Showing papers in "ACM Transactions on Sensor Networks in 2013"

Compression in wireless sensor networks: A survey and comparative evaluation

Abstract: Wireless sensor networks (WSNs) are highly resource constrained in terms of power supply, memory capacity, communication bandwidth, and processor performance. Compression of sampling, sensor data, and communications can significantly improve the efficiency of utilization of three of these resources, namely, power supply, memory and bandwidth. Recently, there have been a large number of proposals describing compression algorithms for WSNs. These proposals are diverse and involve different compression approaches. It is high time that these individual efforts are put into perspective and a more holistic view taken. In this article, we take a step in that direction by presenting a survey of the literature in the area of compression and compression frameworks in WSNs. A comparative study of the various approaches is also provided. In addition, open research issues, challenges and future research directions are highlighted.

CTP: An efficient, robust, and reliable collection tree protocol for wireless sensor networks

Abstract: We describe CTP, a collection routing protocol for wireless sensor networks. CTP uses three techniques to provide efficient, robust, and reliable routing in highly dynamic network conditions. CTP's link estimator accurately estimates link qualities by using feedback from both the data and control planes, using information from multiple layers through narrow, platform-independent interfaces. Second, CTP uses the Trickle algorithm to time the control traffic, sending few beacons in stable topologies yet quickly adapting to changes. Finally, CTP actively probes the topology with data traffic, quickly discovering and fixing routing failures. Through experiments on 13 different testbeds, encompassing seven platforms, six link layers, and multiple densities and frequencies, and detailed observations of a long-running sensor network application that uses CTP, we study how these three techniques contribute to CTP's overall performance.

Towards statistically strong source anonymity for sensor networks

Abstract: For sensor networks deployed to monitor and report real events, event source anonymity is an attractive and critical security property, which unfortunately is also very difficult and expensive to achieve. This is not only because adversaries may attack against sensor source privacy through traffic analysis, but also because sensor networks are very limited in resources. As such, a practical trade-off between security and performance is desirable. In this article, for the first time we propose the notion of statistically strong source anonymity, under a challenging attack model where a global attacker is able to monitor the traffic in the entire network. We propose a scheme called FitProbRate, which realizes statistically strong source anonymity for sensor networks. We demonstrate the robustness of our scheme under various statistical tests that might be employed by the attacker to detect real events. Our analysis and simulation results show that our scheme, besides providing source anonymity, can significantly reduce real event reporting latency compared to two baseline schemes.However, the degree of source anonymity in the FitProbRate scheme might decrease as real message rate increases. We propose a dynamic mean scheme which has better performance under high real message rates. Simulation results show that the dynamic mean scheme is capable of increasing the attacker's false positive rate and decreasing the attacker's Bayesian detection rate significantly even under high-rate continuous real messages.

On the quality and value of information in sensor networks

Abstract: The increasing use of sensor-derived information from planned, ad-hoc, and/or opportunistically deployed sensor networks provides enhanced visibility to everyday activities and processes, enabling fast-paced data-to-decision in personal, social, civilian, military, and business contexts. The value that information brings to this visibility and ensuing decisions depends on the quality characteristics of the information gathered. In this article, we highlight, refine, and extend upon our past work in the areas of quality and value of information (QoI and VoI) for sensor networks. Specifically, we present and elaborate on our two-layer QoI/VoI definition, where the former relates to context-independent aspects and the latter to context-dependent aspects of an information product. Then, we refine our taxonomy of pertinent QoI and VoI attributes anchored around a simple ontological relationship between the two. Finally, we introduce a framework for scoring and ranking information products based on their VoI attributes using the analytic hierarchy multicriteria decision process, illustrated via a simple example.

