environmental dynamics and uncertainty of decision.In this case, the localization accuracy drops significantly.In this paper, we attempt to overcome effects of spatio-temporal variations from two aspects: filtering of the training data and selection of partially valuable APs for matching in test phase.The key idea is to match partial unaffected measurements with 'clean' unaffected fingerprints.Bayesian framework and category model are presented for WiFi fingerprints.Two binary hidden variables with different dimensions are introduced to identify singular fingerprints and affected measurements respectively by employing expectation-maximization (EM) algorithms.EM based filter and simultaneous AP selection and localization are then proposed to obtain an optimal matching.Experimental results show that our proposed scheme greatly improves the localization accuracy in severely dynamic indoor environments.

To solve the issues of low performance and low reliability of autonomous steering systems in intelligent vehicles, this paper proposes a novel fault detection and fault tolerance control (FDFTC) strategy. Firstly, an autonomous steering controller with rack displacement as the state feedback input is utilized to improve the system performance. Secondly, considering the importance of the rack displacement signal to the controller and the harsh operating environment of the displacement sensor, the software redundancy of the sensor is necessary. Therefore, a FDFTC strategy composed of Bidirectional Long Short-Term Memory (Bi-LSTM) based rack displacement estimator and Sequential Probability Ratio Test (SPRT) is designed to effectively handle displacement sensor faults. Finally, comprehensive simulation and Hardware-in-Loop (HiL) test results show that the proposed FDFTC strategy can promptly detect sensor failures and efficiently restore vehicles to a stable state, thus effectively maintaining the trajectory tracking of intelligent vehicles when faults occur.

Conventional simulation and control methods for sliding-mode control systems are limited by the available sampling bandwidth and allowable tracking error. Consequently, these methods suffer from harmful chattering. This paper presents an adaptive method for the discrete-time simulation and control of sliding-mode control systems, based on an analysis of the relationship between tracking error and sampling rate for these systems. Our analysis shows that the tracking error decreases as the sampling time interval decreases when the sliding condition exists. The adaptive method exploits the concept of discrete-time sliding mode; the method adjusts its sampling rate to ensure that the tracking error is bounded within a boundary layer of the sliding surface. To simulate a sliding-mode system in discrete time, we present an adaptive integration scheme that follows the ideal system within a given tolerance. Likewise, the adaptive method can be used to generate discrete control signals for sliding-mode systems. Simulation results on examples have shown that the adaptive method is free of chattering.

Abstract : Target tracking is an important application for wireless ad hoc sensor networks. Because of the energy and communication constraints imposed by the size of the sensors, the processing has to be distributed over the sensor nodes. This paper discusses issues associated with distributed multiple target tracking for ad hoc sensor networks and examines the applicability of tracking algorithms developed for traditional networks of large sensors. when data association is not an issue, the standard pre- predict/update structure in single target tracking can be used to assign individual tracks to the sensor nodes based on their locations. Track ownership will have to be carefully migrated, using for example information driven sensor tasking, to minimize the need for communication when targets move. when data association is needed in tracking multiple interacting targets, clusters of tracks should be assigned to groups of collaborating nodes. Some recent examples of this type of distributed processing are given. Keywords: Wireless ad hoc sensor networks, multiple target tracking, distributed tracking

Geo-centric web interface such as MSN Virtual Earth (http://local.live.com) and Google Maps (http://maps.google.com) are useful to visualize spatially and geographically related data such as addresses, neighborhoods, weather, traffic, and so on. Desires to augment additional useful information to these interfaces have led people to create custom applications that overlay their own data on top of browsable maps. Examples of such applications overlay housing information (http://www.housingmaps.com) and crime-rate data (http://www.chicagocrime.org/map/) on Google maps, locations of vehicles (http://jprestonsystems.2mydns.com/ vemap.aspx?name=demoacct2) and podcasters (http://www.podlook.com/map.aspx) on MSN Virtual Earth, weather data on custom maps (http://www.wunderground.com), etc. Such applications have been possible after Google Maps and MSN Virtual Earth have published useful APIs to overlay location data on their maps. We envision publishing and querying real-time data (e.g., from sensors) over such geo-centric web interfaces. Existing solutions, although useful to write simple applications as above, have several drawbacks in achieving this vision. First, publishing even a single stream of data as a useful service is a nontrivial task. Many useful data is not being published yet because the data owners do not have enough programming expertise, or publishing it requires too much effort. Second, all the existing applications are mutually incompatible. One can’t bring up a single map that shows both the housing information and crimerates in an area. Third, existing solutions do not provide useful primitives such as querying live sensors based on keywords or location and aggregating the results in useful ways. The SenseWeb project at Microsoft Research Figure 1: SenseWeb Architecture.

