Regression based critical information aggregation and dissemination in VANETs: A cognitive agent approach

Abstract

Data aggregation in Vehicular Ad hoc NETworks (VANETs) is an efficient technique for effective usage of communication resources. In dense VANET traffic scenarios, data aggregation is needed to represent several numbers of almost similar critical information into one refined critical information to reduce bandwidth requirements. This paper proposes a cognitive agent based critical information aggregation and dissemination in VANETs by using regression mechanism. Regression based cognitive agent approach efficiently aggregates the collected critical information and minimizes redundant data dissemination. The proposed scheme works over clustered vehicles by using a set of static and mobile agents. The scheme operates in the following steps: (1) validation and filtering of collected critical information; (2) generation of beliefs based on valid and filtered critical information; (3) aggregating the beliefs to develop desire using a regression technique; (4) revision of desire for better quality of aggregation; (5) finalizing the intention based on revised desire; (6) disseminating aggregated information to neighboring clusters. We validate the proposed scheme by simulation. The scheme performs better as compared to ESSMD (Event Suppression for Safety Message Dissemination) scheme in terms of critical information acquisition delay, aggregation delay, end-to-end delay, dissemination delay and bandwidth utilization.