Secure ad hoc trust initialization and key management in wireless body area networks

Abstract: The body area network (BAN) is a key enabling technology in e-healthcare. An important security issue is to establish initial trust relationships among the BAN devices before they are actually deployed and generate necessary shared secret keys to protect the subsequent wireless communications. Due to the ad hoc nature of the BAN and the extreme resource constraints of sensor devices, providing secure as well as efficient and user-friendly trust initialization is a challenging task. Traditional solutions for wireless sensor networks mostly depend on key predistribution, which is unsuitable for a BAN in many ways. In this article, we propose group device pairing (GDP), a user-aided multi-party authenticated key agreement protocol. Through GDP, a group of sensor devices that have no pre-shared secrets establish initial trust by generating various shared secret keys out of an unauthenticated channel. Devices authenticate themselves to each other with the aid of a human user who performs visual verifications. The GDP supports fast batch deployment, addition and revocation of sensor devices, does not rely on any additional hardware device, and is mostly based on symmetric key cryptography. We formally prove the security of the proposed protocols, and we implement GDP on a sensor network testbed and report performance evaluation results.

The GINSENG system for wireless monitoring and control: Design and deployment experiences

Abstract: Today's industrial facilities, such as oil refineries, chemical plants, and factories, rely on wired sensor systems to monitor and control the production processes. The deployment and maintenance of such cabled systems is expensive and inflexible. It is, therefore, desirable to replace or augment these systems using wireless technology, which requires us to overcome significant technical challenges. Process automation and control applications are mission-critical and require timely and reliable data delivery, which is difficult to provide in industrial environments with harsh radio environments. In this article, we present the GINSENG system which implements performance control to allow us to use wireless sensor networks for mission-critical applications in industrial environments. GINSENG is a complete system solution that comprises on-node system software, network protocols, and back-end systems with sophisticated data processing capability. GINSENG assumes that a deployment can be carefully planned. A TDMA-based MAC protocol, tailored to the deployment environment, is employed to provide reliable and timely data delivery. Performance debugging components are used to unintrusively monitor the system performance and identify problems as they occur. The article reports on a real-world deployment of GINSENG in an especially challenging environment of an operational oil refinery in Sines, Portugal. We provide experimental results from this deployment and share the experiences gained. These results demonstate the use of GINSENG for sensing and actuation and allow an assessment of its ability to operate within the required performance bounds. We also identify shortcomings that manifested during the evaluation phase, thus giving a useful perspective on the challenges that have to be overcome in these harsh application settings.

Coverage algorithms for visual sensor networks

Abstract: Visual sensor networks (VSNs) are becoming increasingly popular in a number of application domains. A distinguishing characteristic of VSNs is to self-configure to minimize the need for operator control and to improve scalability. One of the areas of self-configuration is camera coverage control that is, how should cameras adjust their field-of-views to cover maximum targetsq This is an NP-hard problem. We show that the existing heuristics have a number of weaknesses that influence both coverage and overhead. Therefore, we first propose a computationally efficient centralized heuristic that provides near-optimal coverage for small-scale networks. However, it requires significant communication and computation overhead, making it unsuitable for large-scale networks. Thus, we develop a distributed algorithm that outperforms the existing distributed algorithm with lower communication overhead, at the cost of coverage accuracy. We show that the proposed heuristics guarantee to cover at least half of the targets covered by the optimal solution. Finally, to gain benefits of both centralized and distributed algorithms, we propose a hierarchical algorithm where cameras are decomposed into neighborhoods that coordinate their coverage using an elected local coordinator. We observe that the hierarchical algorithm provides scalable near-optimal coverage with networking cost significantly less than that of centralized and distributed solutions.

SenseCode: Network coding for reliable sensor networks

Abstract: Designing a communication protocol for sensor networks often involves obtaining the right trade-off between energy efficiency and end-to-end packet error rate. In this article, we show that network coding provides a means to elegantly balance these two goals. We present the design and implementation of SenseCode, a collection protocol for sensor networks—and, to the best of our knowledge, the first such implemented protocol to employ network coding. SenseCode provides a way to gracefully introduce a configurable amount of redundant information into the network, thereby decreasing end-to-end packet error rate in the face of packet loss. We compare SenseCode to the best (to our knowledge) existing alternative and show that it reduces end-to-end packet error rate in highly dynamic environments, while consuming a comparable amount of network resources. We have implemented SenseCode as a TinyOS module and evaluate it through extensive TOSSIM simulations.