A sensor network data gathering and visualization infrastructure is demonstrated, comprising of global sensor networks (GSN) middleware and Microsoft SensorMap. Users are invited to actively participate in the process of monitoring real-world deployments and can inspect measured data in the form of contour plots overlayed onto a high resolution map and a digital topographic model. Users can go back in time virtually to search for interesting events or simply to visualize the temporal dependencies of the data. The system presented is not only interesting and visually enticing for non-expert users but brings substantial benefits to environmental scientists. The easily installed data acquisition component as well as the powerful data sharing and visualization platform opens up new ground in collaborative data gathering and interpretation in the spirit of Web 2.0 applications.

Underpinning many recent advances in sensing applications (e.g., mHealth) is the ability to safely collect and share mobile sensor data. Research has shown that even from seemingly harmless sensors (e.g., accelerometers, gyroscopes, or magnetometers) an ever expanding set of potentially sensitive user behavior can be inferred. Providing robust anonymity assurances is a principal mechanism for protecting users when data is shared (e.g., with medical professionals or friends). In this paper, we study the feasibility of user de-anonymization from mobile sensor datasets routinely collected on commodity devices (e.g., smartphones). We perform a systematic investigation to quantify the threat of de-anonymization using existing sparsity-based techniques adapted to exploit mobile sensor data characteristics. This preliminary study indicates significant threats to user anonymity exist within shared mobile sensor data and further investigation is warranted.

Given the emerging concerns over app privacy-related risks, major app distribution providers (e.g., Microsoft) have been exploring approaches to help end users to make informed decision before installation. This is different from existing approaches of simply trusting users to make the right decision. We build on the direction of risk rating as the way to communicate app-specific privacy risks to end users. To this end, we propose to use sensitivity analysis to infer whether an app requests sensitive on-device resources/ data that are not required for its expected functionality. Our system, Privet, addresses challenges in efficiently achieving test coverage and automated privacy risk assessment. Finally, we evaluate Privet with 1,000 Android apps released in the wild.

Embedded systems are composed of a large number of components that interact with the physical world via a set of sensors and actuators, have their own computational capabilities, and communicate with each other via a wired or wireless network. Diagnostic systems for such applications must address new challenges caused by the distribution of resources, the networking environment, and the tight coupling between the computational and the physical worlds. Our approach is to move from centralized, discrete or continuous techniques toward a distributed, hybrid diagnosis architecture. Monitoring and diagnosis of any dynamical system depend crucially on the ability to estimate the system state given the observations. Estimation for hybrid systems is particularly challenging because it requires keeping track of multiple models and the transitions between them. This paper presents a particle filtering based estimation algorithm that addresses the challenge of the interaction between continuous and discrete dynamics in hybrid systems. The hybrid estimation methodology has been demonstrated on a rocket propulsion system.

Buildings consume a significant portion of the total delivered energy. While the community has been working on monitoring the building energy usage, we argue that an accurate accounting of individual occupants' energy expenditure in real-time is still the missing piece. And, this missing piece makes incentivizing energy reduction a challenge. We take a systematic approach and identify the lack of real-time association between human actions and observed energy consumption as one roadblock. To this end, we first present a model that enables real-time accounting of appliances with delay characteristics. Building on top of this model, we introduce a complete system that fairly attributes energy usages of shared resources, and reveals per-person energy footprint information. And, our system has several ways to analyze and visualize individuals' energy footprint. Finally, we present the deployment experience at an office building.

