Task Allocation Algorithm In Wireless Sensor Network

Wireless sensor network is widely used in military, industrial, commercial and security, and it has a wide range of applications and development prospects. Since the self-organizing, network topology and network environment changes dynamically, and nodes’ limited power, it’s very important to get a suitable task allocation mechanism in wireless sensor networks, so that the energy consumption can be reduced, the lifetime of the network can be extended, while accelerating the speed of task completion, and to increase the nodes’ efficiency. Traditional task allocation algorithm in wireless sensor network focus on how to choose a suitable node to allocate tasks, and many people processed task allocation model based on trust mechanism or task threshold policy. However, in the present study, there is little research work for large amount of tasks while task dependencies in wireless sensor network. To study the correlation between the task group within the task can help us to reduce the completion time of the task group, to improve the work efficiency of the node, energy consumption between nodes’ communication can be reduced and the network life cycle can be extended.In wireless sensor networks, evaluation task allocation strategy mainly about the network’s energy consumption, task completion time, task completion effect. This paper firstly studies the dependent relationships between tasks, and get the task group scheduling cluster tree (CT) based on the abstract Directed Acyclic Graph (DAG), and then puts forward copy-based cluster algorithm. At the same time, we studied the negotiation process based on the contract net model, decreased the participate in bidding scale through the node clustering method, reduced the network energy consumption; And puts forward the prediction algorithm when bidding, improved the Least Mean Square(LMS) algorithm, which increased the tasks’ completion rate.This paper used OPNET to simulate the improved algorithm, to verify the validity and accuracy of the proposed algorithm. Experimental results show that the proposed algorithm reduces the task completion time, which ensure the efficiency in the use of node; Reduced the number of nodes when simulate the tasks, and at the same time decreased the nodes’ number when bidding, and greatly reduce the communication energy consumption between the nodes and prolong the survival time; improved the accuracy of node bidding values, increased the quality of the task completion.