Energy-efficient geographic multicast routing for Sensor and Actuator Networks

  • Authors:

  • Juan A. Sanchez;Pedro M. Ruiz;Ivan Stojmenovic

  • Affiliations:

  • Department of Communications and Information Engineering, University of Murcia, Espinardo, 30071 Murcia, Spain;Department of Communications and Information Engineering, University of Murcia, Espinardo, 30071 Murcia, Spain;SITE, University of Ottawa, Ottawa, ON KIN 6NR, Canada

  • Venue:
  • Computer Communications
  • Year:
  • 2007

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Abstract

The case in which the same information or events need to be sent from a single sensor node to multiple actuator nodes, is very common in many applications of Sensor and Actuator Networks (SANET). Sensors have very limited resources in terms of energy, bandwidth, and computational power. Thus, routing messages preserving energy and network bandwidth is a challenging requirement of paramount importance. In this paper we present a novel energy-efficient multicast routing protocol called GMREE which is specifically designed to achieve that goal. Our protocol builds multicast trees based on a greedy algorithm using local information. The heuristic we use is based in the concept of cost over progress metric and it is specially designed to minimize the total energy used by the multicast tree. GMREE incorporates a relay selection function which selects nodes from a node's neighborhood taking into account not only the minimization of the energy but also the number of relays selected. Nodes only select relays based on a locally built and energy-efficient underlying graph reduction such as Gabriel graph, enclosure graph or a local shortest path tree. Thus, the topology of the resulting multicast trees really takes advantage of the benefit of sending a single message to multiple destinations through the relays which provide best energy paths. Our simulation results show that our proposed protocol outperforms the traditional energy-efficient multiunicast routing over a variety of network densities and number of receivers. In addition, for dense networks, the performance approximates the one achieved using the centralized shortest weighted path tree (computed by Dijkstra's algorithm).