Improvement of handoff in wireless networks using mobility prediction and multicasting techniques

  • Authors:

  • Suresh Venkatachalaiah;Richard J. Harris;Robert Suryasaputra

  • Affiliations:

  • Centre for Advanced Technology in Telecommunications, School of Electrical and Computer Engineering, RMIT University, Melbourne, Victoria, Australia;Centre for Advanced Technology in Telecommunications, School of Electrical and Computer Engineering, RMIT University, Melbourne, Victoria, Australia;Centre for Advanced Technology in Telecommunications, School of Electrical and Computer Engineering, RMIT University, Melbourne, Victoria, Australia

  • Venue:
  • EHAC'05 Proceedings of the 4th WSEAS International Conference on Electronics, Hardware, Wireless and Optical Communications
  • Year:
  • 2005

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Abstract

Achieving seamless mobility is a significant challenge for wireless networking today. This paper illustrates the use of multicasting techniques aided by mobility prediction to improve handoff performance in wireless networks. Handoff holds the key to defining the performance of wireless networks since there could be packet losses during handoff as the mobile node moves from one point of attachment to another. A new method of determining a multicast tree routing scheme with specific performance objectives is presented in this paper. The Grey model has been used as the prediction methodology as it has been shown to provide good prediction accuracy[1]. A situation is modelled where a multicast tree is defined covering multiple access routers (AR) to maintain connectivity with the mobile node using mobility prediction (by selecting the least number of access routers) whilst ensuring guarantees of bandwidth and minimum hop count such that packet loss can be avoided. To simultaneously solve the above two problem formulations gives rise to a multi-objective optimisation problem. Discovering the optimal routing is an NP hard problem where network state information is not accurate, which is a common feature in wireless networks. After describing the problem, an algorithm that satisfies the constraints and objectives with a near optimal cost is presented.