Some observations on using meta-heuristics for efficient location management in mobile computing networks

  • Authors:

  • Albert Y. Zomaya;Michael Haydock;Stephan Olariu

  • Affiliations:

  • School of Information Technologies, The University of Sydney, Sydney, NSW 2006, Australia;Department of Electrical and Electronic Engineering, The University of Western Australia, Nedlands Perth 6907, Australia;Department of Computer Science Old Dominion University Norfolk, VA

  • Venue:
  • Journal of Parallel and Distributed Computing - Special issue on wireless and mobile ad hoc networking and computing
  • Year:
  • 2003

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Abstract

With the increase in global wireless communication, there is a need for efficient network management strategies. Location management in a mobile network involves keeping track of mobile host (MH) cell locations. MHs perform location updates to inform the network of their location. When a call arrives for an MH, the network uses its last known cell location and a paging strategy to find that host. Current location management techniques do not consider host-mobility patterns or call arrival rates. This paper describes a selective update strategy that is modeled on the characteristics of a network, such as, topology, host mobility patterns and connection request rates. Then, a genetic algorithm is used to solve the location management problem that involves the search of a large solution space. The aim of the work is to determine whether genetic algorithms can be applied successfully to solve this problem, and to evaluate their efficiency in solving this class of optimization problems. Results from the selective update strategy show improvements over alternative algorithms. The location management optimization problem is shown to be well adapted to the workings of the genetic algorithm. The proposed solution also saves power, processing time, and network bandwidth.