Wireless Sensor Node Placement Using Hybrid Genetic Programming and Genetic Algorithms

Created by W.Langdon from gp-bibliography.bib Revision:1.4340

  author =       "Arpit Tripathi and Pulkit Gupta and Aditya Trivedi and 
                 Rahul Kala",
  title =        "Wireless Sensor Node Placement Using Hybrid Genetic
                 Programming and Genetic Algorithms",
  journal =      "International Journal of Intelligent Information
  year =         "2011",
  number =       "2",
  volume =       "7",
  pages =        "63--83",
  keywords =     "genetic algorithms, genetic programming",
  ISSN =         "1548-3657",
  URL =          "http://www.igi-global.com/article/wireless-sensor-node-placement-using/54067",
  DOI =          "doi:10.4018/jiit.2011040104",
  abstract =     "The ease of use and re-configuration in a wireless
                 network has played a key role in their widespread
                 growth. The node deployment problem deals with an
                 optimal placement strategy of the wireless nodes. This
                 paper models a wireless sensor network, consisting of a
                 number of nodes, and a unique sink to which all the
                 information is transmitted using the shortest
                 connecting path. Traditionally the systems have used
                 Genetic Algorithms for optimal placement of the nodes
                 that usually fail to give results in problems employing
                 large numbers of nodes or higher areas to be covered.
                 This paper proposes a hybrid Genetic Programming (GP)
                 and Genetic Algorithm (GA) for solving the problem.
                 While the GP optimises the deployment structure, the GA
                 is used for actual node placement as per the GP
                 optimised structure. The GA serves as a slave and GP
                 serves as master in this hierarchical implementation.
                 The algorithm optimises total coverage area, energy ,
                 lifetime of the network, and the number of nodes
                 deployed. Experimental results show that the algorithm
                 could place the sensor nodes in a variety of scenarios.
                 The placement was found to be better than random
                 placement strategy as well as the Genetic Algorithm
                 placement strategy.",
  notes =        "Indian Institute of Information Technology and
                 Management Gwalior, India",
  bibdate =      "2011-05-27",
  bibsource =    "DBLP,

Genetic Programming entries for Arpit Tripathi Pulkit Gupta Aditya Trivedi Rahul Kala