Optimal Sensor Deployment for Wireless Surveillance Sensor Networks by a Hybrid Steady-State Genetic Algorithm
- Authors
- Seo, Jae-Hyun; Kim, Yong-Hyuk; Ryou, Hwang-Bin; Cha, Si-Ho; Jo, Minho
- Issue Date
- 11월-2008
- Publisher
- IEICE-INST ELECTRONICS INFORMATION COMMUNICATIONS ENG
- Keywords
- wireless sensor networks; surveillance sensor deployment; hybrid steady-state genetic algorithm
- Citation
- IEICE TRANSACTIONS ON COMMUNICATIONS, v.E91B, no.11, pp.3534 - 3543
- Indexed
- SCIE
SCOPUS
- Journal Title
- IEICE TRANSACTIONS ON COMMUNICATIONS
- Volume
- E91B
- Number
- 11
- Start Page
- 3534
- End Page
- 3543
- URI
- https://scholar.korea.ac.kr/handle/2021.sw.korea/122508
- DOI
- 10.1093/ietcom/e91-b.11.3534
- ISSN
- 0916-8516
- Abstract
- An important objective of surveillance sensor networks is to effectively monitor the environment. and detect, localize, and classify targets of interest. The optimal sensor placement enables us to minimize manpower and time. to acquire accurate information on target situation and movement, and to rapidly change tactics in the dynamic field. Most of previous researches regarding the sensor deployment have been conducted without considering practical input factors. Thus in this paper, we apply more real-world input factors such as sensor capabilities, terrain features. target identification, and direction of target movements to the sensor placement problem. We propose a novel and efficient hybrid steady-state genetic algorithm giving low computational overhead as well as optimal sensor placement for enhancing surveillance capability to monitor and locate target vehicles. The proposed algorithm introduces new two-dimensional geographic crossover and mutation. By using a new simulator adopting the proposed genetic algorithm developed in this paper, we demonstrate successful applications to the wireless real-world surveillance sensor placement problem giving very high detection and classification rates. 97.5% and 87.4%, respectively.
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Collections - Graduate School > Department of Computer and Information Science > 1. Journal Articles
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