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| Title | A Novel Caching Framework For Information-Centric Iot Using Deep Reinforcement Proximal Policy Optimization |
|---|---|
| ID_Doc | 3270 |
| Authors | Asmat H.; Ullah F.; Khan A.A.; Ali F.; Mohmand M.I. |
| Year | 2025 |
| Published | Computer Communications, 241 |
| DOI | http://dx.doi.org/10.1016/j.comcom.2025.108261 |
| Abstract | The Internet of Things (IoT) continues to evolve rapidly, necessitating innovative approaches to content delivery as the number of connected devices increases. Integrating Information-Centric Networking (ICN) within IoT environments offers a transformative solution, shifting from host-centric to content-centric architectures. This shift is particularly suitable for the distributed nature of IoT applications, which enhances content retrieval and distribution efficiency. However, the dynamic and diverse patterns of IoT networks require intelligent and adaptive caching mechanisms. This paper proposes an enhanced centrally controlled cache (ECCC) scheme that integrates the Proximal Policy Optimization (PPO) algorithm to optimize caching decisions in ICN-IoT environments. The ECCC scheme adapts in real time, adjusting caching strategies based on network conditions, resulting in improved network performance, higher energy efficiency, and reduced server load. The ECCC demonstrated an average energy savings of 15% and a cache-hit ratio improvement of 10% compared to traditional schemes. Extensive simulations demonstrate that ECCC outperforms traditional caching schemes, significantly improving the efficiency of the IoT application network and resource management. Furthermore, this work opens up new opportunities for smart cities, autonomous systems, and edge computing applications, where real-time data access and efficient resource management are critical. © 2025 Elsevier B.V. |
| Author Keywords | Caching; Information centric network; Internet of Things; Update |
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