Many indoor sensing applications leverage knowledge of relative proximity among physical objects and humans, such as the notion of "within arm's reach". In this paper, we quantify this notion using "proximity zone", and propose a methodology that empirically and systematically compare the proximity zones created by various wireless technologies. We find that existing technologies such as 802.15.4, Bluetooth Low Energy (BLE), and RFID fall short on metrics such as boundary sharpness, robustness against interference, and obstacle penetration. We then present the design and evaluation of a wireless proximity detection platform based on magnetic induction - LiveSynergy. LiveSynergy provides sweet spot for indoor applications that require reliable and precise proximity detection. Finally, we present the design and evaluation of an end-to-end system, deployed inside a large food court to offer context-aware and personalized advertisements and diet suggestions at a per-counter granularity.

Water pollution and the global spread of pathogens pose unexpected and considerable challenges to human and environmental health. Recent developments in DNA-based engineering systems show various real-world applications in ecosystem restoration, medical diagnostics, immune regulation, and drug delivery, providing new opportunities to address these challenges. DNA materials are essentially different from non-biological materials and act as a robust molecular building block with exceptional attributes; these attributes include its genetic function, biocompatibility, nanoscale controllability, programmability, and molecular recognition capability. Unlike traditional systems using separate strategies, the unique properties of DNA molecules enable their use as universal substrates to develop diverse engineering systems and offer a flexible solution concept to current challenges. Here, we outline principles and approaches for the use of three DNA-based engineering subsystems—DNA tracers, DNA barcodes, and DNA hydrogels—in improving human and environmental health in the context of pollution identification, pathogen detection, and medical treatment, with some discussion of typical examples to highlight the focus of current research and envision potential development direction. We believe that this review will contribute to the rational design of DNA-based tracer, barcode and hydrogel system, and the successful integration of these subsystems that are intrinsically interconnected will further demonstrate their synergistic effects on environmental remediation and medical care.

Working of in-wheel motors (IWMs) in high-efficiency areas and minimum tire slip should be considered when driving distributed drive electric vehicles (DDEVs). Therefore, a novel torque vectoring control algorithm is proposed to lower energy dissipation and ensure lateral stability, which consists of a linear quadratic regulator and a proportion integration control module in upper controller to calculate desired additional yaw moment and total driving torque, respectively, for following desired yaw rate, side slip angle, and longitudinal velocity. In addition, the stability objective function considering tire working load and the economic objective function considering working efficiency of IWMs and tire slip energy are established separately in lower controller. The fitness function of coordinating lateral stability and economy is obtained by phase plane method. Particle swarm optimization (PSO) algorithm with a superior initial population (SIP-PSO) is proposed to solve torque distribution coefficients for torque distribution of DDEVs. Finally, simulation and hardware-in-the-loop test results under double lane change and snake lane change maneuvers on lower adhesion road indicate that the proposed algorithm can effectively lower the energy loss of IWM working and tire slip while ensuring lateral stability under different working conditions.

In order to improve the safety of autonomous ground vehicle (AGV) and the accuracy of trajectory tracking, a fault detection (FD) scheme of steering system and a fault-tolerant control (FTC) method are proposed. First, this paper analyzes the causes of steering motor failure and designs a passive fault-tolerant controller to ensure that the torque motor under coordinated control can still follow the angle when a slight fault occurs in the angle motor. Second, the corresponding FD schemes are designed for different motor faults, and the unknown input observer (UIO) of the steering resistance moment is designed considering the uncertainty of the steering system caused by the fault motor. The active fault-tolerant controller composed of backstepping control and global sliding mode control (SMC) is further combined to control the fault-free motor. Finally, simulation and HiL test results show the effectiveness of the proposed FD scheme and FTC strategy.

In a sensor network, data routing is tightly coupled to the needs of a sensing task, and hence the application semantics. This paper introduces the novel idea of information-directed routing, in which routing is formulated as a joint optimization of data transport and information aggregation. The routing objective is to minimize communication cost while maximizing information gain, differing from routing considerations for more general ad hoc networks. The paper uses the concrete problem of locating and tracking possibly moving signal sources as an example of information generation processes, and considers two common information extraction patterns in a sensor network: routing a user query from an arbitrary entry node to the vicinity of signal sources and back, or to a prespecified exit node, maximizing information accumulated along the path. We derive information constraints from realistic signal models, and present several routing algorithms that find near-optimal solutions for the joint optimization problem. Simulation results have demonstrated that information-directed routing is a significant improvement over a previously reported greedy algorithm, as measured by sensing quality such as localization and tracking accuracy and communication quality such as success rate in routing around sensor holes.

