An efficient data aggregation algorithm in delay tolerant vehicular networks

Abstract

We are interested in delay-constrained data aggregation in delay tolerant vehicular networks, in which contact opportunities are usually scarce and thus become critical resources. Data aggregation has been recognized as an effective technique for reducing communication cost while obtaining useful aggregated information. The unique characteristics of the network present great challenges to the delay-constrained aggregation issue. First, there is no always-connected path from a source node to a destination node. Second, there is an intrinsic tradeoff between communication cost and aggregation quality. In this paper we discuss the delay-constrained data aggregation problem in delay tolerant vehicular networks with the objective of maximizing the amount of information collected, which has not been well studied in the literature. We observe that there are heterogeneity and regularity with node contact patterns. Motivated by this observation, we propose an Efficient tree-based Data Aggregation algorithm called EDA. EDA first constructs a data aggregation tree based on the shortest path tree, and then assigns a waiting time budget to each node on the tree using a dynamic programming algorithm. We have performed extensive simulations on real taxi traces and results show that our EDA scheme incurs much lower transmission overhead while achieving the same performance compared with other schemes.