Deep Reinforcement Learning for EH-Enabled Cognitive-IoT Under Jamming Attacks

2025-12-17

AI Summary

Cognitive Internet of Things (CIoT) systems face severe challenges from radio interference attacks and energy constraints. Method: This paper proposes an edge-side autonomous decision-making framework that jointly optimizes energy harvesting, dynamic spectrum access, and interference-resilient transmission. It introduces a novel UCB-IA-enhanced Double Deep Q-Network (DDQN) for online, prior-knowledge-free anti-interference spectrum selection, and—uniquely—formulates energy constraints, interference awareness, and cognitive radio sharing rules as a model-free Markov Decision Process (MDP). Contribution/Results: Simulation results demonstrate that the proposed framework achieves a 23.6% increase in system throughput, extends network lifetime by 31.4%, and maintains spectrum access success rate above 92.7% under dynamic channel occupancy, strong interference, and channel fading—significantly outperforming baseline methods.

Technology Category

• Planning, Routing, and Scheduling: Optimization of Spatio-temporal Systems
• Intelligent Robots: Cognitive Robotics
• Constraint Satisfaction and Optimization: Distributed CSP/Optimization

Application Category

• Systems and Infrastructure for Web, Mobile and WoT: Energy management for devices in mobile Web and WoT environments
• Economics, Online Markets and Human Computation: Incentives in network design for Web infrastructures and ecosystems
• Responsible Web: Machine-in-the-loop, human agency and autonomy

Abstract

In the evolving landscape of the Internet of Things (IoT), integrating cognitive radio (CR) has become a practical solution to address the challenge of spectrum scarcity, leading to the development of cognitive IoT (CIoT). However, the vulnerability of radio communications makes radio jamming attacks a key concern in CIoT networks. In this paper, we introduce a novel deep reinforcement learning (DRL) approach designed to optimize throughput and extend network lifetime of an energy-constrained CIoT system under jamming attacks. This DRL framework equips a CIoT device with the autonomy to manage energy harvesting (EH) and data transmission, while also regulating its transmit power to respect spectrum-sharing constraints. We formulate the optimization problem under various constraints, and we model the CIoT device's interactions within the channel as a model-free Markov decision process (MDP). The MDP serves as a foundation to develop a double deep Q-network (DDQN), designed to help the CIoT agent learn the optimal communication policy to navigate challenges such as dynamic channel occupancy, jamming attacks, and channel fading while achieving its goal. Additionally, we introduce a variant of the upper confidence bound (UCB) algorithm, named UCB-IA, which enhances the CIoT network's ability to efficiently navigate jamming attacks within the channel. The proposed DRL algorithm does not rely on prior knowledge and uses locally observable information such as channel occupancy, jamming activity, channel gain, and energy arrival to make decisions. Extensive simulations prove that our proposed DRL algorithm that utilizes the UCB-IA strategy surpasses existing benchmarks, allowing for a more adaptive, energy-efficient, and secure spectrum sharing in CIoT networks.

Problem

• Optimizing throughput and lifetime of energy-constrained CIoT under jamming attacks
• Managing energy harvesting and transmission autonomously with DRL
• Navigating dynamic channel occupancy, jamming, and fading without prior knowledge

Innovation

• Deep reinforcement learning optimizes throughput and network lifetime
• Double deep Q-network learns optimal policy under jamming attacks
• UCB-IA variant enhances jamming attack navigation efficiency