Reducing Data Aggregation Latency by Using Partially Overlapped Channels in Sensor Networks

Existing works on data aggregation in sensor networks usually use a single channel, which results in a long latency due to high interference, especially in high-density networks. In this paper, we present a novel approach to minimize the latency of data aggregation by using partially overlapped channels. We first propose a joint tree construction, channel assignment and scheduling algorithm for this problem. The basic idea is to select a parent and assign a feasible channel to each node such that it can be scheduled in a timeslot that has been used by other nodes, meanwhile leaving unconsidered nodes more chances to avoid conflicts. Next, we give a distributed implementation of this joint scheme. Finally, we compare the performance of our algorithm with two heuristic algorithms that solve this problem in three separate steps, and another multi-channel protocol that only considers orthogonal channels in sensor networks. Simulation results demonstrate that our joint scheme can significantly reduce the data aggregation latency, especially in high-density sensor networks. To our best knowledge, this is the first work in the literature that minimizes the data aggregation latency by using partially overlapped channels.