Efficient path-sense transmission based on IoT system in opportunistic social networks

With the rapid development of 5G technology, mobile smart devices have become ubiquitous in daily life. The opportunistic social network composed of these mobile smart devices is a manifestation of the current network technology being upgraded to a new level. How to better perceive the spatiotemporal movement stated information about opportunistic social network nodes and establishing a suitable information prediction model is the key for the current research of opportunistic social networks. In the social network environment where widely distributed terminal carriers are members of the society, the network has the characteristics of sparsely distributed nodes, fast movement, and highly dynamic typologies, etc. Data transmission of nodes will cause great energy and overhead during the transmission. Aiming at this problem, this paper proposes an efficient path-sense message transmission strategy (EPST) based on Internet of Things systems in opportunistic social networks. It is a strategy proposed combining the social relationship and mobility characteristics of nodes. Furthermore, it proposes the problem of how to use the locally connected paths in opportunistic social networks. By studying the connectivity of network topology, it gives the identification method of stable locally connected subnets in the network and the method of effective path sensing. Simulation experiments show that this strategy can effectively improve the delivery ratio and greatly reduce network delay and overhead.