A Dynamic Approach to Sensor Network Deployment for Mobile-Target Detection in Unstructured, Expanding Search Areas. Vilela, J., Kashino, Z., Ly, R., Nejat, G., & Benhabib, B. IEEE Sensors Journal, 16(11):4405–4417, June, 2016. doi abstract bibtex This paper proposes a novel strategy for the deployment of a static-sensor network based on the use of a target-motion probability model. The focus is on the real-time dynamic and optimal deployment of the network for detecting untrackable targets. The dynamic nature of the deployment refers to the on-line reconfigurability of the network as real-time information about the target becomes available. The optimal locations of the network nodes, in turn, are determined based on maximizing the probability of finding the target through the use of iso-cumulative-probability curves. The proposed strategy is adaptable to unstructured environments with natural terrain variation and the presence of obstacles. Extensive simulations, some of which are included in this paper, verified the advantage of our deployment strategy over other existing methods. Namely, the proposed strategy can tangibly increase the success rate of target detection, while reducing the mean detection time, when compared with uniform-coverage-based approaches that do not consider probabilistic target-motion modeling. A comprehensive example is also included, herein, to illustrate the successful application of our proposed deployment strategy to a wilderness search and rescue scenario, where both static and mobile sensors are employed within a hybrid sensor-deployment strategy.
@article{vilela_dynamic_2016,
title = {A {Dynamic} {Approach} to {Sensor} {Network} {Deployment} for {Mobile}-{Target} {Detection} in {Unstructured}, {Expanding} {Search} {Areas}},
volume = {16},
copyright = {All rights reserved},
issn = {1530-437X},
doi = {10.1109/JSEN.2016.2537331},
abstract = {This paper proposes a novel strategy for the deployment of a static-sensor network based on the use of a target-motion probability model. The focus is on the real-time dynamic and optimal deployment of the network for detecting untrackable targets. The dynamic nature of the deployment refers to the on-line reconfigurability of the network as real-time information about the target becomes available. The optimal locations of the network nodes, in turn, are determined based on maximizing the probability of finding the target through the use of iso-cumulative-probability curves. The proposed strategy is adaptable to unstructured environments with natural terrain variation and the presence of obstacles. Extensive simulations, some of which are included in this paper, verified the advantage of our deployment strategy over other existing methods. Namely, the proposed strategy can tangibly increase the success rate of target detection, while reducing the mean detection time, when compared with uniform-coverage-based approaches that do not consider probabilistic target-motion modeling. A comprehensive example is also included, herein, to illustrate the successful application of our proposed deployment strategy to a wilderness search and rescue scenario, where both static and mobile sensors are employed within a hybrid sensor-deployment strategy.},
number = {11},
journal = {IEEE Sensors Journal},
author = {Vilela, J. and Kashino, Z. and Ly, R. and Nejat, G. and Benhabib, B.},
month = jun,
year = {2016},
keywords = {Genetic algorithms, Mobile communication, Object detection, Probabilistic logic, Real-time systems, Sensor phenomena and characterization, Sensors, Static-sensor networks, Target tracking, dynamic approach, expanding search areas, genetic algorithms, mobile-target detection, natural terrain variation, object detection, on-line reconfigurability, optimal deployment, real-time systems, sensor network deployment, sensors, static-sensor network, unstructured search areas},
pages = {4405--4417},
}
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The optimal locations of the network nodes, in turn, are determined based on maximizing the probability of finding the target through the use of iso-cumulative-probability curves. The proposed strategy is adaptable to unstructured environments with natural terrain variation and the presence of obstacles. Extensive simulations, some of which are included in this paper, verified the advantage of our deployment strategy over other existing methods. Namely, the proposed strategy can tangibly increase the success rate of target detection, while reducing the mean detection time, when compared with uniform-coverage-based approaches that do not consider probabilistic target-motion modeling. 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