KOR

e-Article

Deceiving Reactive Jamming in Dynamic Wireless Sensor Networks: A Deep Reinforcement Learning Based Approach
Document Type
Conference
Source
GLOBECOM 2023 - 2023 IEEE Global Communications Conference Global Communications Conference, GLOBECOM 2023 - 2023 IEEE. :4455-4460 Dec, 2023
Subject
Communication, Networking and Broadcast Technologies
Components, Circuits, Devices and Systems
Engineering Profession
General Topics for Engineers
Power, Energy and Industry Applications
Signal Processing and Analysis
Wireless communication
Wireless sensor networks
Simulation
Sensors
Resource management
Jamming
Optimization
Reactive jamming
deep reinforcement learning
jamming deceiving
deep Q network (DQN)
wireless sensor network (WSN)
Language
ISSN
2576-6813
Abstract
A reactive jamming attack, which performs spec-trum jamming only during legal signal transmission based on the knowledge of sensor behaviors, poses a significant threat to wireless sensing networks (WSNs). In this paper, a novel deceiving approach is proposed for defending reactive jamming in dynamic WSNs. Specifically, when the maximum transmission power is given, we first formulate the anti-jamming process as an optimization problem to maximize the average received power while eliminating the effects of the jamming attack. Then the interaction between reactive jamming and legitimate sensors is modeled with the Markov decision process (MDP). Finally, a deep Q network (DQN) based jamming deceiving method is proposed to solve the formulated optimization problem. Simulation results show that the proposed anti-jamming scheme can converge quickly and is superior to the classical counterparts in terms of the mean of received signal power.