Research On Rendezvous-based And Information Potential-based Mobile Data Collection On WSNs

With the rapid development of basic facilities of Internet of Things and wide application of smart mobile devices, Wireless Sensor Networks(WSNs) have a great variety of forms. In this paper, a large number of sensor nodes are deployed in the network, being static. Mobile Sink(MS) move around the network to collect data of Event of Interest(EOI), which is directly analyzed and tackled by MSs or send to remote base stations for further mining.The first scene we study is, the MS move around the WSN along a static trajectory. The WSN is divided into multiple cluster, in which a unique Rendezvous Point(RP) is elected to be the data transmission destination from other sensors with this cluster. This paper explore to achieve the multiple-object optimization of the entire network lifetime, including the initial configuration phase and the dynamic updating phase. In the initial configuration phase, in order to achieve network energy consumption minimum and network throughput rate maximum, the optimal map between each node and its RP should be finished, the corresponding shortest path tree should be also established. Firstly, we give the optimal problem formulation, and then a heuristic solution is got by the multi-object generation algorithm, because the problem equaling to the 0-1 knapsack problem is proved to be NP. To balance energy consumption of the entire network, once the RP which consume energy rapidly has too low residual energy, the dynamic updating phase is triggered. The RP should be replaced by another optimum node, in terms of residual energy and the trajectory length of MS's movement. Then the network topology is adaptively updated. Comprehensive experiments have showed that the network lifetime is prolonged and network throughput rate is improved by proposed algorithmsIn the other scenario, MS walk around the network, and reach the vicinity of EOI to collect data or take with EOI according to some protocols. The information gradient-based routing and navigation protocols have been proved to be effective by leveraging the principle of achieving the global objective through local decision. In this paper, we establish the information gradient by solving a discrete approximation to the harmonic function, called information potential field(IPF). Energy-efficient methods to construct the IPF should be fully investigated to trade off the energy efficiency and the quality of IPF, especially in the largescale networks with high information dynamics. In this paper, two algorithms are proposed to efficiently construct the IPF, including Hierarchical Skeleton-based Construction Algorithm(HSCA) and Value Estimating Substitution Algorithm(VESA).Both algorithms obey the typical hypotheses on wireless sensor networks settings and the gossip-styled propagation principle. In addition, we propose advanced approaches to constructing IPF to tackle the challenges of its practical applications. Comprehensive simulation results show the feasibility of the proposed algorithms, which can reduce the number of iterations to reach a convergence status by 80% so as to conserve energy consumption, and they perform well considering the requirements of real life applications.