With the development of the Internet of Things(IoT),it requires better performance from wireless sensor networks(WSNs),such as larger coverage,longer lifetime,and lower latency.However,a large amount of data generated...With the development of the Internet of Things(IoT),it requires better performance from wireless sensor networks(WSNs),such as larger coverage,longer lifetime,and lower latency.However,a large amount of data generated from monitoring and long-distance transmission places a heavy burden on sensor nodes with the limited battery power.For this,we investigate an unmanned aerial vehicles assisted mobile wireless sensor network(UAV-assisted WSN)to prolong the network lifetime in this paper.Specifically,we use UAVs to assist the WSN in collecting data.In the current UAV-assisted WSN,the clustering and routing schemes are determined sequentially.However,such a separate consideration might not maximize the lifetime of the whole WSN due to the mutual coupling of clustering and routing.To efficiently prolong the lifetime of the WSN,we propose an integrated clustering and routing scheme that jointly optimizes the clustering and routing together.In the whole network space,it is intractable to efficiently obtain the optimal integrated clustering and routing scheme.Therefore,we propose the Monte-Las search strategy based on Monte Carlo and Las Vegas ideas,which can generate the chain matrix to guide the algorithm to find the solution faster.Unnecessary point-to-point collection leads to long collection paths,so a triangle optimization strategy is then proposed that finds a compromise path to shorten the collection path based on the geometric distribution and energy of sensor nodes.To avoid the coverage hole caused by the death of sensor nodes,the deployment of mobile sensor nodes and the preventive mechanism design are indispensable.An emergency data transmission mechanism is further proposed to reduce the latency of collecting the latency-sensitive data due to the absence of UAVs.Compared with the existing schemes,the proposed scheme can prolong the lifetime of the UAVassisted WSN at least by 360%,and shorten the collection path of UAVs by 56.24%.展开更多
Path planning algorithm is the key point to UAV path planning scenario.Many traditional path planning methods still suffer from low convergence rate and insufficient robustness.In this paper,three main methods are con...Path planning algorithm is the key point to UAV path planning scenario.Many traditional path planning methods still suffer from low convergence rate and insufficient robustness.In this paper,three main methods are contributed to solving these problems.First,the improved artificial potential field(APF)method is adopted to accelerate the convergence process of the bat’s position update.Second,the optimal success rate strategy is proposed to improve the adaptive inertia weight of bat algorithm.Third chaos strategy is proposed to avoid falling into a local optimum.Compared with standard APF and chaos strategy in UAV path planning scenarios,the improved algorithm CPFIBA(The improved artificial potential field method combined with chaotic bat algorithm,CPFIBA)significantly increases the success rate of finding suitable planning path and decrease the convergence time.Simulation results show that the proposed algorithm also has great robustness for processing with path planning problems.Meanwhile,it overcomes the shortcomings of the traditional meta-heuristic algorithms,as their convergence process is the potential to fall into a local optimum.From the simulation,we can see also obverse that the proposed CPFIBA provides better performance than BA and DEBA in problems of UAV path planning.展开更多
基金supported in part by National Natural Science Foundation of China under Grants 62122069, 62071431, 62072490 and 62301490in part by Science and Technology Development Fund of Macao SAR, China under Grant 0158/2022/A+2 种基金in part by the Guangdong Basic and Applied Basic Research Foundation (2022A1515011287)in part by MYRG202000107-IOTSCin part by FDCT SKL-IOTSC (UM)-2021-2023
文摘With the development of the Internet of Things(IoT),it requires better performance from wireless sensor networks(WSNs),such as larger coverage,longer lifetime,and lower latency.However,a large amount of data generated from monitoring and long-distance transmission places a heavy burden on sensor nodes with the limited battery power.For this,we investigate an unmanned aerial vehicles assisted mobile wireless sensor network(UAV-assisted WSN)to prolong the network lifetime in this paper.Specifically,we use UAVs to assist the WSN in collecting data.In the current UAV-assisted WSN,the clustering and routing schemes are determined sequentially.However,such a separate consideration might not maximize the lifetime of the whole WSN due to the mutual coupling of clustering and routing.To efficiently prolong the lifetime of the WSN,we propose an integrated clustering and routing scheme that jointly optimizes the clustering and routing together.In the whole network space,it is intractable to efficiently obtain the optimal integrated clustering and routing scheme.Therefore,we propose the Monte-Las search strategy based on Monte Carlo and Las Vegas ideas,which can generate the chain matrix to guide the algorithm to find the solution faster.Unnecessary point-to-point collection leads to long collection paths,so a triangle optimization strategy is then proposed that finds a compromise path to shorten the collection path based on the geometric distribution and energy of sensor nodes.To avoid the coverage hole caused by the death of sensor nodes,the deployment of mobile sensor nodes and the preventive mechanism design are indispensable.An emergency data transmission mechanism is further proposed to reduce the latency of collecting the latency-sensitive data due to the absence of UAVs.Compared with the existing schemes,the proposed scheme can prolong the lifetime of the UAVassisted WSN at least by 360%,and shorten the collection path of UAVs by 56.24%.
基金This project is supported by National Science Foundation for Young Scientists of China(61701322)the Key Projects of Liaoning Natural Science Foundation(20170540700)+3 种基金the Key Projects of Liaoning Provincial Department of Education Science Foundation(L201702)Liaoning Natural Science Foundation(201502008,20102175)the Program for Liaoning Excellent Talents in University(LJQ2012011)the Liaoning Provincial Department of Education Science Foundation(L201630).
文摘Path planning algorithm is the key point to UAV path planning scenario.Many traditional path planning methods still suffer from low convergence rate and insufficient robustness.In this paper,three main methods are contributed to solving these problems.First,the improved artificial potential field(APF)method is adopted to accelerate the convergence process of the bat’s position update.Second,the optimal success rate strategy is proposed to improve the adaptive inertia weight of bat algorithm.Third chaos strategy is proposed to avoid falling into a local optimum.Compared with standard APF and chaos strategy in UAV path planning scenarios,the improved algorithm CPFIBA(The improved artificial potential field method combined with chaotic bat algorithm,CPFIBA)significantly increases the success rate of finding suitable planning path and decrease the convergence time.Simulation results show that the proposed algorithm also has great robustness for processing with path planning problems.Meanwhile,it overcomes the shortcomings of the traditional meta-heuristic algorithms,as their convergence process is the potential to fall into a local optimum.From the simulation,we can see also obverse that the proposed CPFIBA provides better performance than BA and DEBA in problems of UAV path planning.