Enhancing RSSI-based tracking accuracy in wireless sensor networks

Abstract: In recent years, the demand for high-precision tracking systems has significantly increased in the field of Wireless Sensor Network (WSN). A new tracking system based on exploitation of Received Signal Strength Indicator (RSSI) measurements in WSN is proposed. The proposed system is designed in particular for WSNs that are deployed in close proximity and can transmit data at a high transmission rate. The close proximity and an optimized transmit power level enable accurate conversion of RSSI measurements to range estimates. Having an adequate transmission rate enables spatial-temporal correlation between consecutive RSSI measurements. In addition, advanced statistical and signal processing methods are used to mitigate channel distortion and to compensate for packet loss. The system is evaluated in indoor conditions and achieves tracking resolution of a few centimeters which is compatible with theoretical bounds.

Beyond triangle inequality: Sifting noisy and outlier distance measurements for localization

Abstract: Knowing accurate positions of nodes in wireless ad hoc and sensor networks is essential for a wide range of pervasive and mobile applications. However, errors are inevitable in distance measurements and we observe that a small number of outliers can degrade localization accuracy drastically. To deal with noisy and outlier ranging results, triangle inequality, is often employed in existing approaches. Our study shows that triangle inequality has many limitations, which make it far from accurate and reliable. In this study, we formally define the outlier detection problem for network localization and build a theoretical foundation to identify outliers based on graph embeddability and rigidity theory. Our analysis shows that the redundancy of distance measurements plays an important role. We then design a bilateration generic cycles-based outlier detection algorithm, and examine its effectiveness and efficiency through a network prototype implementation of MicaZ motes as well as extensive simulations. The results show that our design significantly improves the localization accuracy by wisely rejecting outliers.

Optimizing sensor network reprogramming via in situ reconfigurable components

Abstract: Wireless reprogramming of sensor nodes is a critical requirement in long-lived wireless sensor networks (WSNs) addressing several concerns, such as fixing bugs, upgrading the operating system and applications, and adapting applications behavior according to the physical environment. In such resource-poor platforms, the ability to efficiently delimit and reconfigure the necessary portion of sensor software—instead of updating the full binary image—is of vital importance. However, most existing approaches in this field have not been adopted widely to date due to the extensive use of WSN resources or lack of generality. In this article, we therefore consider WSN programming models and runtime reconfiguration models as two interrelated factors and we present an integrated approach for addressing efficient reprogramming in WSNs. The middleware solution we propose,

Connectivity-based and anchor-free localization in large-scale 2D/3D sensor networks

Abstract: A connectivity-based and anchor-free three-dimensional localization (CATL) scheme is presented for large-scale sensor networks with concave regions. It distinguishes itself from previous work with a combination of three features: (1) it works for networks in both 2D and 3D spaces, possibly containing holes or concave regions; (2) it is anchor-free and uses only connectivity information to faithfully recover the original network topology, up to scaling and rotation; (3) it does not depend on the knowledge of network boundaries, which suits it well to situations where boundaries are difficult to identify. The key idea of CATL is to discover the notch nodes, where shortest paths bend and hop-count-based distance starts to significantly deviate from the true Euclidean distance. An iterative protocol is developed that uses a notch-avoiding multilateration mechanism to localize the network. Simulations show that CATL achieves accurate localization results with a moderate per-node message cost.

