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Dellat: Delivery Latency Minimization in Wireless Sensor Networks with Mobile Sink

机译:Dellat:带移动接收器的无线传感器网络中的交付延迟最小化

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Adopting mobile data gathering in wireless sensor networks (WSNs) can reduce the energy consumption on data forwarding thus achieve more uniform energy consumption among sensor nodes. However, the data delivery latency inevitably increases in mobile data gathering due to the travel of the mobile sink. In this paper, we consider a delivery latency minimization problem (DLMP) in a randomly deployed WSN. Our goal is to minimize the travel latency of the mobile sink. We formulate the DLMP as an integer programming problem which subjects to the direct access constraint, the data transmission constraint and the route traverse constraint. We prove that the DLMP is an NP-Complete (NPC) problem, and then propose a substitution heuristic algorithm to solve it by shortening the travel route and having the mobile sink move and collect data at the same time. We compare the proposed algorithm with other two algorithms, a traveling salesman problem (TSP) heuristic algorithm and a random heuristic algorithm through simulations. Our extensive simulation results show that although all the three algorithms can shorten the data delivery latency in mobile data gathering, the proposed substitution heuristic algorithm is the most effective one.
机译:在无线传感器网络(WSN)中采用移动数据收集可以减少数据转发的能耗,从而在传感器节点之间实现更均匀的能耗。但是,由于移动接收器的移动,在移动数据收集中数据传递延迟不可避免地增加了。在本文中,我们考虑了随机部署的WSN中的传递延迟最小化问题(DLMP)。我们的目标是最大程度地减少移动接收器的旅行延迟。我们将DLMP公式化为整数规划问题,它受到直接访问约束,数据传输约束和路径遍历约束的约束。我们证明了DLMP是一个NP-Complete(NPC)问题,然后提出了一种替代启发式算法,通过缩短行进路线并让移动宿同时移动和收集数据来解决该问题。我们通过仿真将提出的算法与其他两种算法进行比较,这两种算法分别是旅行商问题(TSP)启发式算法和随机启发式算法。我们广泛的仿真结果表明,尽管这三种算法都可以缩短移动数据收集中的数据传递延迟,但是所提出的替代启发式算法是最有效的一种。

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