Decentralized context sharing in vehicular delay tolerant networks with compressive sensing

Abstract

Vehicles equipped with various types of sensors can act as mobile sensors to monitor the road conditions. To speed up the information collection process, the monitoring data can be shared among vehicles upon their encounters to facilitate drivers to find a good route. The vehicular network experiences intermittent connectivity as a result of the mobility, which makes the inter-vehicle contact duration a scarce resource for data transmissions and the support of monitoring applications over vehicular networks a challenge. We propose a novel compressive sensing (CS)-based scheme to enable efficient decentralized context sharing in vehicular delay tolerant networks, called CS-Sharing. To greatly reduce the data transmission overhead and speed up the monitoring processing, CS-sharing exploits two techniques: sending an aggregate message in each vehicle encounter, and quick collection of information taking advantage of data sharing and the sparsity of events in vehicle networks to significantly reduce the number of measurements needed for global information recovery. We propose a novel data structure, and an aggregation method that can take advantage of the random and opportunistic vehicle encounters to form the measurement matrix. We prove that the measurement matrix satisfies the Restricted Isometry Property (RIP) property required by the CS technique. Our results from extensive simulations demonstrate that CS-Sharing allows vehicles in a large network to quickly obtain the full context data with the successful recovery ratio larger than 90%.