In hybrid wireless sensor networks composed of both static and mobile sensor nodes, the random deployment of stationary nodes may cause coverage holes in the sensing field. Hence, mobile sensor nodes are added after t...In hybrid wireless sensor networks composed of both static and mobile sensor nodes, the random deployment of stationary nodes may cause coverage holes in the sensing field. Hence, mobile sensor nodes are added after the initial deployment to overcome the coverage holes problem. To achieve optimal coverage, an efficient algorithm should be employed to find the best positions of the additional mobile nodes. This paper presents a genetic algorithm that searches for an optimal or near optimal solution to the coverage holes problem. The proposed algorithm determines the minimum number and the best locations of the mobile nodes that need to be added after the initial deployment of the stationary nodes. The performance of the genetic algorithm was evaluated using several metrics, and the simulation results demonstrated that the proposed algorithm can optimize the network coverage in terms of the overall coverage ratio and the number of additional mobile nodes.展开更多
Node deployment strategy plays an important role in wireless sensor networks(WSNs)application because it determines the coverage,connectivity and network lifetime of WSNs.This paper reports the current research on the...Node deployment strategy plays an important role in wireless sensor networks(WSNs)application because it determines the coverage,connectivity and network lifetime of WSNs.This paper reports the current research on the optimization means for achieving the desirable design goals in various applications.We categorize the placements strategies into are the static and the dynamic according to whether the node position change after the network is operational.The coverage,connectivity and energy consumption of WSNs are analysed and discussed in detail.展开更多
Wireless sensor networks(WSN)can be used in many fields.In wireless sensor networks,sensor nodes transmit data in multi hop mode.The large number of hops required by data transmission will lead to unbalanced energy co...Wireless sensor networks(WSN)can be used in many fields.In wireless sensor networks,sensor nodes transmit data in multi hop mode.The large number of hops required by data transmission will lead to unbalanced energy consumption and large data transmission delay of the whole network,which greatly affects the invulnerability of the network.Therefore,an optimal deployment of heterogeneous nodes(ODHN)algorithm is proposed to enhance the invulnerability of the wireless sensor networks.The algorithm combines the advantages of DEEC(design of distributed energy efficient clustering)clustering algorithm and BAS(beetle antenna search)optimization algorithm to find the globally optimal deployment locations of heterogeneous nodes.Then,establish a shortcut to communicate with sink nodes through heterogeneous nodes.Besides,considering the practical deployment operation,we set the threshold of the mobile location of heterogeneous nodes,which greatly simplifies the deployment difficulty.Simulation results show that compared with traditional routing protocols,the proposed algorithm can make the network load more evenly,and effectively improve energy-utilization and the fault tolerance of the whole network,which can greatly improve the invulnerability of the wireless sensor networks.展开更多
Wireless sensor networks (WSNs) are very important for monitoring underground mine safety. Sensor node deployment affects the performances of WSNs. In our study, a chain-type wireless underground mine sensor network (...Wireless sensor networks (WSNs) are very important for monitoring underground mine safety. Sensor node deployment affects the performances of WSNs. In our study, a chain-type wireless underground mine sensor network (CWUMSN) is first pre- sented. A CWUMSN can monitor the environment and locate miners in underground mines. The lowest density deployment strate- gies of cluster head nodes are discussed theoretically. We prove that the lifetime of CWUMSN with a non-uniform deployment strategy is longer than with a uniform deployment strategy. Secondly, we present the algorithm of non-uniform lowest density de- ployment of cluster head nodes. Next, we propose a dynamic choice algorithm of cluster head nodes for CWUMSN which can im- prove the adaptability of networks. Our experiments of CWUMSN with both non-uniform lowest density and uniform lowest den- sity deployments are simulated. The results show that the lifetime of CWUMSN with non-uniform lowest density deployment is almost 2.5 times as long as that of the uniform lowest density deployment. This work provides a new deployment strategy for wire- less underground mine sensor networks and then effectively promotes the application of wireless sensor networks to underground mines.展开更多
为应对开放型无线接入网(Open Radio Access Network,O-RAN)中的数据传输成本过高及网络兼容不足等问题,研究了面向O-RAN的多级边缘服务资源分配与部署联合优化问题。首先,利用四层融合模型将多目标联合优化问题转化为异构边缘服务器数...为应对开放型无线接入网(Open Radio Access Network,O-RAN)中的数据传输成本过高及网络兼容不足等问题,研究了面向O-RAN的多级边缘服务资源分配与部署联合优化问题。首先,利用四层融合模型将多目标联合优化问题转化为异构边缘服务器数量选择及位置确定问题,并提出了一种负载约束和迭代优化的异构边缘服务器资源分配算法,解决了O-RAN网络中的异构资源分配与数据传输问题。然后,提出了一种能效驱动的异构节点部署优化算法,解决了多级异构资源最佳部署位置问题。最后,利用上海电信基站的真实数据集,验证了所提资源优化与部署算法的有效性,实验结果表明,所提算法较其它算法在部署成本上至少降低了22.5%,能效比值上至少提高了25.96%。展开更多
文摘In hybrid wireless sensor networks composed of both static and mobile sensor nodes, the random deployment of stationary nodes may cause coverage holes in the sensing field. Hence, mobile sensor nodes are added after the initial deployment to overcome the coverage holes problem. To achieve optimal coverage, an efficient algorithm should be employed to find the best positions of the additional mobile nodes. This paper presents a genetic algorithm that searches for an optimal or near optimal solution to the coverage holes problem. The proposed algorithm determines the minimum number and the best locations of the mobile nodes that need to be added after the initial deployment of the stationary nodes. The performance of the genetic algorithm was evaluated using several metrics, and the simulation results demonstrated that the proposed algorithm can optimize the network coverage in terms of the overall coverage ratio and the number of additional mobile nodes.
