Showing papers in "ACM Transactions on Sensor Networks in 2014"
TL;DR: FIND is proposed, a novel method to detect nodes with data faults that neither assumes a particular sensing model nor requires costly event injections and shows that average ranking difference is a provable indicator of possible data faults.
Abstract: Wireless Sensor Networks (WSN) promise researchers a powerful instrument for observing sizable phenomena with fine granularity over long periods. Since the accuracy of data is important to the whole system's performance, detecting nodes with faulty readings is an essential issue in network management. As a complementary solution to detecting nodes with functional faults, this article, proposes FIND, a novel method to detect nodes with data faults that neither assumes a particular sensing model nor requires costly event injections. After the nodes in a network detect a natural event, FIND ranks the nodes based on their sensing readings as well as their physical distances from the event. FIND works for systems where the measured signal attenuates with distance. A node is considered faulty if there is a significant mismatch between the sensor data rank and the distance rank. Theoretically, we show that average ranking difference is a provable indicator of possible data faults. FIND is extensively evaluated in simulations and two test bed experiments with up to 25 MicaZ nodes. Evaluation shows that FIND has a less than 5p miss detection rate and false alarm rate in most noisy environments.
155 citations
TL;DR: This article shows how real time occupancy data from a wireless sensor network can be used to create occupancy models, which in turn can be integrated into building conditioning system for usage-based demand control conditioning strategies.
Abstract: Heating, cooling and ventilation accounts for 35p energy usage in the United States. Currently, most modern buildings still condition rooms assuming maximum occupancy rather than actual usage. As a result, rooms are often over-conditioned needlessly. Thus, in order to achieve efficient conditioning, we require knowledge of occupancy. This article shows how real time occupancy data from a wireless sensor network can be used to create occupancy models, which in turn can be integrated into building conditioning system for usage-based demand control conditioning strategies. Using strategies based on sensor network occupancy model predictions, we show that it is possible to achieve 42p annual energy savings while still maintaining American Society of Heating, Refrigerating and Air-Conditioning Engineers (ASHRAE) comfort standards.
127 citations
TL;DR: This article proposes 4C, a novel link estimator that applies link quality prediction along with link estimation, and presents three prediction models that utilize different machine learning methods, namely, naive Bayes classifier, logistic regression, and artificial neural networks.
Abstract: As an integral part of reliable communication in wireless networks, effective link estimation is essential for routing protocols. However, due to the dynamic nature of wireless channels, accurate link quality estimation remains a challenging task. In this article, we propose 4C, a novel link estimator that applies link quality prediction along with link estimation. Our approach is data driven and consists of three steps: data collection, offline modeling, and online prediction. The data collection step involves gathering link quality data, and based on our analysis of the data, we propose a set of guidelines for the amount of data to be collected in our experimental scenarios. The modeling step includes offline prediction model training and selection. We present three prediction models that utilize different machine learning methods, namely, naive Bayes classifier, logistic regression, and artificial neural networks. Our models take a combination of PRR and the physical-layer information, that is, Received Signal Strength Indicator (RSSI), Signal-to-Noise Ratio (SNR), and Link Quality Indicator (LQI) as input, and output the success probability of delivering the next packet. From our analysis and experiments, we find that logistic regression works well among the three models with small computational cost. Finally, the third step involves the implementation of 4C, a receiver-initiated online link quality prediction module that computes the short temporal link quality. We conducted extensive experiments in the Motelab and our local indoor testbeds, as well as an outdoor deployment. Our results with single- and multiple-senders experiments show that with 4C, CTP improves the average cost of delivering a packet by 20p to 30p. In some cases, the improvement is larger than 45p.
108 citations
TL;DR: This article introduces ORW, a practical opportunistic routing scheme for wireless sensor networks that uses a novel opportunist routing metric, EDC, that reflects the expected number of duty-cycled wakeups that are required to successfully deliver a packet from source to destination.
Abstract: Opportunistic routing is widely known to have substantially better performance than unicast routing in wireless networks with lossy links. However, wireless sensor networks are usually duty cycled, that is, they frequently enter sleep states to ensure long network lifetime. This renders existing opportunistic routing schemes impractical, as they assume that nodes are always awake and can overhear other transmissions. In this article we introduce ORW, a practical opportunistic routing scheme for wireless sensor networks. ORW uses a novel opportunistic routing metric, EDC, that reflects the expected number of duty-cycled wakeups that are required to successfully deliver a packet from source to destination. We devise distributed algorithms that find the EDC-optimal forwarding and demonstrate using analytical performance models and simulations that EDC-based opportunistic routing results in significantly reduced delay and improved energy efficiency compared to traditional unicast routing. In addition, we evaluate the performance of ORW in both simulations and testbed-based experiments. Our results show that ORW reduces radio duty cycles on average by 50p (up to 90p on individual nodes) and delays by 30p to 90p when compared to the state-of-the-art.
