Research On Power-Saving Protocols And Algorithms For Wireless Sensor Networks

Wireless Sensor Networks(WSNs) are special wireless communication networks which are composed of a large number of nodes with low cost and simple structure. These nodes are easy to be deployed without any fixture.They can collect information quickly,accurately and comprehensively under a variety of occasions. WSNs make a strong impact on the intercommunion between human and the nature. And they are considered as one of the most important technologies in the 21st century.Since almost all nodes are supported by battery which are difficult to be supplied,energy efficiency is one of the most important goals in designing wireless sensor networks.Moreover energy-efficiency design is also significant for decreasing network maintenance cost and saving natural resources.This thesis investigates the hotspots of the research on the power-saving protocols for WSNs,studies the energy-efficiency issues on the MAC layer and the cross-layer protocols.Besides,it proposes a deployment algorithm and a monitoring algorithm based on the artificial intelligence theory.The main results and innovations of the thesis are as follows:1) Proposes a TDMA MAC protocol TDMA-WSN:First of all,the nodes broadcast between the neighborhoods to find the conflicting sets.Then each node is allocated a slot in the frame in the slot allocation process.The nodes which are conflicting with each other will not be assigned the same slot.Then the nodes could transmit data without collision during their own slots in the data transmission process.TDMA-WSN can solve the hidden nodes,collision, competition and overhearing phenomenon,and prove that the conflicting set is effective and minimal which increases the number of nodes sending data at the same time without collision and improves the throughput.Moreover,the self-organized and distributed channel access pattern decreases the mount of control packets.Simulation results indicate that TDMA-WSN outperforms the traditional contention-based and scheduling-based protocols in terms of energy consumption and prolongs the lifetime of the networks.2)Proposes two Cross-layer protocols CLWSN and TEPA:CLWSN and TEPA both exchange information directly between the network layer and the MAC layer,which can avoid the disadvantage of large delay in the traditional protocols.They optimize energy utilization and improve the integrated performance of the network.CLWSN fuses the TDMA-WSN and the tree routing protocol.However,the definition of the conflicting set is different from that in the TDMA-WSN.It reduces the number of the nodes in the conflicting set to increase the throughput by using the routing information from the tree.Besides,in the slot optimization scheme,it optimizes the slot assignment in the path of the tree by rearranging the order of the slots to avoid the disadvantage of large delay with the routing information.TEPA also fuses the TDMA-WSN and the tree routing protocol.On one hand, TEPA is different from the distributed CLWSN that the BS calculates centrally to assign the TDMA schedule for the whole networks using the Differential Evolution (DE) algorithm which has fast convergence speed.In DE,the candidate is the schedule of the networks,and the routing informations are referenced in the crossing and selecting operations.To minimize the total delay of the networks,the direction of the evolution is limiting contention between adjacent branches and accelerating transmission in individual branch of the data gathering tree.On the other hand,it is different from the previous schemes that TEPA does not try to minimize the frame length but choose a suitable schedule length based on the tradeoff on throughput and delay under different traffic load.Simulation results show that CLWSN and TEPA enhance the integrated performance in terms of delay and energy consumption.3)Proposes the intelligent monitoring algorithm SOMSA and the intelligent deployment algorithm SOMDAThis thesis studies the energy saving algorithms in WSNs using neural networks theory.SOMSA implements the intelligent monitoring in the variable environment, which is based on artificial neural-networks self-organizing maps(SOM) algorithm. During the training process,the nodes do not work alone but compete and cooperate to choose the victors which are the most adaptive to the environment.Then the victors guide the evolution direction,and adjust their neighbors' internal prototype vectors and the sampling frequency to study the input patterns of the environment.At the end of the training,the sampling frequency of the network will reflect the variety of the environment to save the energy consumption of the sensing module and increase perceptive precision.Intelligent deployment algorithm SOMDA resolves the self-adaptive deployment problem in the sensor networks.First of all,the randomly distributed nodes need to be arranged in a two-dimensional lattice.During the SOM training process,the input patterns are the event location.The nodes which detect the events are active,and the activated nodes update their location following the events.After going through the training,the networks keep stable and the statistical distribution of the nodes approaches that of the events in the interest area.The simulation results indicate that SOMDA does not lose the coverage rate but enhance the performance in terms of detecting ability and energy equalization.