Mobile Wireless Sensor Network Data Gathering

Mobile wireless sensor network has a wide range of applications. For example, sensor nodes fixed in cars are used to monitor the road conditions of certain cities, the nodes carried by vehicles are composed of the mobile sensor networks. Nodes obtain road flat information through cars'vibration. For all received data, node calculates an average, which is an aggregation value. Then, sensor nodes transform the aggregation values to base station. Base station determines whether the road needs to be repaired or not through the aggregation values. Since the topology of mobile sensor networks changes frequently, moreover together with the limited sensor nodes'computing power, storage space and communication bandwidth, data aggregation in mobile wireless sensor networks faces great challenges. So far, the data aggregation based different mobility models in mobile wireless sensor networks have not been taken into account in the academic field. For these reasons, this paper proposes data aggregation algorithms based both random waypoint mobility model and group mobility model.(1) This paper proposes data aggregation algorithm in a random mobility model -- DARMSN. It adopts the idea of stochastic clustering, cluster head nodes gather information, and then forward the aggregation to sink node through the mechanism of prediction and angle forwarding. At the same time, in the forwarding process the received data can be gathered with local data. Therefore, the network traffic and the aggregation delay are reduced. In the simulation, this paper compares DARMSN with the best data collection algorithm in random waypoint mobile sensor networks. Simulation results show that the DARMSN algorithm proposed in this paper achieves the minimum traffic, lower gathering delay, and the highest accuracy of the aggregate results. (2) In the group mobility model, this paper presents two data aggregation algorithms: DAG-C and DAG-P. Two algorithms differ in the forwarding strategy. DAG-C is forwarding the aggregation based on contagion, while DAG-P is based on the transmission probability. In the simulation part, the advantages and disadvantages of DAG-C and DAG-P algorithms are analyzed in this paper, respectively, and compared with sidewinder, which is the best data collection algorithm in group mobile sensor networks. The results show that DAG-C and DAG-P algorithms can get perfect accuracy, lower energy cost and delay.(3) Through a large number of experimental analyses in this paper, concluded: different mobile models require different data processing protocols.