Work

An Evolutionary Game Theory and Reinforcement Learning-Based Security Protocol for Intermittently Connected Wireless Networks

Year: 2026

Type: article

Abstract: Intermittently Connected Wireless Networks (ICWNs) are characterized by dynamic node mobility and the absence of persistent end-to-end paths, making them highly susceptible to security threats. This paper proposes a novel secure routing protocol, called the Evolutionary Game Theoretic model with Reinforcement Learning (EGT-RL), designed to provide adaptive and resilient protection against blackhole attacks in such networks. EGT-RL integrates Q-learning for dynamic threat assessment with evolutionary game theory to model and influence node behavior over time. Simulation results, based on both synthetic and real-world mobility traces, show that EGT-RL significantly outperforms three benchmark protocols in delivery ratio, packet drops, end-to-end latency, and communication overhead.

Source: Telecom

DOI: 10.3390/telecom7010013

Authors: Jagdeep Singh, Sanjay K. Dhurandher, Isaac Woungang, Petros Nicopolitidis

Institutions: Sant Longowal Institute of Engineering and Technology, Netaji Subhas University of Technology, Toronto Metropolitan University, Fo Guang University, Aristotle University of Thessaloniki

Language: English

Topic: Mobile Ad Hoc Networks

Subfield: Computer Networks and Communications

Field: Computer Science

Domain: Physical Sciences

SDG: Industry, innovation and infrastructure

Open Access status: gold

Estimated APC paid: $1,448