104 citations
TL;DR: This article carries out a performance evaluation of existing and new compression schemes, considering linear, autoregressive, FFT-/DCT- and wavelet-based models, by looking at their performance as a function of relevant signal statistics and results reveal that the DCT-based schemes are the best option in terms of compression efficiency but are inefficient in termsof energy consumption.
Abstract: Lossy temporal compression is key for energy-constrained wireless sensor networks (WSNs), where the imperfect reconstruction of the signal is often acceptable at the data collector, subject to some maximum error tolerance. In this article, we evaluate a number of selected lossy compression methods from the literature and extensively analyze their performance in terms of compression efficiency, computational complexity, and energy consumption. Specifically, we first carry out a performance evaluation of existing and new compression schemes, considering linear, autoregressive, FFT-/DCT- and wavelet-based models , by looking at their performance as a function of relevant signal statistics. Second, we obtain formulas through numerical fittings to gauge their overall energy consumption and signal representation accuracy. Third, we evaluate the benefits that lossy compression methods bring about in interference-limited multihop networks, where the channel access is a source of inefficiency due to collisions and transmission scheduling. Our results reveal that the DCT-based schemes are the best option in terms of compression efficiency but are inefficient in terms of energy consumption. Instead, linear methods lead to substantial savings in terms of energy expenditure by, at the same time, leading to satisfactory compression ratios, reduced network delay, and increased reliability performance.
101 citations
TL;DR: The state-of-the-art in building energy management systems is surveyed and a generic architecture is proposed after which a detailed taxonomy of existing documented systems is presented.
Abstract: Reducing energy consumption within buildings has been an active area of research in the past decade; more recently, there has been an increased influx of activity, motivated by a variety of issues including legislative, tax-related, as well as an increased awareness of energy-related issues. Energy usage both in commercial and residential buildings represents a significant portion of overall energy consumption; however, much of this may be categorized as waste, that is, energy usage that does not fulfil a definite purpose. In the past decade, the viability of Wireless Sensor Network (WSN) technologies has been demonstrated, leading to increased possibilities for novel services for building energy management. This development has resulted in numerous approaches being proposed for harnessing WSNs for energy management and conservation. This article surveys the state-of-the-art in building energy management systems. A generic architecture is proposed after which a detailed taxonomy of existing documented systems is presented. Gaps in the literature are highlighted and directions for future research identified.
89 citations
TL;DR: The first maximum likelihood solution to handle the cases where measurements from different participants may be conflicting is provided and is shown to outperform previous work used for corroborating observations, the state-of-the-art fact-finding baselines, as well as simple heuristics such as majority voting.
Abstract: This article addresses the challenge of truth discovery from noisy social sensing data. The work is motivated by the emergence of social sensing as a data collection paradigm of growing interest, where humans perform sensory data collection tasks. Unlike the case with well-calibrated and well-tested infrastructure sensors, humans are less reliable, and the likelihood that participants' measurements are correct is often unknown a priori. Given a set of human participants of unknown trustworthiness together with their sensory measurements, we pose the question of whether one can use this information alone to determine, in an analytically founded manner, the probability that a given measurement is true. In our previous conference paper, we offered the first maximum likelihood solution to the aforesaid truth discovery problem for corroborating observations only. In contrast, this article extends the conference paper and provides the first maximum likelihood solution to handle the cases where measurements from different participants may be conflicting. The article focuses on binary measurements. The approach is shown to outperform our previous work used for corroborating observations, the state-of-the-art fact-finding baselines, as well as simple heuristics such as majority voting.
87 citations
TL;DR: The methodology of sensor placement optimization for SHM is studied that addresses three key aspects: finding a high quality placement of a set of sensors that satisfies civil engineering requirements; ensuring the communication efficiency and low complexity for sensor placement; and reducing the probability of a network failure.
