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138
Time synchronization for high latency acoustic networks
- In Proc. IEEE InfoCom
, 2006
"... Abstract — Distributed time synchronization is an important part of a sensor network where sensing and actuation must be coordinated across multiple nodes. Several time synchronization protocol that maximize accuracy and energy conservation have been developed, including FTSP, TPSN, and RBS. All of ..."
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Cited by 71 (10 self)
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Abstract — Distributed time synchronization is an important part of a sensor network where sensing and actuation must be coordinated across multiple nodes. Several time synchronization protocol that maximize accuracy and energy conservation have been developed, including FTSP, TPSN, and RBS. All of these assume nearly instantaneous wireless communication between sensor nodes; each of them work well in today’s RF-based sensor networks. We are just beginning to explore underwater sensor networks where communication is primarily via acoustic telemetry. With acoustic communication, where the propagation speed is nearly five orders of magnitude slower than RF, assumptions about rapid communication are incorrect and new approaches to time synchronization are required. We present Time Synchronization for High Latency (TSHL), designed assuming such high latency propagation. We show through analysis and simulation that it achieves precise time synchronization with minimal energy cost. Although at very short distances existing protocols are adequate, TSHL shows twice the accuracy at 500m, demonstrating the need to model both clock skew and propagation latency. I.
An energy-aware resource-centric RTOS for sensor networks.
- In RTSS ’05: Proceedings of the 26th IEEE International Real-Time Systems Symposium
, 2005
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Tracking and coordination of multiple agents using sensor networks: system design, algorithms and experiments
"... This paper considers the problem of pursuit evasion games (PEGs), where a group of pursuers is required to chase and capture a group of evaders in minimum time with the aid of a sensor network. We assume that a sensor network is previously deployed and provides global observability of the surveilla ..."
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Cited by 43 (13 self)
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This paper considers the problem of pursuit evasion games (PEGs), where a group of pursuers is required to chase and capture a group of evaders in minimum time with the aid of a sensor network. We assume that a sensor network is previously deployed and provides global observability of the surveillance region, allowing an optimal pursuit policy. While sensor networks provide global observability, they cannot provide high quality measurements in a timely manner due to packet losses, communication delays, and false detections. This has been the main challenge in developing a real-time control system using sensor networks. We address this challenge by developing a real-time hierarchical control system which decouples the estimation of evader states from the control of pursuers via multiple layers of data fusion. While a sensor network generates noisy, inconsistent, and bursty measurements, the multiple layers of data fusion convert them into consistent and high quality measurements and forward them to the controllers of pursuers in a timely manner. For this control system, three new algorithms are developed: multi-sensor fusion, multi-target tracking and multi-agent coordination algorithms. The multi-sensor fusion algorithm converts correlated sensor measurements into position estimates, the multi-target tracking algorithm tracks an unknown number of targets, and the multi-agent coordination algorithm coordinates pursuers to capture all evaders in minimum time using a robust minimum-time feedback controller. The combined system is evaluated in simulation and tested in a sensor network deployment. To our knowledge, this paper presents the first demonstration of multi-target tracking using a sensor network without relying on classification.
Data Fusion Improves the Coverage of Wireless Sensor Networks
, 2009
"... Wireless sensor networks (WSNs) have been increasingly available for critical applications such as security surveillance and environmental monitoring. An important performance measure of such applications is sensing coverage that characterizes how well a sensing field is monitored by a network. Alth ..."
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Cited by 33 (7 self)
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Wireless sensor networks (WSNs) have been increasingly available for critical applications such as security surveillance and environmental monitoring. An important performance measure of such applications is sensing coverage that characterizes how well a sensing field is monitored by a network. Although advanced collaborative signal processing algorithms have been adopted by many existing WSNs, most previous analytical studies on sensing coverage are conducted based on overly simplistic sensing models (e.g., the disc model) that do not capture the stochastic nature of sensing. In this paper, we attempt to bridge this gap by exploring the fundamental limits of coverage based on stochastic data fusion models that fuse noisy measurements of multiple sensors. We derive the scaling laws between coverage, network density, and signal-to-noise ratio (SNR). We show that data fusion can significantly improve sensing coverage by exploiting the collaboration among sensors. In particular, for signal path loss exponent of k (typically between 2.0 and 5.0), ρf = O(ρ 1−1/k d), where ρf and ρd are the densities of uniformly deployed sensors that achieve full coverage under the fusion and disc models, respectively. Our results help understand the limitations of the previous analytical results based on the disc model and provide key insights into the design of WSNs that adopt data fusion algorithms. Our analyses are verified through extensive simulations based on both synthetic data sets and data traces collected in a real deployment for vehicle detection.
A cellular learning automata based clustering algorithm for wireless sensor networks
- Sensor Letters
, 2008
"... In the first part of this paper, we propose a generalization of cellular learning automata (CLA) called irregular cellular learning automata (ICLA) which removes the restriction of rectangular grid structure in traditional CLA. In the second part of the paper, based on the proposed model a new clust ..."
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Cited by 24 (13 self)
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In the first part of this paper, we propose a generalization of cellular learning automata (CLA) called irregular cellular learning automata (ICLA) which removes the restriction of rectangular grid structure in traditional CLA. In the second part of the paper, based on the proposed model a new clustering algorithm for sensor networks is designed. The proposed clustering algorithm is fully distributed and the nodes in the network don't need to be fully synchronized with each other. The proposed clustering algorithm consists of two phases; initial clustering and reclustering. Unlike existing methods in which the reclustering phase is performed periodically on the entire network, reclustering phase in the proposed method is performed locally whenever it is needed. This results in a reduction in the consumed energy for reclustering phase and also allows reclustering phase to be performed as the network operates. The proposed clustering method in comparison to existing methods produces a clustering in which each cluster has higher number of nodes and higher residual energy for the cluster head. Local reclustering, higher residual energy in cluster heads and higher number of nodes in each cluster results in a network with longer lifetime. To evaluate the performance of the proposed algorithm several experiments have been conducted. The results of experiments have shown that the proposed clustering algorithm outperforms existing clustering methods in terms of quality of clustering measured by the total number of clusters, the number of sparse clusters and the remaining energy level of the cluster heads. Experiments have also shown that the proposed clustering algorithm in comparison to other existing methods prolongs the network lifetime.