Duty-cycle optimization for IEEE 802.15.4 wireless sensor networks

Abstract: Most applications of wireless sensor networks require reliable and timely data communication with maximum possible network lifetime under low traffic regime. These requirements are very critical especially for the stability of wireless sensor and actuator networks. Designing a protocol that satisfies these requirements in a network consisting of sensor nodes with traffic pattern and location varying over time and space is a challenging task. We propose an adaptive optimal duty-cycle algorithm running on top of the IEEE 802.15.4 medium access control to minimize power consumption while meeting the reliability and delay requirements. Such a problem is complicated because simple and accurate models of the effects of the duty cycle on reliability, delay, and power consumption are not available. Moreover, the scarce computational resources of the devices and the lack of prior information about the topology make it impossible to compute the optimal parameters of the protocols. Based on an experimental implementation, we propose simple experimental models to expose the dependency of reliability, delay, and power consumption on the duty cycle at the node and validate it through extensive experiments. The coefficients of the experimental-based models can be easily computed on existing IEEE 802.15.4 hardware platforms by introducing a learning phase without any explicit information about data traffic, network topology, and medium access control parameters. The experimental-based model is then used to derive a distributed adaptive algorithm for minimizing the power consumption while meeting the reliability and delay requirements in the packet transmission. The algorithm is easily implementable on top of the IEEE 802.15.4 medium access control without any modifications of the protocol. An experimental implementation of the distributed adaptive algorithm on a test bed with off-the-shelf wireless sensor devices is presented. The experimental performance of the algorithms is compared to the existing solutions from the literature. The experimental results show that the experimental-based model is accurate and that the proposed adaptive algorithm attains the optimal value of the duty cycle, maximizing the lifetime of the network while meeting the reliability and delay constraints under both stationary and transient conditions. Specifically, even if the number of devices and their traffic configuration change sharply, the proposed adaptive algorithm allows the network to operate close to its optimal value. Furthermore, for Poisson arrivals, the duty-cycle protocol is modeled as a finite capacity queuing system in a star network. This simple analytical model provides insights into the performance metrics, including the reliability, average delay, and average power consumption of the duty-cycle protocol.

System-level calibration for data fusion in wireless sensor networks

Abstract: Wireless sensor networks are typically composed of low-cost sensors that are deeply integrated in physical environments. As a result, the sensing performance of a wireless sensor network is inevitably undermined by biases in imperfect sensor hardware and the noises in data measurements. Although a variety of calibration methods have been proposed to address these issues, they often adopt the device-level approach that becomes intractable for moderate-to large-scale networks. In this article, we propose a two-tier system-level calibration approach for a class of sensor networks that employ data fusion to improve the sensing performance. In the first tier of our calibration approach, each sensor learns its local sensing model from noisy measurements using an online algorithm and only transmits a few model parameters. In the second tier, sensors' local sensing models are then calibrated to a common system sensing model. Our approach fairly distributes computation overhead among sensors and significantly reduces the communication overhead of calibration compared with the device-level approach. Based on this approach, we develop an optimal model calibration scheme that maximizes the target detection probability of a sensor network under bounded false alarm rate. Our approach is evaluated by both experiments on a testbed of TelosB motes and extensive simulations based on synthetic datasets as well as data traces collected in a real vehicle detection experiment. The results demonstrate that our system-level calibration approach can significantly boost the detection performance of sensor networks in scenarios with low signal-to-noise ratios.

Modeling and stability analysis of hybrid multiple access in the IEEE 802.15.4 protocol

Abstract: To offer flexible quality of service to several classes of applications, the medium access control (MAC) protocol of IEEE 802.15.4 wireless sensor networks (WSNs) combines the advantages of a random access with contention with a time division multiple access (TDMA) without contention. Understanding reliability, delay, and throughput is essential to characterizing the fundamental limitations of the MAC and optimizing its parameters. Nevertheless, there is not yet a clear investigation of the achievable performance of hybrid MAC. In this article, an analytical framework for modeling the behavior of the hybrid MAC protocol of the IEEE 802.15.4 standard is proposed. The main challenge for an accurate analysis is the coexistence of the stochastic behavior of the random access and the deterministic behavior of the TDMA scheme. The analysis is done in three steps. First, the contention access scheme of the IEEE 802.15.4 exponential back-off process is modeled through an extended Markov chain that takes into account channel, retry limits, acknowledgements, unsaturated traffic, and superframe period. Second, the behavior of the TDMA access scheme is modeled by another Markov chain. Finally, the two chains are coupled to obtain a complete model of the hybrid MAC. By using this model, the network performance in terms of reliability, average packet delay, average queuing delay, and throughput is evaluated through both theoretical analysis and experiments. The protocol has been implemented and evaluated on a testbed with off-the-shelf wireless sensor devices to demonstrate the utility of the analysis in a practical setup. It is established that the probability density function of the number of received packets per superframe follows a Poisson distribution. It is determined under which conditions the guaranteed time slot allocation mechanism of IEEE 802.15.4 is stable. It is shown that the mutual effect between throughput of the random access and the TDMA scheme for a fixed superframe length is critical to maximizing the overall throughput of the hybrid MAC. In high traffic load, the throughput of the random access mechanism dominates over TDMA due to the constrained use of TDMA in the standard. Furthermore, it is shown that the effect of imperfect channels and carrier sensing on system performance heavily depends on the traffic load and limited range of the protocol parameters. Finally, it is argued that the traffic generation model established in this article may be used to design an activation timer mechanism in a modified version of the CSMA/CA algorithm that guarantees a stable network performance.