Abstract: Structural health monitoring (SHM) refers to the process of implementing a damage detection and characterization strategy for engineering structures. Its objective is to monitor the integrity of structures and detect and pinpoint the locations of possible damages. Although wired network systems still dominate in SHM applications, it is commonly believed that wireless sensor network (WSN) systems will be deployed for SHM in the near future, due to their intrinsic advantages. However, the constraints (e.g., communication, fault tolerance, energy) of WSNs must be considered before their deployment on structures. In this article, we study the methodology of sensor placement optimization for WSN-based SHM. Sensor placement plays a vital role in SHM applications, where sensor nodes are placed on critical locations that are of civil/structural engineering importance. We design a three-phase sensor placement approach, named TPSP, aiming to achieve the following objectives: finding a high-quality placement for a given set of sensors that satisfies the engineering requirements, ensuring communication efficiency and reliability and low placement complexity, and reducing the probability of failures in a WSN. Along with the sensor placement, we enable sensor nodes to develop “connectivity trees” in such a way that maintaining structural health state and network connectivity, for example, in case of a sensor fault, can be done in a distributed manner. The trees are constructed once (unlike dynamic clusters or trees) and do not incur additional communication costs for the WSN. We optimize the performance of TPSP by considering multiple objectives: low communication cost, fault tolerance, and lifetime prolongation. We validate the effectiveness and performance of TPSP through both simulations using real datasets and a proof-of-concept system on a physical structure.
79 citations
TL;DR: An approach to object tracking handover in a network of smart cameras, based on self-interested autonomous agents, which exchange responsibility for tracking objects in a market mechanism, in order to maximise their own utility.
Abstract: In this article we present an approach to object tracking handover in a network of smart cameras, based on self-interested autonomous agents, which exchange responsibility for tracking objects in a market mechanism, in order to maximise their own utility. A novel ant-colony inspired mechanism is used to learn the vision graph, that is, the camera neighbourhood relations, during runtime, which may then be used to optimise communication between cameras. The key benefits of our completely decentralised approach are on the one hand generating the vision graph online, enabling efficient deployment in unknown scenarios and camera network topologies, and on the other hand relying only on local information, increasing the robustness of the system. Since our market-based approach does not rely on a priori topology information, the need for any multicamera calibration can be avoided. We have evaluated our approach both in a simulation study and in network of real distributed smart cameras.
75 citations
TL;DR: This article shows that when restricted to shortest path trees, building maximum lifetime aggregation trees can be solved in polynomial time and presents a centralized algorithm and a distributed protocol for building such trees.
Abstract: In wireless sensor networks, the spanning tree is usually used as a routing structure to collect data. In some situations, nodes do in-network aggregation to reduce transmissions, save energy, and maximize network lifetime. Because of the restricted energy of sensor nodes, how to build an aggregation tree of maximum lifetime is an important issue. It has been proved to be NP-complete in previous works. As shortest path spanning trees intuitively have short delay, it is imperative to find an energy-efficient shortest path tree for time-critical applications. In this article, we first study the problem of building maximum lifetime shortest path aggregation trees in wireless sensor networks. We show that when restricted to shortest path trees, building maximum lifetime aggregation trees can be solved in polynomial time. We present a centralized algorithm and design a distributed protocol for building such trees. Simulation results show that our approaches greatly improve the lifetime of the network and are very effective compared to other solutions. We extend our discussion to networks without aggregation and present interesting results.
64 citations
TL;DR: This article argues that, in addition to the estimation of current link quality, prediction of the future link quality is more important for the routing protocol to establish low-cost delivery paths and proposes to apply machine learning methods to predict the link quality in the near future to facilitate the utilization of intermediate links with frequent quality changes.
Abstract: Link quality estimation is a fundamental component of the low-power wireless network protocols and is essential for routing protocols in Wireless Sensor Networks (WSNs). However, accurate link quality estimation remains a challenging task due to the notoriously dynamic and unpredictable wireless environment. In this article we argue that, in addition to the estimation of current link quality, prediction of the future link quality is more important for the routing protocol to establish low-cost delivery paths. We propose to apply machine learning methods to predict the link quality in the near future to facilitate the utilization of intermediate links with frequent quality changes. Moreover, we show that, by using online learning methods, our adaptive link estimator (TALENT) adapts to network dynamics better than statically trained models without the need of a priori data collection for training the model before deployment. We implemented TALENT in TinyOS with Low-Power Listening (LPL) and conducted extensive experiments in three testbeds. Our experimental results show that the addition of TALENT increases the delivery efficiency 1.95 times on average compared with a 4B, state-of-the-art link quality estimator, as well as improves the end-to-end delivery rate when tested on three different wireless testbeds.
TL;DR: This paper proposes a set of optimal patterns to achieve full coverage and global connectivity under two different antenna models, i.e., the sector model and the knob model, and introduces with detailed analysis several fundamental theorems and conjectures.
