Research On Delay Optimization And Efficient Deployment Of Energy Harvesting Sensor Networks

Energy-harvesting wireless sensor networks is a network that can absorb energy from the surrounding environment.Although it effectively alleviates the problem of large delay due to limited wireless network energy,in order to further reduce network delay,researchers are eager to find a more effective method.The MAC protocol is one of the most basic protocols in wireless sensor networks,and its quality directly affects the network performance.Most of the current energy-harvesting wireless sensor networks still use the MAC protocol for wireless sensor networks,which makes the characteristics of energy-harvestable networks not fully utilized.Therefore,finding a MAC protocol suitable for harvestable energy networks is the key to optimization.In this regard,based on the RI-MAC protocol with higher delay,this paper proposes a Maximum Listening Length MAC(MLL-MAC)protocol.The MLL-MAC protocol can make the sender send an additional beacon according to the energy level of the node,and make the receiver maintain an additional listening time.Through these additional operations,even if MLL-MAC,like RI-MAC,misses the beacon initiated by the receiver after waking up,it can establish communication with the receiver by actively initiating additional beacons,which increases the probability of interaction between nodes.It can be seen that the actual operation effect of MLL-MAC is determined by the node energy level.Therefore,this paper designs an Estimated Historical Energy Harvesting Trends(EHEHT)algorithm to predict the current energy harvesting situation through the historical energy harvesting data closest to the current moment.Then,through theoretical analysis and simulation experiments,it is proved that the node can use the collected energy to dynamically adjust the receiver’s additional listening time,thereby effectively improving the network energy utilization rate and reducing the network sleep delay.In addition,even in the absence of energy supplementation,the MLL-MAC protocol can also take advantage of the uneven distribution of energy in the sensor network.Under the premise of ensuring that the network does not die,it uses the remaining energy in the network edge area to implement some additional operations,thereby further optimizing the network performance.The experimental results show that compared with the RI-MAC protocol,the MLL-MAC protocol can improve the network energy utilization by about 32.5% and reduce the average end-to-end delay by about 61.9%;In addition,ignoring the influence of time factor,MLL-MAC protocol can improve energy utilization by at least 4.8% and reduce the average end-to-end delay by at least 26.7% even when there is no energy replenishment.In addition,the deployment of energy harvesting devices in energyharvesting wireless sensor networks is also an important issue,because if the deployment strategy is not appropriate,the performance improvement of the network may not be obvious or the corresponding cost may be unacceptable.More importantly,with the rising cost of equipment deployment,the efficiency of improving network performance may become lower and lower.In this regard,this paper proposes a strategy of dynamically adjusting energy harvesting modules(DAEHM).The core of this strategy is to dynamically adjust the size of the solar radiation panels in the energy collection module according to the different energy consumption in the data transmission process of each node.Based on this,this paper designs the process algorithm of the DAEHM strategy.Through theoretical analysis and simulation experiments,the results of the DAEHM strategy are compared with the results of the Lowest Delay Deployment Equipment(LDDE)and the Lowest Cost Deployment Equipment(LCDE),it is proved that DAEHM’s strategy of dynamically adjusting solar radiant panels is far superior to the LDDE strategy and LCDE strategy of unifying the size of solar radiant panels.In general,when DAEHM is compared with LDDE,their average sleep latency is the same,but the cost of DAEHM is about 89.6% lower than that of LDDE;when DAEHM is compared with LCDE,the average sleep latency of DAEHM is 75.4% lower than LCDE %,while the cost of DAEHM is only about 6.3% higher than that of LDDE.