Research On Intelligent Cooperative Algorithm For Distributed Wireless Sensor And Actor Networks

Wireless Sensor and Actor Networks(WSANs),formed by cooperative communication between nodes,is an innovative continuation development of traditional wireless sensor networks(Wireless Sensor Networks).The addition of heterogeneous actor nodes makes WSANs have the ability to control and change the physical world.This feature also makes great changes to the WSANs cooperative communication approach.In addition to the sensor node-sensor node(S-S)cooperative communication provided by the traditional WSNs,the sensor node-actor node(S-A)cooperative communication,the actor node-actor node(A-A)cooperative communication.Efficient cooperative work mechanism is a necessary condition to optimize node resource allocation,establish reliable communication links and allocate nodes reasonably,so it is the key to improve network lifetime,enhance network fault tolerance and robustness,and also is the foundation of the WSANs which plays a more effective role in civil and military fields.On the basis of the research status at home and abroad,three kinds of WSANs intelligent cooperative control algorithms are proposed in this paper.The related research work is as follows:(1)In order to solve the problem of data packet loss,transmission delay and unbalance of energy consumption in the S-S cooperative work,a S-S cooperative method is proposed based on improved Artificial Bee Colony Algorithm.This method aims at optimizing the sensing node and the relay node.Firstly,the energy sensing model and the perceived cooperative response probability are used to complete the selection of the dominant sensing node.Then,the improved honeybee foraging mechanism is used to find the optimal relay node.The process analogy to the process of bees searching for honey to build a nest,through a limited loop iteration,completed the selection of the dominant relay node;Finally,simulation experiments show that compared to the typical SS cooperative algorithm,the network performance of the proposed algorithm is very large Improvement,the network life is significantly improved;(2)In order to solve the problem of data packet loss,delay and "energy hole" caused by data aggregation in S-A cooperation process,a dynamic data aggregation algorithm based on biological immune mechanism is proposed.The algorithm aims at optimizing the movement trajectories of relay nodes,proxy sinks,and design executor nodes.First,under the influence of learning factors,the relay node's selection probability is calculated using the affinity and node residual energy,and the information The distortion dynamically corrects the cooperative response probability threshold to optimize the number of selected nodes to be activated;then,the probability of selection of the proxy sink is calculated from the affinity,node residual energy,and load conditions;finally,the mobile actuator uses a bidding mechanism to dynamically bid for the proxy sink.,and determine the direction of movement based on the probability of the success of the proxy sink campaign,complete the dynamic data collection.The simulation results show that the load balance and network lifetime of the proposed algorithm are better than other algorithms.(3)Aiming to solve the problems that the task completion time is too long and the energy of the nodes are unbalanced during the cooperative work of Actor-Actor(AA),inspired by the biological immune mechanism,an AA collaborative method based on biological immune mechanism is proposed.The objective of this method is to solve the problem of the maximum constraint time and the optimal node involved in the collaboration.The state prediction function is introduced into the task cooperation time model to obtain the maximum system constraint time.The biological immune mechanism is used to solve the candidate nodes and the cooperative response probability.The power control technology dynamically changes the scope of nodes cooperation request.Finally,it selects the dominant nodes to participate in the task decision-making and processing.The simulation results show that the proposed algorithm has better performance than typical RC and MOTS algorithms in terms of the average completion time and the network lifetime.