A Centrality Approach to Select Offloading Data Aggregation Points in Vehicular Sensor Networks

TL;DR

This paper introduces a centrality-based method for selecting data aggregation points in vehicular sensor networks, significantly reducing upload costs and improving data aggregation efficiency through simulation-based evaluation.

Contribution

It presents a novel centrality-based approach for vehicle selection as aggregation points, outperforming reservation-based and optimal solutions in realistic scenarios.

Findings

30.92% reduction in upload costs

up to 10.45% improvement in aggregation rate

validated with real traffic data simulation

Abstract

This work proposes a centrality-based approach to identify data offloading points in a VSN. The solution presents a scheme to select vehicles used as aggregation points to collect and aggregate other vehicles' data before uploading it to processing stations. We evaluate the proposed solution in a realis tic simulation scenario derived from data traffic containing more than 700,000 individual car trips for 24 hours. We compare our approach with both a reservation-based algorithm and the optimal solution. Our results indicate an upload cost reduction of 30.92\% using the centrality-based algorithm and improving the aggregation rate by up to 10.45% when considering the centralized scenario.