Spatiotemporal Adaptive Optimization of a Static-Sensor Network via a Non-Parametric Estimation of Target Location Likelihood

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

doi:10.1109/jsen.2016.2638623

Cited by 21 publications

(24 citation statements)

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“…We use ( , ) to represent the unknown node as shown in Figure 2; three anchor nodes within the communication radius of are 1 The exact Euclidean distances without any noise between and 1 , and 2 , and 3 are 1 , 2 , and 3 , respectively. The coordinates of can be obtained by solving (1).…”

Section: Mathematical Model Of Trilateration Localizationmentioning

confidence: 99%

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A Distributed Anchor Node Selection Algorithm Based on Error Analysis for Trilateration Localization

Fan

Liu

2018

Mathematical Problems in Engineering

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This paper proposes a distributed anchor node selection algorithm based on error analysis for trilateration localization (EATL). The influence of distance measurement error on localization accuracy is discussed from two aspects: condition number of triangle formed by the three anchor nodes and the relative position between the unknown node and the three anchor nodes. Based on the error analysis, three principles for optimizing the selection of anchor nodes are given and then an algorithm for selecting anchor nodes on the ring is proposed.

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Section: Mathematical Model Of Trilateration Localizationmentioning

confidence: 99%

“…In a distributed sensor network, for most applications, such as target tracking, environmental monitoring [1], the geographical information of sensor nodes needs to be known. Estimation of node position is a fundamental requirement in distributed sensor networks.…”

Section: Introductionmentioning

confidence: 99%

A Distributed Anchor Node Selection Algorithm Based on Error Analysis for Trilateration Localization

Fan

Liu

2018

Mathematical Problems in Engineering

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“…8 They have been suggested for a variety of application areas, such as dynamic wireless sensor networks (WSNs) with mobile nodes, 9,10 micro-manufacturing with small scale robots 11 , and minimally invasive medical treatments. 12,13 Future applications also include urban [14][15][16][17][18] and wilderness [19][20][21][22][23] search and rescue, and surveillance [24][25][26][27][28][29] .…”

Section: Introductionmentioning

confidence: 99%

Design and implementation of a millirobot for swarm studies –mROBerTO

Kim¹,

Kashino²,

Colaco³

et al. 2018

Robotica

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SUMMARYThe use of millirobots, particularly in swarm studies, would enable researchers to verify their proposed autonomous cooperative behavior algorithms under realistic conditions with a large number of agents. While multiple designs for such robots have been proposed, they, typically, require custom-made components, which make replication and manufacturing difficult, and, mostly, employ non-modular integral designs. Furthermore, these robots' proposed small sizes tend to limit sensory perception capabilities and operational time. Some have resolved few of the above issues through the use of extensions that, unfortunately, add to their size.In contribution to the pertinent field, thus, a novel millirobot with an open-source design, addressing the above concerns, is presented in this paper. Our proposed millirobot has a modular design and uses easy to source, off-the-shelf components. Themilli-robot-Toronto (mROBerTO) also includes a variety of sensors and has a 16 × 16 mm2footprint.mROBerTO's wireless communication capabilities include ANT™, Bluetooth Smart, or both simultaneously. Data-processing is handled by an ARM processor with 256 KB of flash memory. Additionally, the sensing modules allow for extending or changing the robot's perception capabilities without adding to the robot's size. For example, the swarm-sensing module, designed to facilitate swarm studies, allows for measuring proximity and bearing to neighboring robots and performing local communications.Extensive experiments, some of which are presented herein, have illustrated the capability ofmROBerTOunits for use in implementing a variety of commonly proposed swarm algorithms.

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“…PSO and time cumulative probability curves estimated the best position of the sensors. The Monte Carlo simulation proposed in [13] for the deployment of static sensors. The residual energy concept used to detect the coverage holes in [14] and evaluated the life expectancy of processing nodes.…”

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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Spatiotemporal Adaptive Optimization of a Static-Sensor Network via a Non-Parametric Estimation of Target Location Likelihood

Cited by 21 publications

References 50 publications

A Distributed Anchor Node Selection Algorithm Based on Error Analysis for Trilateration Localization

A Distributed Anchor Node Selection Algorithm Based on Error Analysis for Trilateration Localization

Design and implementation of a millirobot for swarm studies –mROBerTO

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

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