Genetic Algorithm Based Node Deployment in Hybrid Wireless Sensor Networks

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.

Survey on node deployment in wireless sensor networks

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.

Optimal Deployment of Heterogeneous Nodes to Enhance Network Invulnerability

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.

Sensor deployment strategy for 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.

面向暗网抑制的普适性安全理论研究构想和成果展望

近年来,匿名网络及其架构上的“暗网”因其强隐蔽、高匿名、抗追溯的特性,成为传递敏感信息、实施网络攻击及开展网络犯罪的重要工具,给国家安全和社会稳定带来严重威胁。为应对暗网治理中通信行为隐蔽难识别、网络拓扑跳变难绘制、陷阱节点部署难伪装等挑战,本文旨在研究面向暗网抑制的普适性安全理论。本研究的关键科学问题凝练为:强对抗机制下结构信息缺失的动态时变网络行为刻画与推理问题。为突破以上问题,拟从基础理论、应用技术和示范系统3个层面开展研究,实现1个框架、3个方法、1个系统等五大研究内容。具体为:一是,建立面向暗网流量差异性与行为共性的协同量化理论框架,提出异构暗网普适性特征与差异化要素表征、统一安全量化、生态脆弱图构建及推理理论,解决网络结构复杂多样、通信行为动态多变的暗网可抑制性量化评估问题;二是,提出基于凸优化问题求解的流量实时轻量化识别方法,通过构建基于自身相似性关联的小流抽样模型与基于高斯核函数和多模态优化的暗网流量识别与业务分类模型,实现对暗网流量的实时、轻量化精准识别与分类;三是,提出基于行为不变性的多网络全时域连接预测与通连关系绘制方法,在统一安全量化理论的基础上,对跨位点连接进行表示,在动态网络中过滤无关连接后进行多网络全时域连接预测,并绘制通连关系,实现局部观测条件下暗网通连的多点全局关联;四是,提出基于局部观测暗网通连最优化的陷阱节点部署与溯源方法,实现部分可控节点条件下的暗网追踪溯源;五是,研发面向真实暗网场景的实时流量检测与溯源示范应用系统,并在相关执法单位进行落地应用,实现对暗网犯罪的精准治理。并且,详细阐述了协同量化理论构建、轻量化暗网流量识别、连接预测与通连关系绘制、陷阱部署与溯源机理、示范应用系统等五大任务的技术路线。通过本文的基础理论研究、技术应用和系统示范验证,推动暗网治理的理论发展,提升抑制暗网的效率,具有重要的社会与经济效益。

Multiple Node Placement Strategy for Efficient Routing in Wireless Sensor Networks

The advances in recent technology have lead to the development of wireless sensor nodes forming a wireless network, which over the years is used from military application to industry, household, medical etc. The deployment pattern of sensor nodes in Wireless Sensor Network (WSN) is always random for most of the applications. Such technique will lead to ineffective utilization of the network;for example fewer nodes are located at far distance and dense nodes are located at some reason and part of the region may be without the surveillance of any node, where the networks do consume additional energy or even may not transfer the data. The proposed work is intended to develop the optimized network by effective placement of nodes in circular and grid pattern, which we call as uniformity of nodes to be compared with random placement of nodes. Each of the nodes is in optimized positions at uniform distance with neighbors, followed by running a energy efficient routing algorithm that saves an additional energy further to provide connectivity management by connecting all the nodes. Simulation results are compared with the random placement of nodes, the residual energy of a network, lifetime of a network, energy consumption of a network shows a definite improvement for uniform network as that of with the random network.

Method to deploy wireless relays in industrial wireless monitoring

In light of demands for wireless monitoring and the characteristics of wireless channel,a complete deployment method containing channel survey,path loss estimation,and gradient grade of wireless relay nodes is proposed.It can be proved by experiments that under the premise of meeting the requirements of real-time and redundant-topology,the total number of relay nodes could be minimized by using the proposed method.

