An approximation algorithm for conflict-aware broadcast scheduling in wireless ad hoc networks

Authors:
Reza Mahjourian;Feng Chen;Ravi Tiwari;My Thai;Hongqiang Zhai;Yuguang Fang
Affiliations:
University of Florida, Gainesville, FL, USA;University of Florida, Gainesville, FL, USA;University of Florida, Gainesville, FL, USA;University of Florida, Gainesville, FL, USA;Philips Research North America, Briarcliff Manor, NY, USA;University of Florida, Gainesville, FL, USA
Venue:
Proceedings of the 9th ACM international symposium on Mobile ad hoc networking and computing
Year:
2008

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Abstract

Broadcast scheduling is a fundamental problem in wireless ad hoc networks. The objective of a broadcast schedule is to deliver a message from a given source to all other nodes in a minimum amount of time. At the same time, in order for the broadcast to proceed as predicted in the schedule, it must not contain parallel transmissions which can be conflicting based on the collision and interference parameters in the wireless network. Most existing work on this problem use a limited network model which accounts only for conflicts occurring inside the transmission ranges of the nodes. The broadcast schedules produced by these algorithms are likely to experience unpredictable delays when deployed in the network. This is because they do not take into consideration other important sources of conflict in parallel transmissions, namely the interference range and the carrier sensing range. In this paper we develop a conflict-aware network model, which uses these parameters to increase the probability of scheduling conflict-free transmissions, and thereby improve the reliability of the broadcast schedule. We present and prove correctness of a constant approximation algorithm for minimum-latency broadcast scheduling under this network model. We also present a greedy heuristic algorithm for the same problem. Experimental results are provided to evaluate the performance of our algorithms. In addition, the algorithms are analyzed to justify their performance trends.