Path approximation for multi-hop wireless routing under application-based accuracy constraints

  • Authors:

  • Mustafa O. Kilavuz;Murat Yuksel

  • Affiliations:

  • Computer Science and Engineering Department, University of Nevada - Reno, Reno, NV 89557, USA;Computer Science and Engineering Department, University of Nevada - Reno, Reno, NV 89557, USA

  • Venue:
  • Computer Networks: The International Journal of Computer and Telecommunications Networking
  • Year:
  • 2012

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Abstract

Provisioning of rich routing building blocks to mobile ad hoc networking applications has been of high interest. Several MANET applications need flexibility in describing paths their traffic will follow. To accommodate this need, previous work has proposed several viable routing schemes such as Dynamic Source Routing (DSR) and Trajectory-Based Routing (TBR). However, tradeoffs involved in the interaction of these routing schemes and the application-specific requirements have not been explored. Especially, techniques to help the application to do the right routing choices are much needed. In this paper, we consider techniques that minimize routing protocol state costs under application-based constraints. We study the constraint of ''accuracy'' of the application's desired route, as this constraint provides a range of choices to the applications. As a crucial part of this concept, we investigate the tradeoff between the size of packet headers (needed to store end-to-end paths) and the network state (needed to store routing tables). We, then, apply the concept to the case of TBR with application-based accuracy constraints in obeying a given trajectory. We begin with simple discrete models to clarify the tradeoff between the packet header size and the network state. We show that the problem of accurate approximation of a trajectory (a.k.a. an application-specific end-to-end path) with the objective of minimizing the cost incurred due to header size and network state is difficult to solve optimally. We design an exhaustive search method as well as a genetic algorithm to find the optimum solution. We also develop heuristics solving this problem with smaller computational complexity and illustrate their performance. Finally, we explore ways of customizing our trajectory approximation framework for power-scarce or memory-scarce networking scenarios.