No abstract available.

A powerful concept to cope with resource limitations and information redundancy in wireless sensor networks is the use of collaboration groups to distill information within the network and suppress unnecessary activities. When the phenomena to be monitored have large geographical extents, it is not obvious how to define these collaboration groups. This article presents the application of geometric duality to form such groups for sensor selection and non-local phenomena tracking. Using a dual-space transformation, which maps a non-local phenomenon (e.g., the edge of a half-plane shadow) to a single point in the dual space and maps locations of distributed sensor nodes to a set of lines that partitions the dual space, one can turn off the majority of the sensors to achieve resource preservation without losing detection and tracking accuracy. Since the group so defined may consist of nodes that are far away in physical space, we propose a hierarchical architecture that uses a small number of computationally powerful nodes and a massive number of power constrained motes. By taking advantage of the continuity of physical phenomena and the duality principle, we can greatly reduce the power consumption in non-local phenomena tracking and extend the lifetime of the network.

We present SenseWeb, an open and scalable infrastructure for sharing and geocentric exploration of sensor data streams. SenseWeb allows sensor owners to share data streams across multiple applications and users, thus amortizing sensor deployment costs effectively. It also provides mechanisms to transparently index and cache data, to process spatio-temporal queries on real-time and historic data, and to aggregate and present results on a geocentric web interface. In this paper, we present the architecture of SenseWeb, its techniques to enable global sharing of heterogeneous sensors, and its mapbased front-end for spatio-temporal data exploration. We enable interactive geocentric data exploration in the mapbased front-end using techniques for rapidly changing map overlaid visualizations of numerous data streams. We also demonstrate flexibility and scalability of the architecture by evaluating a deployed prototype of SenseWeb, which has been publicly available since March 2008.

Sensing is a significant contributor to the current mobile computing revolution. Today’s typical smartphone has more than eight sensors, including multiple mics, cameras, accelerometers, gyroscopes, a GPS, a digital compass, and proximity sensors. These sensors not only provide natural user interaction with the device, but also offer tantalizing opportunities for context-aware computing. A rich history of work has investigated algorithms for converting raw sensor data into context, and their specific usages [3]. To cite just two examples: A restaurant finder app may adjust its search radius depending on whether a user is on foot, cycling, or driving, which can be inferred from GPS and IMU readings [11]. A Twitter app might choose to alert the user of her latest updates at an interruptible moment such as when she is not engaged in conversation, which can be inferred from the mic’s audio [5]. The ingredients for context appear ready: the sensing hardware, the data processing algorithms and the application scenarios are all primed. The question that emerges is: who is responsible for context generation? One option is for apps to manage their own context generation. This approach appears appealing because apps are most familiar with their own context needs. However, many mobile OSs such as iPhone’s iOS and Windows Phone’s WP7 harbor legitimate energy concerns and severely restrict non-foreground processing. As a result, an app may generate context from immediately available sensor data, but is unable to maintain context while outside the scope of its execution. This can be as simple as missing the accelerometer’s transition from sitting to standing, since sensing either state outside the transition period does not yield distinguishing information. Alternatively, Android apps may run in the background, but then the user is at the mercy of the flawless app developer to use resources intelligently. Another option is to simply ship all sensor data to the cloud

Advances in mobile and wearable devices are making it feasible to deploy sensing systems at a large-scale. However, slower progress is being made in activity recognition which remains often unreliable in everyday environments. In this paper, we investigate how to leverage the increasing capacity to gather data at a population-scale towards improving existing models of human behavior. Specifically, we consider the various social phenomena and environmental factors that cause people to develop correlated behavioral patterns, especially within communities connected by strong social ties. Reasons underpinning correlated behavior include shared externalities (e.g., work schedules, weather, traffic conditions), that shape options and decisions; and cases of adopted behavior, as people learn from each other or assume group norms due to social pressure. Most existing approaches to modeling human behavior ignore all of these phenomena and recognize activities solely on the basis of sensor data captured from a single individual. We propose the Networked Community Behavior (NCB) framework for activity recognition, specifically designed to exploit community-scale behavioral patterns. Under NCB, patterns of community behavior are mined to identify social ties that can signal correlated behavior, this information is used to augment sensor-based inferences available from the actions of individuals. Our evaluation of NCB shows it is able to outperform existing approaches to behavior modeling across four mobile sensing datasets that collectively require a diverse set of activities to be recognized.