Abstract: In this article, we address a new unexplored problem: what are the optimal patterns to achieve connected coverage in wireless networks with directional antennas. As their name implies, directional antennas can focus their transmission energy in a certain direction. This feature leads to lower cross-interference and larger communication distance. It has been shown that, with proper scheduling mechanisms, directional antennas may substantially improve networking performance in wireless networks. In this article, we propose a set of deployment patterns to achieve full coverage and up to 2-connectivity under two different antenna models, namely the sector model and the knob model. These patterns are optimal under most combinations of communication and sensing ranges. We also introduce with detailed analysis several fundamental theorems and conjectures. Finally, we examine a more realistic physical model, where there might be strong interference and both the sensing range and the communication range might be irregular. The results show that our designed patterns work well even in unstable and fickle physical environments.
TL;DR: A lightweight sensing service that runs on the mobile phone and detects the indoor/outdoor environment in a fast, accurate, and efficient manner and greatly benefits many location-based and context-aware applications.
Abstract: The location and context switching, especially the indoor/outdoor switching, provides essential and primitive information for upper-layer mobile applications. In this article, we present IODetector: a lightweight sensing service that runs on the mobile phone and detects the indoor/outdoor environment in a fast, accurate, and efficient manner. Constrained by the energy budget, IODetector primarily leverages lightweight sensing resources, such as light sensors, magnetism sensors, and cell tower signals. For universal applicability, IODetector assumes no prior knowledge (e.g., fingerprints) of the environment and uses only on-board sensors common to mainstream mobile phones. Being a generic and lightweight service component, IODetector greatly benefits many location-based and context-aware applications. We prototype the IODetector on Android mobile phones and evaluate the system comprehensively with data collected from 34 traces that include 133 different places during a 6-week period, employing different phone models. We further perform a case study where we make use of IODetector to instantly infer the GPS availability and localization accuracy in different indoor/outdoor environments.
TL;DR: This article describes methods and tools to collect the data necessary as input for schedule calculation and introduces a heuristic, due to the high complexity of schedule calculation, which is discussed in this article.
Abstract: Wireless sensor networks for industrial process monitoring and control require highly reliable and timely data delivery. To match performance requirements, specialised schedule based medium access control (MAC) protocols are employed. In order to construct an efficient system, it is necessary to find a schedule that can support the given application requirements in terms of data delivery latency and reliability. Furthermore, additional requirements such as transmission power may have to be taken into account when constructing the schedule. In this article, we show how such schedule can be constructed. We describe methods and tools to collect the data necessary as input for schedule calculation. Moreover, due to the high complexity of schedule calculation, we also introduce a heuristic. We evaluate the proposed methods in a real-world process automation and control application deployed in an oil refinery and further present a long-term experiment in an office environment. Additionally, we discuss a framework for schedule life-cycle management.
TL;DR: In this paper, the authors proposed Sequence-Based Fault Detection (SBFD), a novel framework for network fault detection in WSNs, which exploits in-network packet tagging using the Fletcher checksum and server-side network path analysis to efficiently deduce the path of all packets sent to the sink.
Abstract: Wireless Sensor Network (WSN) technology has recently moved out of controlled laboratory settings to real-world deployments. Many of these deployments experience high rates of failure. Common types of failure include node failure, link failure, and node reboot. Due to the resource constraints of sensor nodes, existing techniques for fault detection in enterprise networks are not applicable. Previously proposed WSN fault detection algorithms either rely on periodic transmission of node status data or inferring node status based on passive information collection. The former approach significantly reduces network lifetime, while the latter achieves poor accuracy in dynamic or large networks. Herein, we propose Sequence-Based Fault Detection (SBFD), a novel framework for network fault detection in WSNs. The framework exploits in-network packet tagging using the Fletcher checksum and server-side network path analysis to efficiently deduce the path of all packets sent to the sink. The sink monitors the extracted packet paths to detect persistent path changes which are indicative of network failures. When a failure is suspected, the sink uses control messages to check the status of the affected nodes. SBFD was implemented in TinyOS on TelosB motes and its performance was assessed in a testbed network and in TOSSIM simulation. The method was found to achieve a fault detection accuracy of 90.7p to 95.0p for networks of 25 to 400 nodes at the cost of 0.164p to 0.239p additional control packets and a 0.5p reduction in node lifetime due to in-network packet tagging. Finally, a comparative study was conducted with existing solutions.
TL;DR: A lightweight algorithm for online load adaptation of energy-harvesting sensor nodes using supercapacitors as energy buffers is presented, particularly designed to handle the nonlinear system model and lightweight enough to run on low-power sensor node hardware.
