Data fusaggregation algorithm based on dynamic minimal spanning tree routing protocol

Abstract

Large-scale geographic environmental monitoring and physical infrastructure for transferring and processing data cannot maintain the same growth rate with the monitoring scale, thus make the data gathering and processing under unreliable links, showing a kind of saturation flow state; and the ability of wireless sensor network (WSN) seemingly cannot be stabilized. On the other hand, though the calculation results from ideal network model are accurate enough, they deviate from the practical application greatly, hence the network users cannot adequately analyze and utilize the sensed data from industrial field network, and also cannot analyze the influence of network size and performance on data fusaggregation. Because of these, we will present the‘transitional region phenomenon'to be one of assumptions for industrial field simulation, and propose a data fusaggregation algorithm for the practical application on this basis, i.e. data fusaggregation algorithm based on reliability (R algorithm). When designing the R algorithm, as an example of sum operator, the lower limit η of reliability of appreciate aggregation sum result will be calculated by analyzing and synthesizing the result automatically. Then the aggregation sum result and the value of η will be sent to users together. In addition to providing the summary information from the monitored area to users, R algorithm also provides the parameter η as the judgment of information reliability to facilitate users to do further handling of aggregation results and improve the WSN sensing performance. Simulation results describe the changing rule of reliability η caused by unreliable links from the signal-to-noise ratio in transitional region, and discuss the network influence of size and performance on reliability η, with the increase of network operation cycles and network scale, when the value of reliability η becomes gradually close to 0. And this provides theoretical foundations and empirical formulas for WSNs from theoretical model to practical industrial application.