The Application Of Ant Colony Algorithm In Wireless Sensor Networks

The wireless sensor network node monitors the environment,collects data,processes the data,and transmits the data back to the platform for analysis using a self-organizing network.The application value is very high.But the energy of sensor nodes is limited,which limits the service life of wireless sensor networks.Therefore,the energy consumption problem becomes the bottleneck of wireless sensor networks.How to design an efficient and energy-saving wireless sensor network routing algorithm has become the key.The ant colony algorithm can efficiently and quickly find an optimal path for wireless sensor networks.Introducing the residual energy of the node through the probability selection formula in the ant colony algorithm can effectively control the energy consumption of the node,and can ensure that the conditions of high residual energy and minimum energy consumption in the optimal path are met.It is precisely because the advantages of ant colony algorithm and the requirements of wireless sensor network complement each other and improve their ability to deal with problems,they have the idea and feasibility of applying ant colony algorithm to wireless sensor network.The existing ant colony algorithm cannot guarantee the final convergence of the path,excessive energy consumption of nodes,and the existence of extraneous nodes on the path.In this paper,by introducing the dynamic radius search factor and energy prediction factor,an improved ant colony algorithm DEARA,On the one hand,the dynamic radius search factor can ensure the final convergence of the ant colony algorithm and improve the convergence effect of the ant colony algorithm;On the other hand,the energy prediction factor makes the energy consumption of the node uniform and does not appear to consume the remaining energy of the node but cannot successfully transmit the complete data.However,there are still some irrelevant nodes in the reverse direction in the convergence path of the DEARA algorithm,so based on the DEARA algorithm,the DDEARA algorithm is proposed by introducing the direction factor.Avoid selection of irrelevant nodes.(1)Dynamic search factor.When the dynamic radius search factor makes the ant unable to find the next hop candidate node within the current radius circle,it can expand the search radius until it can find the next hop candidate node,thereby ensuring that the ant colony algorithm can finally converge.In the traditional ant colony algorithm,the search radius may take a fixed value.In this way,the convergence of the ant colony algorithm depends heavily on the distribution of the nodes.If the nodes are unevenly distributed,the ant colony algorithm may fail to converge.Therefore,finding the next hop candidate node by the dynamic radius search factor can improve the convergence of the ant colony algorithm.(2)Energy predictor factor.Before the ant selects the next hop node,it is necessary to determine the remaining energy value of the candidate node and analyze whether it can support the successful transmission of a complete data packet.If the remaining energy of the node is not enough to complete a data transmission,it is discarded.Introducing the node energy predictor to avoid the unreasonable phenomenon that the node is still overloaded when the energy of the node is insufficient,that is,when all the energy of a node is consumed,all the data cannot be successfully transmitted,which is against the practical significance.(3)Direction factor.In the process of finding the optimal path,each generation of ants does not search completely without directions,but instead searches for points along the forward direction of the initial node and the terminal node as a whole.The ant introduces a directional factor in the search for the next hop candidate node,and a directional search point,which avoids that unrelated nodes in the opposite direction are selected as the next hop candidate node,reduces the optimal path distance,and saves node energy consumption.Improve algorithm optimization performance.Simulation results show that the ant colony algorithm optimized by "three-step progressive",First,the final optimal path length is short,the number of intermediate nodes passing through is moderate,and the position distribution is uniform.Secondly,the energy consumption of the nodes is less,and the phenomenon of concentrated death of nodes in a certain area will not occur.Finally,there are no extraneous nodes in the opposite direction on the path,reducing the path length and saving node energy consumption.The "three-step progressive" ant colony algorithm DDEARA has stronger optimizing ability and better optimizing effect,which improves the performance and life span of wireless sensor networks.