Abstract: A sustainable, uniform, and utility-maximizing operation of energy-harvesting sensor networks requires methods for aligning consumption with harvest. This article presents a lightweight algorithm for online load adaptation of energy-harvesting sensor nodes using supercapacitors as energy buffers. The algorithm capitalizes on the elementary relationship between state of charge and voltage that is characteristic for supercapacitors. It is particularly designed to handle the nonlinear system model, and it is lightweight enough to run on low-power sensor node hardware. We define two energy policies, evaluate their performance using real-world solar-harvesting traces, and analyze the influence of the supercapacitor’s capacity and imprecisions in harvest forecasts. To show the practical merit of our algorithm, we devise a load adaptation scheme for multihop data collection sensor networks and run a 4-week field test. The results show that (i) choosing a duty cycle a priori is infeasible, (ii) our algorithm increases the achievable work load of a node when using forecasts, (iii) uniform and steady operation is achieved, and (iv) depletion can be prevented in most cases.
TL;DR: A novel algorithm for localization of Wireless Sensor Networks (WSNs) called Distributed Randomized Gradient Descent (DRGD) is presented and it is proved that in the case of noise-free distance measurements, the algorithm converges and provides the true location of the nodes.
Abstract: We present a novel algorithm for localization of Wireless Sensor Networks (WSNs) called Distributed Randomized Gradient Descent (DRGD) and prove that in the case of noise-free distance measurements, the algorithm converges and provides the true location of the nodes. For noisy distance measurements, the convergence properties of DRGD are discussed and an error bound on the location estimation error is obtained. In contrast to several recently proposed methods, DRGD does not require that the blind nodes be contained in the convex hull of the anchor nodes, and it can accurately localize the network with only a few anchors. Performance of DRGD is evaluated through extensive simulations and compared with three other algorithms, namely, the relaxation-based Second-Order Cone Programming (SOCP), the Simulated Annealing (SA), and the Semi-Definite Programing (SDP). Similar to DRGD, SOCP and SA are distributed algorithms, whereas SDP is centralized. The results show that DRGD successfully localizes the nodes in all the cases, whereas in many cases SOCP and SA fail. Finally, we present a modification of DRGD for mobile WSNs and demonstrate the efficacy of DRGD for localization of mobile networks with several simulation results.
TL;DR: Novel mixed-integer linear programs (MILPs) are proposed to design periodic trajectories and TX power policies for mobile agents that minimize the total energy consumption of the mobile agents in each period.
Abstract: In this article, we study the problem of dynamic coverage of a set of points of interest (POIs) in a time-varying environment. We consider the scenario where a physical quantity is constantly growing at certain rates at the POIs. A number of mobile agents are then deployed to periodically cover (sense or service) the POIs and keep the physical quantity under control bounded at all the POIs. We assume a communication-constrained operation, where the mobile agents need to communicate to a fixed remote station over realistic wireless links to complete their coverage task. We then propose novel mixed-integer linear programs (MILPs) to design periodic trajectories and TX power policies for the mobile agents that minimize the total energy (the summation of motion and communication energy) consumption of the mobile agents in each period, while (1) guaranteeing the boundedness of the quantity of interest at all the POIs, and (2) meeting the constraints on the connectivity of the mobile agents, the frequency of covering the POIs, and the total energy budget of the mobile agents. We furthermore provide a probabilistic analysis of the problem. Our results show the superior performance of the proposed framework for dynamic coverage in realistic fading environments.
TL;DR: This work proposes the physical-ratio-K (PRK) interference model as a reliability-oriented instantiation of the protocol model, and shows that PRK-based scheduling achieves a network throughput very close to what is enabled by physical-model- based scheduling while ensuring the required packet delivery reliability.
Abstract: Interference model is the basis of MAC protocol design in wireless networked sensing and control, and it directly affects the efficiency and predictability of wireless messaging. To exploit the strengths of both the physical and the protocol interference models, we analyze how network traffic, link length, and wireless signal attenuation affect the optimal instantiation of the protocol model. We also identify the inherent trade-off between reliability and throughput in the model instantiation. Our analysis sheds light on the open problem of efficiently optimizing the protocol model instantiation. Based on the analytical results, we propose the physical-ratio-K (PRK) interference model as a reliability-oriented instantiation of the protocol model. Via analysis, simulation, and testbed-based measurement, we show that PRK-based scheduling achieves a network throughput very close to (e.g., 95p) what is enabled by physical-model-based scheduling while ensuring the required packet delivery reliability. The PRK model inherits both the high fidelity of the physical model and the locality of the protocol model, thus it is expected to be suitable for distributed protocol design. These findings shed new light on wireless interference models; they also suggest new approaches to MAC protocol design in the presence of uncertainties in network and environmental conditions as well as application QoS requirements.
