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ALOE: autonomic locating of obstructing entities in MANETs

  • Mohamed Belhassen
  • Amine Dhraief
  • Abdelfettah Belghith
  • Hassan Mathkour
Original Research
  • 45 Downloads

Abstract

The presence of communication obstructing entities severely degrades the routing efficiency in Mobile Ad hoc Networks (MANET). While several routing protocols have proposed ways to mitigate this degradation essentially by getting around and avoiding obstacles, the detection of obstacle locations and their contours driven by the very signaling of the underlying routing protocol has not been given that much attention. Yet, the integration of such a capability within the routing protocol presents an adequate leverage of the routing efficiency. In this paper, we first propose a distributed autonomic scheme to locate obstructing entities (coined ALOE) within a MANET using the basic signaling of the Cartography Enhanced Optimized Link State routing (CE-OLSR) protocol. Then, we propose ALOE-CE-OLSR the integration of ALOE within CE-OLSR, and show the resulting improvement in routing validity as compared to that of both CE-OLSR with and without a priori knowledge of the obstructing entities map. While the routing validity measures the pertinence of the routes computed by the underlying routing protocol, two new metrics, namely the coverage and the precision ratios, are defined to properly evaluate the efficiency and performance of our proposed detection scheme, in addition to the throughput as well as the end-to-end delay of received data packets. Simulation results show that our proposed CE-OLSR signaling-based obstacle detection scheme accurately localizes and detects the boundaries of the stationary obstructing obstacles. The integrated ALOE-CE-OLSR achieves the same route validity, throughput and delay as CE-OLSR with a priori precise knowledge of the obstacles map.

Keywords

Multiple obstructing entities Contour detection MANETs Routing improvement 

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Copyright information

© Springer-Verlag GmbH Germany, part of Springer Nature 2018

Authors and Affiliations

  1. 1.HANA Research laboratoryUniversity of ManoubaManoubaTunisia
  2. 2.College of Computer and Information SciencesKind Saud UniversityRiyadhSaudi Arabia

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