Research On Transmission Optimization Based On Data Aggregation In Wireless Sensor Networks

Because wireless sensor networks have the characteristics of large-scale intensivedeployment, similar nodes collect data typically have great similarities, which lead to alot of data redundancy. According to the network characteristics and applicationenvironments, data aggregation technique has become an important mean of dataprocessing technology within the network. Data aggregation is a promising technology,reduce the number of data packets and remove the inherent redundancy of the originaldata through the utilization of the network processing. However, shortening the timedelay of data aggregation is a basic demand in wireless sensor networks, especially inreal-time applications, it is a challenging problem. Therefore, to design a dataaggregation scheme of low-power and low-delay is very important to improve thereal-time capability of wireless sensor networks.Considering energy consumption and communication delay problem, two dataaggregation schemes are proposed for the wireless sensor networks.(1) The real-time dynamic adaptive routing protocol based on the selection ofaggregation timing is energy and delay balanced algorithm protocol. Balance energyconsumption and delay cost by controlling the wait time of data aggregation and thechoice of real-time dynamic adaptive routing within a specified delay, without explicitmaintenance of a structure. Simulation results show that this algorithm has a betteraggregation gain, a lower average energy consumption of nodes and a lower delay andmiss ratio.(2) The peony-tree based data aggregation based on maximum independent set andfirst-fit algorithm is a low-power and low-delay sheep scheduling algorithm protocol.On the basis of data aggregation tree, using time-division multiplexing way, weintroduced the concept of low-power sleep schedule in the cyclical time slot of dataaggregation. This is largely possible to reduce the amount of data transmission，networktime delay and achieve a low-power data aggregation. Simulation results show that theproposed algorithm can reduce network energy consumption, prolong the networklifetime and reduce the network delay cost.