TL;DR: This work proposes a novel localized algorithm, named Back-Tracking Deployment (BTD), which involves significantly (80%) less robot moves and messages than LRV and is evaluated in comparison with the only competing algorithms SLD and LRV.
Abstract: Existing solutions to carrier-based sensor placement by a single robot in a bounded unknown Region of Interest (ROI) do not guarantee full area coverage or termination. We propose a novel localized algorithm, named Back-Tracking Deployment (BTD). To construct a full coverage solution over the ROI, mobile robots (carriers) carry static sensors as payloads and drop them at the visited empty vertices of a virtual square, triangular, or hexagonal grid. A single robot will move in a predefined order of directional preference until a dead end is reached. Then it back-tracks to the nearest sensor adjacent to an empty vertex (an “entrance” to an unexplored/uncovered area) and resumes regular forward movement and sensor dropping from there. To save movement steps, the back-tracking is carried out along a locally identified shortcut. We extend the algorithm to support multiple robots that move independently and asynchronously. Once a robot reaches a dead end, it will back-track, giving preference to its own path. Otherwise, it will take over the back-track path of another robot by consulting with neighboring sensors. We prove that BTD terminates within finite time and produces full coverage when no (sensor or robot) failures occur. We also describe an approach to tolerate failures and an approach to balance workload among robots. We then evaluate BTD in comparison with the only competing algorithms SLD [Chang et al. 2009a] and LRV [Batalin and Sukhatme 2004] through simulation. In a specific failure-free scenario, SLD covers only 40--50p of the ROI, whereas BTD covers it in full. BTD involves significantly (80p) less robot moves and messages than LRV.
TL;DR: This work studies the coverage of a line interval with a set of wireless sensors with adjustable coverage ranges and designs constant-approximation algorithms when the cost for all sensors is proportional to r for some constant κ ≥ 1, where r is the covering radius corresponding to the chosen power.
Abstract: One of the most fundamental tasks of wireless sensor networks is to provide coverage of the deployment region. We study the coverage of a line interval with a set of wireless sensors with adjustable coverage ranges. Each coverage range of a sensor is an interval centered at that sensor whose length is decided by the power the sensor chooses. The objective is to find a range assignment with the minimum cost. There are two variants of the optimization problem. In the discrete variant, each sensor can only choose from a finite set of powers, whereas in the continuous variant, each sensor can choose power from a given interval. For the discrete variant of the problem, a polynomial-time exact algorithm is designed. For the continuous variant of the problem, NP-hardness of the problem is proved and followed by an ILP formulation. Then, constant-approximation algorithms are designed when the cost for all sensors is proportional to rκ for some constant κ ≥ 1, where r is the covering radius corresponding to the chosen power. Specifically, if κ = 1, we give a 1.25-approximation algorithm and a fully polynomial-time approximation scheme; if κ > 1, we give a 2-approximation algorithm. We also show that the approximation analyses are tight.
TL;DR: This work presents a novel framework for selecting cameras to track people in a distributed smart camera network that is based on generalized information-theory and dynamically assign a subset of all available cameras to each target and track it in difficult circumstances of occlusions and limited fields of view with the same accuracy as when using all cameras.
Abstract: Tracking persons with multiple cameras with overlapping fields of view instead of with one camera leads to more robust decisions. However, operating multiple cameras instead of one requires more processing power and communication bandwidth, which are limited resources in practical networks.When the fields of view of different cameras overlap, not all cameras are equally needed for localizing a tracking target. When only a selected set of cameras do processing and transmit data to track the target, a substantial saving of resources is achieved. The recent introduction of smart cameras with on-board image processing and communication hardware makes such a distributed implementation of tracking feasible.We present a novel framework for selecting cameras to track people in a distributed smart camera network that is based on generalized information-theory. By quantifying the contribution of one or more cameras to the tracking task, the limited network resources can be allocated appropriately, such that the best possible tracking performance is achieved.With the proposed method, we dynamically assign a subset of all available cameras to each target and track it in difficult circumstances of occlusions and limited fields of view with the same accuracy as when using all cameras.
TL;DR: A method for selecting useful parts of an activity to present to a viewer using activity motifs and a novel framework to score the importance of activity occurrences and allow transfer of importance between temporally related activities without solving the correspondence problem are presented.