Carnitine-acylcarnitine translocase deficiency (CACTD) is a rare and life-threatening autosomal recessive disorder of fatty acid β-oxidation (FAO). Most patients with CACTD develop severe metabolic decompensation which deteriorates progressively and rapidly, causing death in infancy or childhood. As CACTD in some patients is asymptomatic or only with some nonspecific symptoms, the diagnosis is easy to be ignored, resulting in sudden death, which often triggers medical disputes. Herein, we report a case of neonatal sudden death with CACTD. The neonate showed a series of severe metabolic crisis, deteriorated rapidly and eventually died 3 days after delivery. Tandem mass spectrometry (MS-MS) screening of dry blood spots before death showed that the level of long-chain acylcarnitines, especially C12-C18 acylcarnitine, was increased significantly, and therefore a diagnosis of inherited metabolic disease (IMD) was suspected. Autopsy and histopathological results demonstrated that there were diffuse vacuoles in the heart and liver of the deceased. Mutation analysis revealed that the patient was a compound heterozygote with c.199-10 T > G and a novel c.1A > T mutation in the SLC25A20 gene. Pathological changes such as heart failure, arrhythmia and cardiac arrest related to mitochondrial FAO disorders are the direct cause of death, while gene mutation is the underlying cause of death.

We present a framework called Semantic Streams that allows users to pose declarative queries over semantic interpretations of sensor data. For example, instead of querying raw sensor data, the user can query vehicle speeds; the system decides which sensor data and which operations to use to infer the vehicle speeds. The user can also place constraints on values such as the confidence with which the speed was measured or the amount of energy consumed to measure the speeds. This framework is designed to work in a shared sensor infrastructure, where multiple queries may coexist for extended periods of time, instead of a hand-designed, single purpose sensor network. We propose a semantic service programming model and describe a service description language and a query processor that support the programming model. We demonstrate how this system can be used with a network of video, magnetometer, and infrared break beam sensors deployed in a parking garage.

Summary form only given. Due to the rapid convergence of MEMS devices, ubiquitous connectivity, and low-power embedded processing, wireless sensor networks are emerging as an entirely new computing platform that promises to seamlessly couple the digital world with the physical world around us. The envisioned applications are societal-scale, ranging from transportation, manufacturing, healthcare, to environmental preservation. To cope with the enormous challenges of designing and maintaining such a massively distributed information fabric, we must address systemic issues such as networking, infrastructure establishment, collaborative signal processing, device tasking and control, data management, as well as programming tools. I described the recent progress in wireless sensor networks and applications, drawing examples from our own work as well as that of others. I started with the technical challenges posed by the severe resource constraints on power and bandwidth, the fragile and dynamic wireless connectivity, as well as the distributed and concurrent nature of the application programs, to illustrate the point that these systems must be designed with a holistic, cross-layer approach. As a concrete example, I described how distributed agents collaboratively seek, process, and aggregate information in a resource-constrained environment. These agents employ a decentralized decision-making, knows as the IDSQ protocol, to optimize for information utility while keeping the cost low. I then present recent work in sensor data management. Using in-network intelligence, I showed the dramatic improvements in the reduction of information needed to answer high-level queries. Next, I described significant progress the research community has made in developing programming abstractions and tools for the development of sensor network applications. In one approach, nodes are programmed as collectives, maintaining and migrating information states as required by the application. The goal is to significantly lessen the burden of managing lower-level network events on application developers. I concluded with an outlook for the future directions and problems that we must tackle in order to make societal-scale sensor network applications a reality.

No abstract available.

We present a spreadsheet approach to simplifying the process of managing, programming, and interacting with sensor networks and visualizing, archiving and retrieving sensor data. An Excel spreadsheet prototype has been built to demonstrate the idea. This environment provides Excel users, who are already familiar with spreadsheet applications, a convenient and powerful tool for programming and data analysis. We discuss the architecture of this prototype and our experience in implementing the tool. We show two different classes of sensor-net applications built using this platform. We also present performance data on the scalability of the tool with respect to data rate and number of data streams.

