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Energy-Efficient Routing Protocol in Vehicular Opportunistic Networks: A Dynamic Cluster-based Routing Using Deep Reinforcement Learning

Meisam Sharifi Sani, Saeid Iranmanesh, Raad Raad, Faisel Tubbal

TL;DR

Cluster-based Routing using Deep Reinforcement Learning (CR-DRL) is proposed, an adaptive routing approach that integrates an Actor-Critic learning framework with a heuristic function that enables real-time optimal relay selection and dynamic cluster overlap adjustment to maintain connectivity while minimizing redundant transmissions and enhancing routing efficiency.

Abstract

Opportunistic Networks (OppNets) employ the Store-Carry-Forward (SCF) paradigm to maintain communication during intermittent connectivity. However, routing performance suffers due to dynamic topology changes, unpredictable contact patterns, and resource constraints including limited energy and buffer capacity. These challenges compromise delivery reliability, increase latency, and reduce node longevity in highly dynamic environments. This paper proposes Cluster-based Routing using Deep Reinforcement Learning (CR-DRL), an adaptive routing approach that integrates an Actor-Critic learning framework with a heuristic function. CR-DRL enables real-time optimal relay selection and dynamic cluster overlap adjustment to maintain connectivity while minimizing redundant transmissions and enhancing routing efficiency. Simulation results demonstrate significant improvements over state-of-the-art baselines. CR-DRL extends node lifetimes by up to 21%, overall energy use is reduced by 17%, and nodes remain active for 15% longer. Communication performance also improves, with up to 10% higher delivery ratio, 28.5% lower delay, 7% higher throughput, and data requiring 30% fewer transmission steps across the network.

Energy-Efficient Routing Protocol in Vehicular Opportunistic Networks: A Dynamic Cluster-based Routing Using Deep Reinforcement Learning

TL;DR

Cluster-based Routing using Deep Reinforcement Learning (CR-DRL) is proposed, an adaptive routing approach that integrates an Actor-Critic learning framework with a heuristic function that enables real-time optimal relay selection and dynamic cluster overlap adjustment to maintain connectivity while minimizing redundant transmissions and enhancing routing efficiency.

Abstract

Opportunistic Networks (OppNets) employ the Store-Carry-Forward (SCF) paradigm to maintain communication during intermittent connectivity. However, routing performance suffers due to dynamic topology changes, unpredictable contact patterns, and resource constraints including limited energy and buffer capacity. These challenges compromise delivery reliability, increase latency, and reduce node longevity in highly dynamic environments. This paper proposes Cluster-based Routing using Deep Reinforcement Learning (CR-DRL), an adaptive routing approach that integrates an Actor-Critic learning framework with a heuristic function. CR-DRL enables real-time optimal relay selection and dynamic cluster overlap adjustment to maintain connectivity while minimizing redundant transmissions and enhancing routing efficiency. Simulation results demonstrate significant improvements over state-of-the-art baselines. CR-DRL extends node lifetimes by up to 21%, overall energy use is reduced by 17%, and nodes remain active for 15% longer. Communication performance also improves, with up to 10% higher delivery ratio, 28.5% lower delay, 7% higher throughput, and data requiring 30% fewer transmission steps across the network.

Paper Structure

This paper contains 15 sections, 27 equations, 18 figures, 3 tables, 1 algorithm.

Table of Contents

  1. Introduction
  2. Related work
  3. Clustering and Machine Learning-Based Routing in OppNets
  4. System model
  5. Preliminary
  6. System description
  7. Problem formulation
  8. Proposed method
  9. Time complexity
  10. Simulation setting and evaluation metrics
  11. Parameter analysis
  12. First scenario
  13. Second scenario
  14. Experimental results
  15. Conclusion and future work

Figures (18)

  • Figure 1: Store-carry-forward architecture in OppNets.
  • Figure 2: Dynamically clustered routing using common members.
  • Figure 3: Convergence of policy and value loss (a), and cumulative reward (b) in the AC learning framework.
  • Figure 4: Sensitivity analysis of CR-DRL hyperparameters: (a) impact of AC learning rates on delivery ratio; (b) effect of discount factor on normalized performance; (c) influence of distance scaling factor on clustering efficiency metrics; (d) role of entropy coefficient in balancing exploration and convergence.
  • Figure 5: Vehicles vs. clusters with ADT (a), and (b) without ADT.
  • ...and 13 more figures