Abstract: Camera network systems generate large volumes of potentially useful data, but extracting value from multiple, related videos can be a daunting task for a human reviewer. Multicamera video summarization seeks to make this task more tractable by generating a reduced set of output summary videos that concisely capture important portions of the input set. We present a system that approaches summarization at the level of detected activity motifs and shortens the input videos by compacting the representation of individual activities. Additionally, redundancy is removed across camera views by omitting from the summary activity occurrences that can be predicted by other occurrences. The system also detects anomalous events within a unified framework and can highlight them in the summary. Our contributions are a method for selecting useful parts of an activity to present to a viewer using activity motifs and a novel framework to score the importance of activity occurrences and allow transfer of importance between temporally related activities without solving the correspondence problem. We provide summarization results for a two camera network, an eleven camera network, and data from PETS 2001. We also include results from Amazon Mechanical Turk human experiments to evaluate how our visualization decisions affect task performance.
TL;DR: This article proposes an optimization framework that integrates a local power management algorithm with a global distributed LM rate allocation scheme and applies it to solar-powered wireless sensor networks (SP-WSNs) to achieve both LM optimality and sustainable operation.
Abstract: Understanding the optimal usage of fluctuating renewable energy in wireless sensor networks (WSNs) is complex. Lexicographic max-min (LM) rate allocation is a good solution but is nontrivial for multihop WSNs, as both fairness and sensing rates have to be optimized through the exploration of all possible forwarding routes in the network. All current optimal approaches to this problem are centralized and offline, suffering from low scalability and large computational complexity—typically solving O(N2) linear programming problems for N-node WSNs. This article presents the first optimal distributed solution to this problem with much lower complexity. We apply it to solar-powered wireless sensor networks (SP-WSNs) to achieve both LM optimality and sustainable operation. Based on realistic models of both time-varying solar power and photovoltaic-battery hardware, we propose an optimization framework that integrates a local power management algorithm with a global distributed LM rate allocation scheme. The optimality, convergence, and efficiency of our approaches are formally proven. We also evaluate our algorithms via experiments on both solar-powered MICAz motes and extensive simulations using real solar energy data and practical power parameter settings. The results verify our theoretical analysis and demonstrate how our approach outperforms both the state-of-the-art centralized optimal and distributed heuristic solutions.
TL;DR: It is shown that an energy-balanced WSN is likely to maximize its lifespan and is more capable of significantly extending the WSNs lifespan than competing algorithms while delivering comparable or better tracking accuracy.
Abstract: A novel energy-balanced task-scheduling method is proposed that extends the lifespan of wireless sensor networks (WSNs) for collaborative target tracking using an unscented Kalman filter (UKF) algorithm. It is shown that the tracking accuracy is approximately proportional to the number of active sensor nodes participating in collaborative tracking. Excessive sensor nodes thus may be put to sleep mode to conserve energy provided there are a sufficient number of active sensor nodes. It is then shown that the lifespan of a WSN is dictated by the distribution of residue energy of sensor nodes. Specifically, we have shown that an energy-balanced WSN is likely to maximize its lifespan. As such, at each step of the tracking task, the head node must judiciously select active nodes from all sensors within the sensing range to minimize residue energy variations (energy balanced) while achieving desired tracking accuracy. This is formulated as a subset selection problem, which is shown to have a complexity that is NP-hard. Several energy-balanced scheduling for tracking (EBaST) heuristic algorithms are proposed to solve this problem with polynomial execution complexities. Extensive simulations have been conducted to compare EBaST against some state-of-the-art scheduling algorithms. It is observed that EBaST is more capable of significantly extending the WSNs lifespan than competing algorithms while delivering comparable or better tracking accuracy.
TL;DR: PDVLoc, a controlled location data-sharing framework based on selectively sharing data through a Personal Data Vault, is presented, showing that most users find that PDVLoc is useful to manage and control their location data, and are willing to continue using it.
Abstract: Location-Based Mobile Service (LBMS) is one of the most popular smartphone services. LBMS enables people to more easily connect with each other and analyze the aspects of their lives. However, sharing location data can leak people's privacy. We present PDVLoc, a controlled location data-sharing framework based on selectively sharing data through a Personal Data Vault (PDV). A PDV is a privacy architecture in which individuals retain ownership of their data. Data are routinely filtered before being shared with content-service providers, and users or data custodian services can participate in making controlled data-sharing decisions. Introducing PDVLoc gives users flexible and granular access control over their location data. We have implemented a prototype of PDVLoc and evaluated it using real location-sharing social networking applications, Google Latitude and Foursquare. Our user study of 19 participants over 20 days shows that most users find that PDVLoc is useful to manage and control their location data, and are willing to continue using PDVLoc.
