Collaborative Caching for Energy Optimization in Content-Centric Internet of Things
Shupeng Wang, Handi Chen, Yongjian Wang
Abstract
The development of the content-centric Internet of Things (C2IoT) enriches the services provided by the IoT devices, which diversifies the provided contents. However, system resources are terribly wasted by repeated delivering the same content. Efficiently utilizing caching resources can reduce the link load caused by delivering and forwarding contents, further improving the users’ quality of experience (QoE). However, the collaboration among independent decision nodes is insufficient, which consumes substantial energy redundantly. To solve the mentioned issue, we propose a genetic-algorithm-based collaborative caching scheme integrating on-path strategy and off-path strategy, named cost-oriented caching scheme (CCS), to minimize the energy consumption of requesting contents. First, based on the analysis of content popularity, users’ historical requests, and transmission delay, an optimization problem is formulated to minimize the communication delay of obtaining content. Then, we formulate a submodular function optimization problem to optimize energy consumption, and an improved genetic algorithm is proposed to solve the mentioned problem. Performance evaluations demonstrate that the proposed CCS is superior to other existing caching schemes.