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

Douglas Moura, Geymerson S. Ramos, Andre L. L. Aquino, Antonio Loureiro

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.

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

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.
Paper Structure (13 sections, 6 equations, 13 figures, 2 tables, 2 algorithms)

This paper contains 13 sections, 6 equations, 13 figures, 2 tables, 2 algorithms.

Table of Contents

  1. Introduction
  2. Related work
  3. Offloading data aggregation problem
  4. Approaches to determine offloading data aggregation points
  5. Optimal Solution
  6. Reservation-Based Algorithm
  7. Centrality-Based Algorithm
  8. Method for Parameter Selection
  9. Evaluation and results
  10. Scenarios
  11. Centralized scenario
  12. Decentralized scenario
  13. Conclusion

Figures (13)

  • Figure 1: Mobile data offloading in vehicular sensor networks.
  • Figure 2: Aggregation points selection based on $k$-closeness centrality.
  • Figure 3: Cumulative distribution function of the aggregation rate of the centrality-based algorithm.
  • Figure 4: Aggregation rate obtained using a centrality-based algorithm compared to the RB and the optimal solutions (single-hop communication).
  • Figure 5: Aggregation rate obtained using a centrality-based algorithm compared to the RB and optimal solutions (multi-hop communication).
  • ...and 8 more figures

Theorems & Definitions (1)

  • Definition 3.1