基金National Natural Science Foundation of China(No.61071087)Natural Science Foundation of Shandong Province(No.ZR2011FM018)
文摘Node deployment strategy plays an important role in wireless sensor networks(WSNs)application because it determines the coverage,connectivity and network lifetime of WSNs.This paper reports the current research on the optimization means for achieving the desirable design goals in various applications.We categorize the placements strategies into are the static and the dynamic according to whether the node position change after the network is operational.The coverage,connectivity and energy consumption of WSNs are analysed and discussed in detail.
基金This research was funded by the National Natural Science Foundation of China,No.61802010Hundred-Thousand-Ten Thousand Talents Project of Beijing No.2020A28+1 种基金National Social Science Fund of China,No.19BGL184Beijing Excellent Talent Training Support Project for Young Top-Notch Team No.2018000026833TD01.
文摘Wireless sensor networks(WSN)can be used in many fields.In wireless sensor networks,sensor nodes transmit data in multi hop mode.The large number of hops required by data transmission will lead to unbalanced energy consumption and large data transmission delay of the whole network,which greatly affects the invulnerability of the network.Therefore,an optimal deployment of heterogeneous nodes(ODHN)algorithm is proposed to enhance the invulnerability of the wireless sensor networks.The algorithm combines the advantages of DEEC(design of distributed energy efficient clustering)clustering algorithm and BAS(beetle antenna search)optimization algorithm to find the globally optimal deployment locations of heterogeneous nodes.Then,establish a shortcut to communicate with sink nodes through heterogeneous nodes.Besides,considering the practical deployment operation,we set the threshold of the mobile location of heterogeneous nodes,which greatly simplifies the deployment difficulty.Simulation results show that compared with traditional routing protocols,the proposed algorithm can make the network load more evenly,and effectively improve energy-utilization and the fault tolerance of the whole network,which can greatly improve the invulnerability of the wireless sensor networks.
基金Project 20070411065 supported by the China Postdoctoral Science Foundation
文摘Wireless sensor networks (WSNs) are very important for monitoring underground mine safety. Sensor node deployment affects the performances of WSNs. In our study, a chain-type wireless underground mine sensor network (CWUMSN) is first pre- sented. A CWUMSN can monitor the environment and locate miners in underground mines. The lowest density deployment strate- gies of cluster head nodes are discussed theoretically. We prove that the lifetime of CWUMSN with a non-uniform deployment strategy is longer than with a uniform deployment strategy. Secondly, we present the algorithm of non-uniform lowest density de- ployment of cluster head nodes. Next, we propose a dynamic choice algorithm of cluster head nodes for CWUMSN which can im- prove the adaptability of networks. Our experiments of CWUMSN with both non-uniform lowest density and uniform lowest den- sity deployments are simulated. The results show that the lifetime of CWUMSN with non-uniform lowest density deployment is almost 2.5 times as long as that of the uniform lowest density deployment. This work provides a new deployment strategy for wire- less underground mine sensor networks and then effectively promotes the application of wireless sensor networks to underground mines.
文摘为应对开放型无线接入网(Open Radio Access Network,O-RAN)中的数据传输成本过高及网络兼容不足等问题,研究了面向O-RAN的多级边缘服务资源分配与部署联合优化问题。首先,利用四层融合模型将多目标联合优化问题转化为异构边缘服务器数量选择及位置确定问题,并提出了一种负载约束和迭代优化的异构边缘服务器资源分配算法,解决了O-RAN网络中的异构资源分配与数据传输问题。然后,提出了一种能效驱动的异构节点部署优化算法,解决了多级异构资源最佳部署位置问题。最后,利用上海电信基站的真实数据集,验证了所提资源优化与部署算法的有效性,实验结果表明,所提算法较其它算法在部署成本上至少降低了22.5%,能效比值上至少提高了25.96%。
