Edge computing is a promising infrastructure evolution to reduce traffic loads and support low-latency communications. Furthermore, content-centric networks provide a natural solution to cache contents at edge nodes. However, it is a challenge for edge nodes to handle massive and highly dynamic content requests by users, and if without an efficient content caching strategy, the edge nodes will encounter high traffic load and latency due to increasing retrieval from content providers. This paper formulates a proactive edge caching problem to minimize the content retrieval cost at edge nodes. We exploit the inherent content caching and request aggregation mechanism in the content-centric networks to jointly minimize traffic load and content retrieval delay cost generated by the massive and dynamic content requests. We develop a Q-learning algorithm, which is an online optimal caching strategy, as it is adaptable to dynamic content popularity and content request intensity, and derive the long-term minimization of the content retrieval cost. Simulation results illustrate that the proposed algorithm can achieve a lower content retrieval cost compared with several baseline caching schemes. INDEX TERMS Content-centric networks, dynamic content requests, edge computing, proactive caching, Q-learning. XIAOGENG XU (Student Member, IEEE) received the B.S. degree in communications engineering from the Beijing University of Technology, China, in 2005, the M.S. degree in digital communication systems and technology from the Chalmers University of Technology, Sweden, in 2007. She is currently pursuing the Ph.D. degree in communication and information systems from the Beijing University of Posts and Telecommunications. Her current researches focus on caching performance analysis and optimization in content-centric networks. CHUNYAN FENG (Senior Member, IEEE) received the B.S. degree in communications engineering and the M.S. and Ph.D. degrees in communication and information systems from the Beijing University of Posts and Telecommunications (BUPT), Beijing, China. She is currently a Professor with the School of Information and Communication Engineering, BUPT. Her research interests are in the areas of broadband networks and wireless communication systems. Her current research focuses on cognitive radio and green wireless communications.
With the rapid growth of network traffic and the enhancement of the quality of experiences of users, Information-Centric Networking (ICN), which is a content-centric network architecture with named data caching and routing, is proposed to improve the multimedia content distribution efficiency. In arbitrary topology, cache nodes and users are randomly distributed and connected, hence it is challenging to achieve an optimal caching placement under this situation. In this paper, we propose a caching placement algorithm for arbitrary topology in ICN. We formulate an optimization problem of proactive caching placement for arbitrary topology combined with multi-hop forwarding, with an objective to optimize the user delay and the load balancing level of the nodes simultaneously. Since the original problem is NP-hard, we solve the formulated caching placement problem in two sub-problems, content replica allocation sub-problem and content replica placement sub-problem. First, in the content replica allocation sub-problem, the replica number of each content is obtained by utilizing the auction theory. Second, the replica number of each content is used as a constraint for the content replica placement sub-problem, which is solved by matching theory. The caching placement algorithm combined with multi-hop NRR forwarding maximizes the utilization of cache resources in order to achieve better caching performance. The numerical results show that significant hop count savings and load balancing level improvement are attainable via the proposed algorithm.INDEX TERMS Arbitrary topology, caching placement, ICN, multi-hop forwarding.
With the rapid development of wireless sensor techniques and wireless communication techniques in recent years, Wireless Sensor Networks (WSN) has achieved significant improvements. Sensor nodes deployment can be static or dynamic. The support of the mobility becomes one of the important issues in WSN. The problem caused by frequent topology changes is to find a tradeoff between reliability and energy efficiency. Inspired by wireless networks Radio Resource Management (RRM), we introduce handover management strategies to solve the problem. In this research, we therefore deliver some novel WSN based handover techniques under various conditions.
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