Study On Energy Efficient Routing Algorithm Based On Swarm Intelligence Optimization For Wireless Sensor Networks

Numerous mini and low-powered sensors construct the wireless sensor networks(WSNs)using a self-organized manner.WSNs take charge of monitoring the targets in its sensing range and transmitting the monitored data to the sink by multi-hop communication.Due to the convenient deployment and great self-organization,WSNs have been widely applied to various real-time supervision tasks.In WSNs,sensors are powered by their carried-on batteries.Once a sensor exhausts its energy,it will be invalid.When sensors in the network begin to die,the performance of the network will drop sharply in the aspect of connectivity and coverage rate.Routing algorithms can help to reduce the total energy consumption of the network and balance the energy consumption of different sensors.Therefore,it's necessary for energy-saving routing algorithms during the design of WSNs.In many proposed routing algorithms,source node usually uploads the data to the remote sink by multi-hop communication and it results in the premature death of nodes which are close to the sink because of the heavy burden of data forwarding.This phenomenon is also called "hotspots" problem.Recent study on routing algorithms for WSNs is mainly focused on the method of clustering,the schema of multi-hop communication and data collection,while most of the presented algorithms don't take full use of the global information to optimize the energy cost of the whole network.Swarm intelligence optimization algorithms can utilize the collective wisdom to search the solution collaboratively and it is very suitable for the routing optimization in WSNs.This paper mainly studies on the clustering and the trajectory scheduling of the mobile sink in WSNs combining with particle swarm optimization(PSO)as well as ant colony optimization(ACO)to reduce the total energy expending of the whole network and balance the energy cost between different nodes.The main work of this thesis includes:1)We present a strategy of relay node selection for remote communication based the optimal communication distance in WSNs.Source node always prefers to choose the node which is towards a destination and is closet to optimal communication distance to optimize the total energy cost of multi-hop transmission.Meanwhile,we present a clustering method based on energy centers.Instead of location centers,the energy centers are acquired by PSO algorithm for cluster head(CH)selection to balance the energy consumption between different regions2)We present a moving trajectory scheduling algorithm based on shortest path for mobile sink.We consider the wireless communication range based on the classic Traveling Salesman Problem(TSP)and combine the PSO algorithm to further shorten the moving path.3)We present a data collection algorithm based on single-hop communication and optimal coverage for mobile sink.Nodes upload their monitored data to the mobile sink using single-hop transmission and the mobile sink only collect the data when it stops at the sojourn points.The sojourn points cover as much sensors as possible and the overlapped covered sensors are as less as possible to enhance the efficiency of the mobile sink.