Long-term location pattern based forwarding scheme in opportunistic networks

While smart objects begin to permeate our environment, enabling interaction between smart objects in the Internet of Things (IoT) becomes a challenge. Opportunistic networks (OPPNET) are a form of mobile ad-hoc networks. In OPPNET, the communications between smart objects intermittently occur when an object contacts another. OPPNET plays an important role as an enabler for communication in IoT. In OPPNET, in order for nodes to forward message to destination, they not only need to transfer messages but also store and carry messages as relay nodes. The forwarding algorithms in opportunistic networks need to exploit human and social characteristics such as mobility pattern and social relationship. In this paper, we propose long-term location patterns based forwarding scheme (LTLP) which utilizes long-term movement pattern for predicting future location probability. In the proposed scheme, each nodes records its own location pattern and analyze the pattern of its movement. After the analysis of movement pattern is finished, we create location tree of nodes to forward the message to destination. We analyze the proposed scheme on the NS-2 network simulator with the home-cell community-based mobility model (HCMM). Experimental results show that the proposed scheme outperforms most known forwarding schemes in balancing network traffic and transmission delay.