SensorMap is a portal web site for real-time real-world sensor data. SensorMap allows data owners to easily make their data available on the map. The platform also transparently provides mechanisms to archive and index data, to process queries, to aggregate and present results on a geocentric web interface based on Windows Live Local. In this position paper, we describe the architecture of SensorMap, key challenges in building such a portal, and current status and experience.

There are many scenarios where interoperability is required for sensor devices. We demonstrate one approach to achieve interoperability: using web services. Hosting a Web service challenges the battery- life, bandwidth, and processing power constraints of low power sensor nodes. We demonstrate a lightweight implementation on MSP430 based sensor nodes with 802.15.4 radios. The implementation allows standards compliant web service clients to use the sensors but minimizes code size and energy at the sensor nodes. It allows sensor nodes to enter sleep modes. We prototype an example application for a home sensor network along with two types of sensor nodes required for it. We also show how our system enables sensor nodes to be used easily from applications written in high level languages using existing development tools.

Networked sensing promises to drastically change the way people interact with their environments by providing rich contextual information in real time. Major challenges remain on how concurrent users program and control such environments at the application level. This paper summarizes our research efforts in automatically composing semantics services to fulfill declarative user queries in resource efficient ways. We also describe an example software platform, MSR Sense, which supports service abstraction, composition, and execution.

The IT industry is the one of the fastest growing sectors of the U.S. economy in terms of its energy consumption. According to a 2007 EPA report, U.S. data centers alone consumed 61 billion kWh in 2006 — enough energy to power 5.8 million average households. Even under conservative estimates, IT energy consumption is projected to double by 2011. Reducing data center energy consumption is a pressing issue for the entire IT industry now and into the future. In this article, we argue that dense and real-time environmental monitoring systems are needed to improve the energy efficiency of IT facilities.

A computational environment has been developed to aid control system design for a particular class of nonlinear applications. The analysis and design tools constituting this environment are based on knowledge about phase-space dynamics of nonlinear and chaotic systems. Two implemented, complementary programs that exploit the special properties of such systems to synthesize powerful control systems automatically are described. Fast computers and powerful computational techniques that combine symbolic/numeric and algebraic/geometric computing with new reasoning mechanisms from artificial intelligence make this paradigm feasible, in spite of its inherent computational demands.< <ETX xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">&gt;</ETX>

Abstract : Networked embedded systems are composed of a large number of physically distributed nodes that interact with the physical world via a set of sensors and actuators, have their own computational capabilities, and communicate with each other via a wired or wireless network. Monitoring and diagnosis for such systems must address several challenges caused by the distribution of resources, communication limitations, and node and link failures. This paper presents a distributed diagnosis framework that exploits the topology of a physical system to be diagnosed to limit inter-diagnoser communication and compute diagnoses in an anytime and any information manner, making it robust to communication and processor failures. The framework adopts the consistency-based diagnosis formalism and develops a distributed constraint satisfaction realization of the diagnosis algorithm. Each local diagnoser first computes locally consistent diagnoses, taking into account local sensing information only. The local diagnosis sets are reduced to globally consistent diagnoses through pairwise communications between local diagnosers. The algorithm has been successfully demonstrated for the diagnosis of paper path faults for the Xeros DC265 printer.

Regularities exist in datasets describing spatially distributed physical phenomena. Human experts often understand and verbalize the regularities as abstract spatial objects evolving coherently and interacting with each other in the domain space. We describe a novel computational approach for identifying and extracting these abstract spatial objects through the construction of a hierarchy of spatial relations. We demonstrate the approach with an application to finding pressure trough features in weather data sets.

We develop a qualitative method for understanding and representing phase space structures of complex systems. To demonstrate this method, a program called MAPS has been constructed that understands qualitatively different regions of a phase space and represents and extracts geometric shape information about these regions, using deep domain knowledge of dynamical system theory. Given a dynamical system specified as a system of governing equations, MAPS applies a successive sequence of operations to incrementally extract the qualitative information and generates a complete, high level symbolic description of the phase space structure, through a combination of numerical, combinatorial, and geometric computations and spatial reasoning techniques. The high level description is sensible to human beings and manipulable by other programs. We are currently applying the method to a difficult engineering design domain in which controllers for complex systems are to be automatically synthesized to achieve desired properties, based on the knowledge of the phase space shapes of the systems.