Cooperative Caching Towards Efficient Spectrum Utilization in Cognitive-IoT Networks

2025-12-15

AI Summary

To address spectrum scarcity and dynamic access conflicts in Cognitive Internet of Things (CIoT), this paper proposes a joint optimization framework for collaborative caching and spectrum access. Methodologically, it integrates deep reinforcement learning, multi-agent cooperative modeling, joint cache placement optimization, and dynamic spectrum access control. Crucially, it pioneers the introduction of collaborative caching into cognitive spectrum sharing—enabling CIoT secondary users to proactively cache and respond to primary user (PU) content requests, thereby shifting from conventional avoidance- or relay-based paradigms to a novel “collaboration-over-spectrum-yielding” framework. Experimental results under resource-constrained conditions demonstrate significant improvements: end-to-end latency is substantially reduced; CIoT and PU cache hit rates increase by 32% and 27%, respectively; and network throughput rises by 24%. These results validate both the effectiveness and superiority of the proposed paradigm.

Technology Category

• Planning, Routing, and Scheduling: Optimization of Spatio-temporal Systems
• Constraint Satisfaction and Optimization: Distributed CSP/Optimization
• Search and Optimization: Learning to Search

Application Category

• Systems and Infrastructure for Web, Mobile and WoT: Web performance, measurement, and characterization
• Economics, Online Markets and Human Computation: The sharing economy
• User Modeling, Personalization and Recommendation: On-Device user modeling, personalization, and recommendation

Abstract

In cognitive Internet of Things (CIoT) networks, efficient spectrum sharing is essential to address increasing wireless demands. This paper presents a novel deep reinforcement learning (DRL)-based approach for joint cooperative caching and spectrum access coordination in CIoT networks, enabling the CIoT agents to collaborate with primary users (PUs) by caching PU content and serving their requests, fostering mutual benefits. The proposed DRL framework jointly optimizes caching policy and spectrum access under challenging conditions. Unlike traditional cognitive radio (CR) methods, where CIoT agents vacate the spectrum for PUs, or relaying techniques, which merely support spectrum sharing, caching brings data closer to the edge, reducing latency by minimizing retrieval distance. Simulations demonstrate that our approach outperforms others in lowering latency, increasing CIoT and PU cache hit rates, and enhancing network throughput. This approach redefines spectrum sharing, offering a fresh perspective on CIoT network design and illustrating the potential of DRL-guided caching to highlight the benefits of collaboration over dynamic spectrum access scenarios, elevating CIoT performance under constrained resources.

Problem

• Optimizes caching and spectrum access in CIoT networks
• Reduces latency by caching content at the network edge
• Enhances throughput and cache hit rates via DRL collaboration

Innovation

• DRL-based joint caching and spectrum coordination
• Caching PU content at edge for latency reduction
• Collaborative caching redefines spectrum sharing in CIoT