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Finding aggregation tree with genetic algorithm for network correlated data gathering

Habibi Masouleh, H ; Sharif University of Technology | 2008

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  1. Type of Document: Article
  2. DOI: 10.1109/SENSORCOMM.2008.19
  3. Publisher: 2008
  4. Abstract:
  5. The critical issue in designing correlated data networks like Wireless Sensor Networks is to minimize the total cost of data transmission in the network, and decrease the amount of data flow. The problem of finding optimal aggregation tree for correlated data gathering in single sink network is considered as an NP-Complete problem and hence heuristic methods are usually applied to solve it[1]. In this paper, we apply genetic algorithm (GA) to solve the problem. In our method, we improve the performance of genetic search by selecting proper initial population. This initial population is determined in two ways, by using Prime's algorithm, and shortest path tree. The main issue is to regard nodes individually, and predict their behavior in global optimum. We use Node-Based genetic algorithm for building the communication tree to improve the encoding and decoding process, locality and heritability versus typical GAs. © 2008 IEEE
  6. Keywords:
  7. Agglomeration ; Algorithms ; Decoding ; Diesel engines ; Genetic algorithms ; Graph theory ; Heuristic methods ; Hybrid sensors ; Information theory ; Mobile telecommunication systems ; Nuclear propulsion ; Population statistics ; Problem solving ; Sensor networks ; Sensors ; Topology ; Trees (mathematics) ; Wireless telecommunication systems ; Communication trees ; Complete problems ; Correlated data gatherings ; Correlated datums ; Critical issues ; Data flows ; Data transmissions ; Decoding processes ; Genetic searches ; Global optimums ; Initial populations ; NP-complete problem ; Shortest path tree ; Shortest Path trees ; Single sinks ; Total costs ; Two ways ; Wireless sensor networks
  8. Source: 2nd International Conference on Sensor Technologies and Applications, SENSORCOMM 2008, Cap Esterel, 25 August 2008 through 31 August 2008 ; 2008 , Pages 429-434 ; 9780769533308 (ISBN)
  9. URL: https://www.scopus.com/inward/record.uri?eid=2-s2.0-55849107798&doi=10.1109%2fSENSORCOMM.2008.19&partnerID=40&md5=a867090201b5ce9c3ec5072f482c83f8