Preference and Movement Pattern Similarity-Based Resource Discovery Enhancement Algorithm for Internet of People

Abstract

Resource discovery in disconnected delay-tolerant mobile social networks (MSNs) continues to be a challenging issue. Nowadays, the resource discovery mechanisms for MSNs are classified into three categories: social connectivity-based, movement pattern-based and content similarity-based resource discovery mechanisms. The first two categories have some advantages, such as low transmission delay and management overhead, the deficiency of the two mechanisms is that the poorer searching efficiency without considering the content similarity. The third category facilitates the higher searching efficiency. Yet, it leads to higher traffic cost due to not considering the physical location information of nodes. Furthermore, the current methods are designed in Cartesian coordinates, which is not suitable for real physical network environments. In this paper, we propose a novel resource discovery mechanism in 3-dimensional Cartesian coordinate system. Our scheme fully utilizes the content similarity, the movement pattern and the location awareness to achieve the higher searching efficiency and reduce the system overhead for MSNs. We simulated our method on the platform of the opportunistic network environment simulator. Simulations results show that our proposed scheme outperforms the state-of-the-art resource discovery schemes in terms of the searching efficiency and the average delay.