科研之友
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摘要
We develop a novel cloud-assisted energy-efficient computation scheme for opportunistic mobile social networks. Our proposed method can predict future network behavior based on the analysis of historical people-to-people contact patterns stored in the cloud, and accordingly optimize the communication protocol towards balanced QoE (Quality of Experience) and energy consumption in opportunistic mobile social networks. Specifically, we develop an analytical model for the cloud side to quantitatively compare node communication capability. Our model is extended from the conventional concept of walk to dynamic networks, and adopts the thermal Green's Function that originates from statistical physics for down-weighting longer walks. By combining all combinatorial dynamic walks into a cumulative total over all possible lengths of the edges, a concise equation is developed to account for the relative information propagation capability of each mobile node. The equation can be constructed at any time point along the network evolution direction, and therefore can be used for predicting future individual's communication activities as well as network future behavior. We validate the proposed model through extensive experiments based on a variety of real-world trace datasets. It is shown that our model is effective in quantifying information that flows through each mobile node, and the prediction accuracy of nodal future communication activities can reach 75%.
