Mobility-Assisted Relocation for Self-Deployment in Wireless Sensor Networks

Xiaoling WU; Jinsung CHO; Brian J. D'AURIOL; Sungyoung LEE

doi:10.1093/ietcom/e90-b.8.2056

Mobility-Assisted Relocation for Self-Deployment in Wireless Sensor Networks

Xiaoling WU, Jinsung CHO, Brian J. D'AURIOL, Sungyoung LEE

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Sensor network deployment is very challenging due to the hostile and unpredictable nature of environments. The field coverage of wireless sensor networks (WSNs) can be enhanced and consequently network lifetime can be prolonged by optimizing the sensor deployment with a finite number of mobile sensors. In this paper, we introduce a comprehensive taxonomy for WSN self-deployment in which three sensor relocation algorithms are proposed to match the mobility degree of sensor nodes, particle swarm optimization based algorithm (PSOA), relay shift based algorithm (RSBA) and energy efficient fuzzy optimization algorithm (EFOA). PSOA regards the sensors in the network as a swarm, and reorganizes the sensors by the particle swarm optimization (PSO) algorithm, in the full sensor mobility case. RSBA and EFOA assume relatively limited sensor mobility, i.e., the movement distance is bounded by a threshold, to further reduce energy consumption. In the zero mobility case, static topology control or scheduling schemes can be used such as optimal cluster formation. Simulation results show that our approaches greatly improve the network coverage as well as energy efficiency compared with related works.

Publication: IEICE TRANSACTIONS on Communications Vol.E90-B No.8 pp.2056-2069

Publication Date: 2007/08/01

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Online ISSN: 1745-1345

DOI: 10.1093/ietcom/e90-b.8.2056

Type of Manuscript: PAPER

Category: Network

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Xiaoling WU, Jinsung CHO, Brian J. D'AURIOL, Sungyoung LEE, "Mobility-Assisted Relocation for Self-Deployment in Wireless Sensor Networks" in IEICE TRANSACTIONS on Communications, vol. E90-B, no. 8, pp. 2056-2069, August 2007, doi: 10.1093/ietcom/e90-b.8.2056.
Abstract: Sensor network deployment is very challenging due to the hostile and unpredictable nature of environments. The field coverage of wireless sensor networks (WSNs) can be enhanced and consequently network lifetime can be prolonged by optimizing the sensor deployment with a finite number of mobile sensors. In this paper, we introduce a comprehensive taxonomy for WSN self-deployment in which three sensor relocation algorithms are proposed to match the mobility degree of sensor nodes, particle swarm optimization based algorithm (PSOA), relay shift based algorithm (RSBA) and energy efficient fuzzy optimization algorithm (EFOA). PSOA regards the sensors in the network as a swarm, and reorganizes the sensors by the particle swarm optimization (PSO) algorithm, in the full sensor mobility case. RSBA and EFOA assume relatively limited sensor mobility, i.e., the movement distance is bounded by a threshold, to further reduce energy consumption. In the zero mobility case, static topology control or scheduling schemes can be used such as optimal cluster formation. Simulation results show that our approaches greatly improve the network coverage as well as energy efficiency compared with related works.
URL: https://globals.ieice.org/en_transactions/communications/10.1093/ietcom/e90-b.8.2056/_p

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@ARTICLE{e90-b_8_2056,
author={Xiaoling WU, Jinsung CHO, Brian J. D'AURIOL, Sungyoung LEE, },
journal={IEICE TRANSACTIONS on Communications},
title={Mobility-Assisted Relocation for Self-Deployment in Wireless Sensor Networks},
year={2007},
volume={E90-B},
number={8},
pages={2056-2069},
abstract={Sensor network deployment is very challenging due to the hostile and unpredictable nature of environments. The field coverage of wireless sensor networks (WSNs) can be enhanced and consequently network lifetime can be prolonged by optimizing the sensor deployment with a finite number of mobile sensors. In this paper, we introduce a comprehensive taxonomy for WSN self-deployment in which three sensor relocation algorithms are proposed to match the mobility degree of sensor nodes, particle swarm optimization based algorithm (PSOA), relay shift based algorithm (RSBA) and energy efficient fuzzy optimization algorithm (EFOA). PSOA regards the sensors in the network as a swarm, and reorganizes the sensors by the particle swarm optimization (PSO) algorithm, in the full sensor mobility case. RSBA and EFOA assume relatively limited sensor mobility, i.e., the movement distance is bounded by a threshold, to further reduce energy consumption. In the zero mobility case, static topology control or scheduling schemes can be used such as optimal cluster formation. Simulation results show that our approaches greatly improve the network coverage as well as energy efficiency compared with related works.},
keywords={},
doi={10.1093/ietcom/e90-b.8.2056},
ISSN={1745-1345},
month={August},}

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TY - JOUR
TI - Mobility-Assisted Relocation for Self-Deployment in Wireless Sensor Networks
T2 - IEICE TRANSACTIONS on Communications
SP - 2056
EP - 2069
AU - Xiaoling WU
AU - Jinsung CHO
AU - Brian J. D'AURIOL
AU - Sungyoung LEE
PY - 2007
DO - 10.1093/ietcom/e90-b.8.2056
JO - IEICE TRANSACTIONS on Communications
SN - 1745-1345
VL - E90-B
IS - 8
JA - IEICE TRANSACTIONS on Communications
Y1 - August 2007
AB - Sensor network deployment is very challenging due to the hostile and unpredictable nature of environments. The field coverage of wireless sensor networks (WSNs) can be enhanced and consequently network lifetime can be prolonged by optimizing the sensor deployment with a finite number of mobile sensors. In this paper, we introduce a comprehensive taxonomy for WSN self-deployment in which three sensor relocation algorithms are proposed to match the mobility degree of sensor nodes, particle swarm optimization based algorithm (PSOA), relay shift based algorithm (RSBA) and energy efficient fuzzy optimization algorithm (EFOA). PSOA regards the sensors in the network as a swarm, and reorganizes the sensors by the particle swarm optimization (PSO) algorithm, in the full sensor mobility case. RSBA and EFOA assume relatively limited sensor mobility, i.e., the movement distance is bounded by a threshold, to further reduce energy consumption. In the zero mobility case, static topology control or scheduling schemes can be used such as optimal cluster formation. Simulation results show that our approaches greatly improve the network coverage as well as energy efficiency compared with related works.
ER -