A low-bandwidth camera sensor platform with applications in smart camera networks

Abstract: Smart camera networks have recently emerged as a new class of sensor network infrastructure that is capable of supporting high-power in-network signal processing and enabling a wide range of applications. In this article, we provide an exposition of our efforts to build a low-bandwidth wireless camera network platform, called CITRIC, and its applications in smart camera networks. The platform integrates a camera, a microphone, a frequency-scalable (up to 624 MHz) CPU, 16 MB FLASH, and 64 MB RAM onto a single device. The device then connects with a standard sensor network mote to form a wireless camera mote. With reasonably low power consumption and extensive algorithmic libraries running on a decent operating system that is easy to program, CITRIC is ideal for research and applications in distributed image and video processing. Its capabilities of in-network image processing also reduce communication requirements, which has been high in other existing camera networks with centralized processing. Furthermore, the mote easily integrates with other low-bandwidth sensor networks via the IEEE 802.15.4 protocol. To justify the utility of CITRIC, we present several representative applications. In particular, concrete research results will be demonstrated in two areas, namely, distributed coverage hole identification and distributed object recognition.

Adaptive energy-efficient scheduling for hierarchical wireless sensor networks

Abstract: Most Wireless Sensor Network (WSN) applications require distributed signal and collaborative data processing. One of the critical issues for enabling collaborative processing in WSNs is how to schedule tasks in a systematic way, including assigning tasks to sensor nodes, and determining their execution and communication sequence. Since WSN nodes are very resource constrained, mainly regarding their energy supply, one major concern when scheduling tasks in such environments is to minimize and balance the energy consumption, so that the system operational lifetime is maximized. We propose a heuristic-based three-phase algorithm (TPTS) for allocating tasks to multiple clusters in hierarchical WSNs that aims at finding a scheduling scheme that minimizes the overall energy consumption and balances the workload of the system while meeting the application's deadline. The performance of the proposed algorithm and the effect of several parameters on its behavior were evaluated by simulations, with promising results. The experimental results show that the time and energy performance of TPTS are close to the time and energy of benchmarks in most cases, while load balance is always provided.

Fusion-based volcanic earthquake detection and timing in wireless sensor networks

Abstract: Volcano monitoring is of great interest to public safety and scientific explorations. However, traditional volcanic instrumentation such as broadband seismometers are expensive, power hungry, bulky, and difficult to install. Wireless sensor networks (WSNs) offer the potential to monitor volcanoes on unprecedented spatial and temporal scales. However, current volcanic WSN systems often yield poor monitoring quality due to the limited sensing capability of low-cost sensors and unpredictable dynamics of volcanic activities. In this article, we propose a novel quality-driven approach to achieving real-time, distributed, and long-lived volcanic earthquake detection and timing. By employing novel in-network collaborative signal processing algorithms, our approach can meet stringent requirements on sensing quality (i.e., low false alarm/missing rate, short detection delay, and precise earthquake onset time) at low power consumption. We have implemented our algorithms in TinyOS and conducted extensive evaluation on a testbed of 24 TelosB motes as well as simulations based on real data traces collected during 5.5 months on an active volcano. We show that our approach yields near-zero false alarm/missing rate, less than one second of detection delay, and millisecond precision earthquake onset time while achieving up to six-fold energy reduction over the current data collection approach.

