A Stigmergy Learning Approach at the Edge: Securely Cooperative Caching for Fog Radio Access Networks

Abstract

To support the rapid development of multimedia services under the Internet of Things (IoT), fog radio access network (F-RAN) has become an emerging architecture in the 5G era. In this paper, content caching in a cloud and fog heterogeneous cooperative manner for F-RAN is investigated. More specifically, we jointly consider cache placement and file transmission in F-RAN, where fog access points (F-APs) serving as collaborative caching agents to provide caching for popular files, thus reducing the traffic from cloud and improving content delivery efficiency. A file download latency minimization problem subject to the storage capacities of F-APs is formulated. A distributed learning algorithm based on a swarm collaboration framework, i.e., stigmergy which enables an F-AP to expand its influence to other F-APs is proposed to improve caching resource utilization. In addition, a double-masking protocol is proposed to guarantee the security of F-APs' locations during stigmergy learning. Extensive simulations are conducted to show the effectiveness and reliability of our proposed scheme.