Several data collection algorithms, which are based on the combination of using mobile sinks and multiple-hop forwarding, have been proposed to prolong the network lifetime of wireless sensor networks. However, most approaches treat the collection point selection and touring path planning as two independent problems, which leads to a sub-optimal solution for data collection. This article proposed an ant colony optimization based end-to-end data collection strategy to perform the collection point selection and the touring path planning simultaneously. The proposed algorithm first constructs a data-forwarding tree, and then heuristically selects collection points and plans a touring path at the same time. The performance evaluation shows that the end-to-end strategy can improve the network lifetime of wireless sensor network compared to other approaches, especially in the unbalanced distribution scenario of sensors. The end-to-end strategy is also capable of being integrated with other methods.

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Recent studies have demonstrated the advantage of applying mobile sink to prevent the energy-hole problem and prolong network lifetime in wireless sensor network. However, most researches treat the touring length constraint simply as the termination indicator of rendezvous point selection, which leads to a suboptimal solution. In this paper, we notice that the optimal set of rendezvous points is unknown but deterministic and propose to elect the set of rendezvous points directly with the multiwinner voting-based method instead of step-by-step selection. A weighted heuristic voter generation method is introduced to choose the representative voters, and a scoring rule is also well designed to obtain a satisfying solution. We also employ an iterative schema for the voting score update to refine the solution. We have conducted extensive experiments, and the results show that the proposed method can effectively prolong the network lifetime and achieve the competitive performance with other SOTA methods. Compared to the methods based on step-by-step selection, the proposed method increases the network lifetime by 23.2% and 10.5% on average under the balanced-distribution and unbalanced-distribution scenarios, respectively.

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