Surface-level path loss modeling for sensor networks in flat and irregular terrain

Abstract: Many wireless sensor network applications require sensor nodes to be deployed on the ground or other surfaces. However, there has been little effort to characterize the large- and small-scale path loss for surface-level radio communications. We present a comprehensive measurement of path loss and fading characteriztics for surface-level sensor nodes in the 400 MHz band in both flat and irregular outdoor terrain in an effort to improve the understanding of surface-level sensor network communications performance and to increase the accuracy of sensor network modeling and simulation. Based on our measurement results, we characterize the spatial small-scale area fading effects as a Rician distribution with a distance-dependent K-factor. We also propose a new semi-empirical path loss model for outdoor surface-level wireless sensor networks called the Surface-Level Irregular Terrain (SLIT) model. We verify our model by comparing measurement results with predicted values obtained from high-resolution digital elevation model (DEM) data and computer simulation for the 400 MHz and 2.4 GHz band. Finally, we discuss the impact of the SLIT model and demonstrate through simulation the effects when SLIT is used as the path loss model for existing sensor network protocols.

Energy efficiency in wireless sensor networks: A game-theoretic approach based on coalition formation

Abstract: A coalitional game theoretic scheme is proposed that aims at maximizing wireless sensor network lifetime under specified QoS. Employing a small number of nodes of increased computing power and lifetime called representatives, an adaptive clustering scheme is proposed where neighboring nodes form coalitions in order to increase energy efficiency at the cost of controllable data-accuracy reduction. The coalition formation is globally optimized by the representatives. The spatial correlation of the sensed phenomenon measurements is exploited to formulate a cooperation scheme that reduces drastically the number of node transmissions. The specifications regarding the accuracy of the collected data determine the extent of coalition formation. The efficiency and stability of the proposed coalitional scheme are studied through simulations.

Design, development, and performance evaluation of a low-cost, low-power wake-up radio system for wireless sensor networks

Abstract: Energy-efficient operation is a challenge for wireless sensor networks (WSNs) A common method employed for this purpose is duty-cycled operation, which extends battery lifetime yet incurs several types of energy wastes and challenges A promising alternative to duty-cycled operation is the use of wake-up radio (WuR), where the main microcontroller unit (MCU) and transceiver, that is, the two most energy-consuming elements, are kept in energy-saving mode until a special signal from another node is received by an attached, secondary, ultra-low power receiver Next, this so-called wake-up receiver generates an interrupt to activate the receiver node's MCU and, consequently, the main radio This article presents a complete wake-up radio design that targets simplicity in design for the monetary cost and flexibility concerns, along with a good operation range and very low power consumption Both the transmitter (WuTx) and the receiver (WuRx) designs are presented with the accompanying physical experiments for several design alternatives Detailed analysis of the end system is provided in terms of both operational distance (more than 10 m) and current consumption (less than 1 μA) As a reference, a commercial WuR system is analyzed and compared to the presented system by expressing the trade-offs and advantages of both systems

On energy-efficient trap coverage in wireless sensor networks

Abstract: In wireless sensor networks (WSNs), trap coverage has recently been proposed to trade off between the availability of sensor nodes and sensing performance. It offers an efficient framework to tackle the challenge of limited resources in large-scale sensor networks. Currently, existing works only studied the theoretical foundation of how to decide the deployment density of sensors to ensure the desired degree of trap coverage. However, practical issues, such as how to efficiently schedule sensor node to guarantee trap coverage under an arbitrary deployment, are still left untouched. In this article, we formally formulate the Minimum Weight Trap Cover Problem and prove it is an NP-hard problem. To solve the problem, we introduce a bounded approximation algorithm, called Trap Cover Optimization (TCO) to schedule the activation of sensors while satisfying specified trap coverage requirement. We design Localized Trap Coverage Protocol as the localized implementation of TCO. The performance of Minimum Weight Trap Coverage we find is proved to be at most O(ρ) times of the optimal solution, where ρ is the density of sensor nodes in the region. To evaluate our design, we perform extensive simulations to demonstrate the effectiveness of our proposed algorithm and show that our algorithm achieves at least 14p better energy efficiency than the state-of-the-art solution.

