An adaptive static-sensor network deployment strategy for detecting mobile targets

Kashino, Zendai; Vilela, Julio; Kim, Justin Y.; Nejat, Goldie; Benhabib, B.

doi:10.1109/ssrr.2016.7784269

Cited by 7 publications

(7 citation statements)

References 30 publications

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“…However, these often do not consider dynamic scenarios where the target-location likelihood function can change over time, nor do they consider an expanding search area. A modified approach, that deals with these issues, is presented in [41] where a static-sensor network is deployed in a time-phased manner allowing for re-planning if new information on the target were to become available. Others consider only mobile search resources [42][43][44].…”

Section: Introductionmentioning

confidence: 99%

“…This paper, thus, considers the problem of deployment planning of sparse DSNs that comprise only static sensors, with the goal of maximizing the probability of detecting a mobile-target in a time-expanding RoI. While our own and others' earlier works have addressed the mobile-target detection problem using dynamically deployed sensor networks, these have only been for omnidirectional sensors, [41,[68][69][70][71]. As this work considers directional sensors, a new solution approach is required.…”

Section: Introductionmentioning

confidence: 99%

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Directional-Sensor Network Deployment Planning for Mobile-Target Search

et al. 2020

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In this paper, a novel time-phased directional-sensor network deployment strategy is presented for the mobile-target search problem, e.g., wilderness search and rescue (WiSAR). The proposed strategy uses probabilistic target-motion models combined with a variation of a standard direct search algorithm to plan the optimal locations of directional-sensors which maximize the likelihood of target detection. A linear sensing model is employed as a simplification for directional-sensor network deployment planning, while considering physical constraints, such as on-time sensor deliverability. Extensive statistical simulations validated our method. One such illustrative experiment is included herein to demonstrate the method’s operation. A comparative study was also carried out, whose summary is included in this paper, to highlight the tangible improvement of our approach versus three traditional deployment strategies: a uniform, a random, and a ring-of-fire type deployment, respectively.

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Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Directional-Sensor Network Deployment Planning for Mobile-Target Search

et al. 2020

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“…Small robots, typically categorized as milli-, micro-or nanorobots, [1][2][3] have applications in a variety of areas. Some applications include use in surveillance, [4][5][6] wilderness search and rescue, [7][8][9][10][11] urban search and rescue, [12][13][14] medicine, 15,16 micro-assembly 17 and wireless sensor networks. 18,19 In the work of Kashino et al, 7,8 a millirobot was used to perform wilderness search and rescue experiments in a scaled-down lab environment.…”

Section: Introductionmentioning

confidence: 99%

A high-performance millirobot for swarm-behaviour studies: Swarm-topology estimation

Kim

Kashino

Pineros

et al. 2019

International Journal of Advanced Robotic Systems

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In this article, we present a novel high-performance millirobot ( milli- robot- Toronto), designed to allow for the testing of complex swarm-behaviours, including human–swarm interaction. milli- robot- Toronto, built only with off-the-shelf components, has locomotion, processing and sensing capabilities that significantly improve upon existing designs, while maintaining one of the smallest footprints among current millirobots. As complementary software to this hardware development, herein, we also present a new global swarm-topology estimation algorithm. The method is novel in that it uniquely fuses incomplete location data collected by the individual robots in a distributed manner to optimally estimate the topology of the overall swarm using a centralized computer. It is a generalized technique usable by any swarm comprising robots capable of collecting location estimates of neighbouring robots. Numerous experiments, evaluating the performance of milli- robot- Toronto and the proposed optimal swarm-topology estimation algorithm, are also included.

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“…In [12], the deployment of static sensor scheme was proposed for the identification of an unseen target in free space condition. PSO and time cumulative probability curves estimated the best position of the sensors.…”

Section: Introductionmentioning

confidence: 99%

A Swarm Intelligence Based Coverage Hole Healing Approach for Wireless Sensor Networks

Mehta¹,

Malik

2018

ICST Transactions on Scalable Information Systems

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In the WSN network, nodes are always deprived of battery and can't be in operation for a long time. Some nodes may die sooner than others, creating a void in the area. Our work in this article covered this hole area efficiently with the introduction of a novel hybrid optimization algorithm. Our algorithm makes the locally converging particle swarm optimization to a global optimization algorithm by ensembling it with the Gravitational search algorithm. The perturbation in this algorithm is also reduced by logistic chaotic mapping. The current solution has detected the holes and healed them with additional redundant nodes. The Delaunay triangulation method detected the number of holes with the improved algebraic approach. The results are validated by extensive experimentation with different sensing range and node's density. Our method seems to perform well than state of the art schemes. We have also shown that increasing the redundant nodes, more coverage can be achieved.

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An adaptive static-sensor network deployment strategy for detecting mobile targets

Cited by 7 publications

References 30 publications

Directional-Sensor Network Deployment Planning for Mobile-Target Search

Directional-Sensor Network Deployment Planning for Mobile-Target Search

A high-performance millirobot for swarm-behaviour studies: Swarm-topology estimation

A Swarm Intelligence Based Coverage Hole Healing Approach for Wireless Sensor Networks

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