Opportunistic network takes advantage of the contact opportunities introduced by node movement for data transmission. Data is usually piggybacked during the movement of the carrier, which guarantees its independence, however, the system performance is impaired. Inspired from the motion nature of nodes, this paper proposes an adaptive motion based opportunistic routing protocol to address the problem. It develops a data forwarding priority model from the perspective of data communication and determines data transmission rules in combination with node activity range partition scheme. A differential replica transfer strategy is thus proposed to achieve a tradeoff between transmission efficiency and system overhead. A free motion degree model is constructed based on which a utility function is deduced to choose appropriate nodes for data relaying. The simulation results show that the proposed algorithm achieves higher packet delivery ratio and less transmission latency while satisfying application requirements and restraining network overhead. INDEX TERMS Data forwarding priority, free motion degree, opportunistic routing, utility function, activity range partition.
The mobile ad hoc network (MANET) is a kind of dynamic, easy to construct and universal network, which has been widely concerned by a large number of researchers. Graph theory provides an effective theoretical tool for MANETs modeling and analysis. Clustering is one of the most effective methods to measure network performance with different attributes. This paper gives the basic concept of graph kernel and discusses the principle of optimizing graph kernel and multi-graph kernel. In this paper, we propose a Graph Kernel based Clustering Algorithm in MANETs (GKCA). The GKCA algorithm gives the basic concept of graph kernel, discusses the principle of optimizing graph kernel and multi-graph kernel, and proposes the basic principle based on d-hop graph kernel. GKCA algorithm uses shortest path (SP) to connect different cluster head nodes for packet transmission. The performance of GKCA algorithm, such as the control packets ratio, packets loss ratio, and average end-to-end delay are experimentally evaluated using network simulation (NS2) software. Experimental analysis shows that the proposed approach is efficient, and its performance advantage in dynamic mobile networks is promising.
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