Achieving full-view coverage in camera sensor networks

Abstract: Camera sensors are different from traditional scalar sensors, as cameras at different positions can form very different views of the object. However, traditional coverage model does not consider this intrinsic property of camera sensors. To address this issue, a novel model called full-view coverage is proposed. It uses the angle between the object's facing direction and the camera's viewing direction to measure the quality of coverage. An object is full-view covered if there is always a camera to cover it no matter which direction it faces and the camera's viewing direction is sufficiently close to the object's facing direction. An efficient method is proposed for full-view coverage detection in any given camera sensor networks, and a sufficient condition on the sensor density needed for full-view coverage in a random uniform deployment is derived. In addition, the article shows a necessary and sufficient condition on the sensor density for full-view coverage in a triangular lattice-based deployment. Based on the full-view coverage model, the article further studies the barrier coverage problem. Existing weak and strong barrier coverage models are extended by considering direction issues in camera sensor networks. With these new models, weak/strong barrier coverage verification problems are introduced, and new detection methods are proposed and evaluated.

Energy-efficient localization: GPS duty cycling with radio ranging

Abstract: GPS is a commonly used and convenient technology for determining absolute position in outdoor environments, but its high power consumption leads to rapid battery depletion in mobile devices. An obvious solution is to duty cycle the GPS module, which prolongs the device lifetime at the cost of increased position uncertainty while the GPS is off. This article addresses the trade-off between energy consumption and localization performance in a mobile sensor network application. The focus is on augmenting GPS location with more energy-efficient location sensors to bound position estimate uncertainty while GPS is off. Empirical GPS and radio contact data from a large-scale animal tracking deployment is used to model node mobility, radio performance, and GPS. Because GPS takes a considerable, and variable, time after powering up before it delivers a good position measurement, we model the GPS behavior through empirical measurements of two GPS modules. These models are then used to explore duty cycling strategies for maintaining position uncertainty within specified bounds. We then explore the benefits of using short-range radio contact logging alongside GPS as an energy-inexpensive means of lowering uncertainty while the GPS is off, and we propose strategies that use RSSI ranging and GPS back-offs to further reduce energy consumption. Results show that our combined strategies can cut node energy consumption by one third while still meeting application-specific positioning criteria.

Diagnostic tracing for wireless sensor networks

Abstract: Wireless sensor networks are typically deployed in harsh environments, thus post-deployment failures are not infrequent. An execution trace containing events in their order of execution could play a crucial role in postmortem diagnosis of these failures. Obtaining such a trace however is challenging due to stringent resource constraints. We propose an efficient approach to intraprocedural and interprocedural control-flow tracing that generates traces of all interleaving concurrent events and of the control-flow paths taken inside those events. We demonstrate the effectiveness of our approach with the help of case studies and illustrate its low overhead through measurements and simulations.

Controlled and self-organized routing for large-scale wireless sensor networks

Abstract: Improving the scalability and robustness of wireless sensor networks is an important task, and much research on self-organization has been conducted toward this end. However, desired behavior is not yet guaranteed in much larger networks based on pure self-organization. In this article, we propose a controlled potential-based routing protocol implementing a novel controlled self-organization scheme that also allows for external control. The scheme obtains close-to-optimal network behavior by this external control which controls a part of nodes in the network. We show that global traffic flow can be controlled through simulation experiments with a multi-sink sensor network. For example, traffic loads can be equalized among heterogeneously distributed sink nodes, and load balancing among the relay nodes based on remaining energy can bring an approximate four times extension of network lifetime. The proposed method is furthermore robust to message loss and resilient to failure of the sink node.