TL;DR: This article proposes and evaluates a new approach for secret key extraction where multiple sensors collaborate in exchanging probe packets and collecting channel measurements, thereby resulting in more randomness in the information source for key extraction, and this in turn produces stronger secret keys.
Abstract: Secret key establishment is a fundamental requirement for private communication between two entities. In this article, we propose and evaluate a new approach for secret key extraction where multiple sensors collaborate in exchanging probe packets and collecting channel measurements. Essentially, measurements from multiple channels have a substantially higher differential entropy compared to the measurements from a single channel, thereby resulting in more randomness in the information source for key extraction, and this in turn produces stronger secret keys. We also explore the fundamental trade-off between the quadratic increase in the number of measurements of the channels due to multiple nodes per group versus a linear reduction in the sampling rate and a linear increase in the time gap between bidirectional measurements. To experimentally evaluate collaborative secret key extraction in wireless sensor networks, we first build a simple yet flexible testbed with multiple TelosB sensor nodes. Next, we perform large-scale experiments with different configurations of collaboration. Our experiments show that in comparison to the 1 × 1 configuration, collaboration among sensor nodes significantly increases the secret bit extraction per second, per probe, as well as per millijoule of transmission energy. In addition, we show that the collaborating nodes can improve the performance further when they exploit both space and frequency diversities.
TL;DR: This article provides both an analytical model and a distributed heuristic, called EN-MASSE, specifically tailored for energy-harvesting mission-centric WSNs, and demonstrates the effectiveness of this approach.
Abstract: Sensor mission assignment involves matching the sensing resources of a wireless sensor network (WSN) to appropriate tasks (missions), which may come to the network dynamically. Although solutions for WSNs with battery-operated nodes have been proposed for this problem, no attention has been given to networks whose nodes have energy-harvesting capabilities and are powered in part by uncontrollable environmental sources, which impose quite a different energy model. In this article we address this problem by providing both an analytical model and a distributed heuristic, called EN-MASSE, specifically tailored for energy-harvesting mission-centric WSNs. To assess the performance of our proposed solution we have interfaced TelosB nodes with solar cells and performed extensive experiments to derive models and traces of solar energy acquisition. We use such real-life traces in our simulations. A comparative performance evaluation between EN-MASSE and other schemes previously proposed in the literature has shown that our solution significantly outperforms existing energy-harvesting-unaware mission assignment schemes. Moreover, using our analytical model as a benchmark, we also show that the profit earned by EN-MASSE is close to the optimum. Finally, we have implemented our proposed solution in TinyOS and experimentally validated its performance, showing the effectiveness of our approach.
TL;DR: Filtering methods are presented that reconstruct the set of possible paths at three levels of resolution and an inexpensive, low-energy, easily deployable architecture was created which implements the beam model and validates the methods of the article with experiments.
Abstract: A problem is introduced in which a moving body (robot, human, animal, vehicle, and so on) travels among obstacles and binary detection beams that connect between obstacles or barriers. Each beam can be viewed as a virtual sensor that may have many possible alternative implementations. The task is to determine the possible body paths based only on sensor observations that each simply report that a beam crossing occurred. This is a basic filtering problem encountered in many settings, under a variety of sensing modalities. Filtering methods are presented that reconstruct the set of possible paths at three levels of resolution: (1) the possible sequences of regions (bounded by beams and obstacles) visited, (2) equivalence classes of homo-topic paths, and (3) the possible numbers of times the path winds around obstacles. In the simplest case, all beams are disjoint, distinguishable, and directed. More complex cases are then considered, allowing for any amount of beams overlapping, indistinguishability, and lack of directional information. The method was implemented in simulation. An inexpensive, low-energy, easily deployable architecture was also created which implements the beam model and validates the methods of the article with experiments.
TL;DR: Two models that improve the Principal Component-based Context Compression (PC3) model for contextual information forwarding among sensor nodes in a Wireless Sensor Network (WSN) are proposed.
Abstract: This article proposes two models that improve the Principal Component-based Context Compression (PC3) model for contextual information forwarding among sensor nodes in a Wireless Sensor Network (WSN). The proposed models (referred to as iPC3 and oPC3) address issues associated with the control of multivariate contextual information transmission in a stationary WSN. Because WSN nodes are typically battery equipped, the primary design goal of the models is to optimize the amount of energy used for data transmission while retaining data accuracy at high levels. The proposed energy conservation techniques and algorithms are based on incremental principal component analysis and optimal stopping theory. iPC3 and oPC3 models are presented and compared with PC3 and other models found in the literature through simulations. The proposed models manage to extend the lifetime of a WSN application by improving energy efficiency within WSN.