Communication and Coordination in Wireless Sensor and Actor Networks
- IEEE Transactions on Mobile Computing 2007
"... Abstract — In this paper, coordination and communication ..."
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Cited by 24 (3 self)
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Abstract — In this paper, coordination and communication
Distributed Object Tracking Using a Cluster-Based Kalman Filter in Wireless Camera Networks
, 2008
"... Local data aggregation is an effective means to save sensor node energy and prolong the lifespan of wireless sensor networks. However, when a sensor network is used to track moving objects, the task of local data aggregation in the network presents a new set of challenges such as the necessity to es ..."
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Cited by 23 (4 self)
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Local data aggregation is an effective means to save sensor node energy and prolong the lifespan of wireless sensor networks. However, when a sensor network is used to track moving objects, the task of local data aggregation in the network presents a new set of challenges such as the necessity to estimate, usually in real-time, the constantly-changing state of the target based on information acquired by the nodes at different time instants. To address these issues, we propose a distributed object tracking system which employs a cluster-based Kalman filter in a network of wireless cameras. When a target is detected, cameras that can observe the same target interact with one another to form a cluster and elect a cluster head. Local measurements of the target acquired by members of the cluster are sent to the cluster head, which then estimates the target position via Kalman filtering and periodically transmits this information to
Tracking Targets with Quality in Wireless Sensor Networks
"... Tracking of moving targets has attracted more and more attention due to its importance in utilizing sensor networks for surveillance. In this paper, we consider the issue of how to track mobile targets with certain level of quality of monitoring (QoM), while conserving power. We address the target t ..."
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Cited by 22 (0 self)
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Tracking of moving targets has attracted more and more attention due to its importance in utilizing sensor networks for surveillance. In this paper, we consider the issue of how to track mobile targets with certain level of quality of monitoring (QoM), while conserving power. We address the target tracking problem by taking into account of both the coverage and the QoM. In particular, QoM ensures that the probability of reporting inaccurate monitoring information (such as false alarm or target miss) should be as small as possible, even in the presence of noises and signal attenuation. We also analytically whether or not the detection/observation made by a single sensor suffices to tracking the target in a reasonably populated sensor network. Our finding gives a confirmative answer and challenges the long-held paradigm that high tracking quality (low tracking error) necessarily requires high power consumption. To rigorously analyze the impact of target movement on QoM, we derive both lower and upper bounds on the number of sensors (called duty sensors) required to keep track of a moving target. Based on the analysis, we have devised a cooperative, relay-area-based scheme that determines which sensor should become the next duty sensor when the target is moving. The simulation study indicates that the number of duty sensor required in the proposed scheme is, in the worst case, approximately 1.2 times larger than the lower bound. It also indicates that a trade-off exists among QoM, the number of duty sensors required, and the load balance.
A Hierarchical Energy-Efficient Framework for Data Aggregation in Wireless Sensor Networks
- Journal of Pragmatics
, 2001
"... A network of sensors can be used to obtain statebased data from the area in which they are deployed. To reduce costs, the data, sent via intermediate sensors to a sink, is often aggregated (or compressed). This compression is done by a subset of the sensors called aggregators. Since sensors are usua ..."
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Cited by 17 (0 self)
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A network of sensors can be used to obtain statebased data from the area in which they are deployed. To reduce costs, the data, sent via intermediate sensors to a sink, is often aggregated (or compressed). This compression is done by a subset of the sensors called aggregators. Since sensors are usually equipped with small and unreplenishable energy reserves, a critical issue is to strategically deploy an appropriate number of aggregators so as to minimize the amount of energy consumed by transporting and aggregating the data.
1 Exploiting Reactive Mobility for Collaborative Target Detection in Wireless Sensor Networks
"... Abstract—Recent years have witnessed the deployments of wireless sensor networks in a class of mission-critical applications such as object detection and tracking. These applications often impose stringent Quality of Service (QoS) requirements including high detection probability, low false alarm ra ..."
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Cited by 12 (7 self)
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Abstract—Recent years have witnessed the deployments of wireless sensor networks in a class of mission-critical applications such as object detection and tracking. These applications often impose stringent Quality of Service (QoS) requirements including high detection probability, low false alarm rate and bounded detection delay. Although a dense all-static network may initially meet these QoS requirements, it does not adapt to unpredictable dynamics in network conditions (e.g., coverage holes caused by death of nodes) or physical environments (e.g., changed spatial distribution of events). This paper exploits reactive mobility to improve the target detection performance of wireless sensor networks. In our approach, mobile sensors collaborate with static sensors and move reactively to achieve the required detection performance. Specifically, mobile sensors initially remain stationary and are directed to move toward a possible target only when a detection consensus is reached by a group of sensors. The accuracy of final detection result is then improved as the measurements of mobile sensors have higher Signal-to-Noise Ratios after the movement. We develop a sensor movement scheduling algorithm that achieves near-optimal system detection performance under a given detection delay bound. The effectiveness of our approach is validated by extensive simulations using the real data traces collected by 23 sensor nodes. Index Terms—Data fusion, Algorithm/protocol design and analysis, Wireless sensor networks. 1