A high-frequency sampling monitoring system for environmental and structural applications

Abstract: High-frequency sampling is not only a prerogative of high-energy physics or machinery diagnostic monitoring: critical environmental and structural health monitoring applications also have such a challenging constraint. Moreover, such unique design constraints are often coupled with the requirement of high synchronism among the distributed acquisition units, minimal energy consumption, and large communication bandwidth. Such severe constraints have led scholars to suggest wired centralized monitoring solutions, which have only recently been complemented with wireless technologies. This article suggests a hybrid wireless-wired monitoring system combining the advantages of wireless and wired technologies within a distributed high-frequency-sampling framework. The suggested architecture satisfies the mentioned constraints, thanks to an ad-hoc design of the hardware, the availability of efficient energy management policies, and up-to-date harvesting mechanisms. At the same time, the architecture supports adaptation capabilities by relying on the remote reprogrammability of key application parameters. The proposed architecture has been successfully deployed in the Swiss-Italian Alps to monitor the collapse of rock faces in three geographical areas.

Real-time routing in wireless sensor networks: A potential field approach

Abstract: Wireless Sensor Networks (WSNs) are embracing an increasing number of real-time applications subject to strict delay constraints. Utilizing the methodology of potential field in physics, in this article we effectively address the challenges of real-time routing in WSNs. In particular, based on a virtual composite potential field, we propose the Potential-based Real-Time Routing (PRTR) protocol that supports real-time routing using multipath transmission. PRTR minimizes delay for real-time traffic and alleviates possible congestions simultaneously. Since the delay bounds of real-time flows are extremely important, the end-to-end delay bound for a single flow is derived based on the Network Calculus theory. The simulation results show that PRTR minimizes the end-to-end delay for real-time routing, and also guarantees a tight bound on the delay.

Object tracking in the presence of occlusions using multiple cameras: A sensor network approach

Abstract: This article describes a sensor network approach to tracking a single object in the presence of static and moving occluders using a network of cameras. To conserve communication bandwidth and energy, we combine a task-driven approach with camera subset selection. In the task-driven approach, each camera first performs simple local processing to detect the horizontal position of the object in the image. This information is then sent to a cluster head to track the object. We assume the locations of the static occluders to be known, but only prior statistics on the positions of the moving occluders are available. A noisy perspective camera measurement model is introduced, where occlusions are captured through occlusion indicator functions. An auxiliary particle filter that incorporates the occluder information is used to track the object. The camera subset selection algorithm uses the minimum mean square error of the best linear estimate of the object position as a metric, and tracking is performed using only the selected subset of cameras.Using simulations and preselected subsets of cameras, we investigate (i) the dependency of the tracker performance on the accuracy of the moving occluder priors, (ii) the trade-off between the number of cameras and the occluder prior accuracy required to achieve a prescribed tracker performance, and (iii) the importance of having occluder priors to the tracker performance as the number of occluders increases. We find that computing moving occluder priors may not be worthwhile, unless it can be obtained cheaply and to high accuracy. We also investigate the effect of dynamically selecting the subset of camera nodes used in tracking on the tracking performance. We show through simulations that a greedy selection algorithm performs close to the brute-force method and outperforms other heuristics, and the performance achieved by greedily selecting a small fraction of the cameras is close to that of using all the cameras.

Localization with sparse acoustic sensor network using UAVs as information-seeking data mules

Abstract: We propose and demonstrate a novel architecture for on-the-fly inference while collecting data from sparse sensor networks. In particular, we consider source localization using acoustic sensors dispersed over a large area, with the individual sensors located too far apart for direct connectivity. An Unmanned Aerial Vehicle (UAV) is employed for collecting sensor data, with the UAV route adaptively adjusted based on data from sensors already visited, in order to minimize the time to localize events of interest. The UAV therefore acts as a information-seeking data mule, not only providing connectivity, but also making Bayesian inferences from the data gathered in order to guide its future actions. The system we demonstrate has a modular architecture, comprising efficient algorithms for acoustic signal processing, routing the UAV to the sensors, and source localization. We report on extensive field tests which not only demonstrate the effectiveness of our general approach, but also yield specific practical insights into GPS time synchronization and localization accuracy, acoustic signal and channel characteristics, and the effects of environmental phenomena.