数据引流下异构传感器网络能量空洞防护算法

异构传感器网络在工作时,靠近传感器基站位置的节点会由于接收转发次数较多而出现能量空洞现象。为有效对此现象实施防护,保障网络安全运行,提出数据引流下异构传感器网络能量空洞防护算法。构建网络节点部署模型,对传感器网络节点实施节点优化部署;计算网络节点能耗对网络实施能量均衡控制,获取异构传感器网络节点的簇内传输跳数;结合获取的节点簇内跳数以及能量均衡结果开展节点接收数据的的引流分配,平衡传感器网络层内节点能量消耗,实现传感器网络的能量空洞防护。实验结果表明,使用上述方法开展传感器网络能量空洞防护时,数据转发接收能耗较低,防护方法性能高,防护效果好。

基于TCPSO的三维无线传感器网络覆盖

针对三维无线传感器网络(3D WSNs)的节点部署问题,提出了一种基于双层混沌粒子群优化(TCPSO)算法的解决方案。利用TCPSO算法对节点进行优化部署,以提高网络的空间覆盖率。TCPSO算法通过非线性分类系数将种群分为精英种群和普通种群,分别采取不同的速度、位置更新公式进行迭代优化。TCPSO提出了基于Logistic混沌映射的递减惯性权重,用来控制算法在局部的开发,并且为了避免算法早熟,引入了Levy飞行策略增强算法的全局搜索能力。通过在1个三维网格空间中进行仿真实验,验证TCPSO以及粒子群优化(PSO)算法解决3D WSNs的节点部署问题的能力。分别在不同数量的节点数、不同的通信半径以及不同的种群规模上进行了3组实验,实验采用控制变量法,观察在不同条件下TCPSO的性能。TCPSO在全部的实验中提出的节点部署方案均明显优于PSO的部署方案。

基于组合加权k近邻分类的无线传感网络节点复制攻击检测方法

无线传感网络节点体积小,隐蔽性强,节点复制攻击检测的难度较大,为此提出一种基于组合加权k近邻分类的无线传感网络节点复制攻击检测方法。通过信标节点的空间位置数据与相距跳数得出各节点之间的相似程度,结合高斯径向基核函数求解未知节点的横轴、纵轴的空间坐标,确定各网络节点的空间位置;根据网络节点的属性特征与投票机制建立节点复制攻击模型,凭借组合加权k近邻分类法划分节点类型,并将结果传送至簇头节点,由簇头节点做出最后的仲裁,识别出节点复制攻击行为。仿真结果表明,所提方法的节点复制攻击检测率最大值为99.5%,最小值为97.9%,对节点复制攻击检测的耗时为5.41 s,通信开销数据包数量最大值为209个,最小值为81个。

面向O-RAN的多级异构边缘资源分配与部署

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%。

基于边缘节点技术的电力系统入侵安全防护

在电力系统安全防护中,为解决防护不全面问题,设计了基于边缘节点技术的电力系统安全防护方法。首先,基于边缘节点技术,建立边缘多址接入计算模型,对电力系统实施数据迁移,实现数据分布式部署。其次,设计数据传输加密算法,并基于GCForest,建立电力系统网络入侵检测模型,模型由样本输入层、多粒度扫描层以及训练层构成,实施系统入侵检测。测试结果表明:该方法的平均服务时延最低仅为456.32ms,网络健壮性没有较大波动,入侵检测的最高误检率和漏检率分别为0.13%和0.14%。

大差异无线传感网络节点最佳覆盖部署算法设计

无线传感网络在节点能耗大差异的情况下,其网络覆盖存在节点部署效果差、覆盖率低等问题,因此,设计一种新的大差异无线传感网络节点最佳覆盖部署算法。通过三角点阵排列法,以节点能耗大差异角度,确定最少部署节点数量,并且初步部署节点,引入离子键导向的粒子群优化覆盖算法,在粒子群算法中增加离子键导向,获取节点最佳位置,从而获取网络节点最佳部署,实现网络节点最佳覆盖部署算法设计。仿真结果表明,所提方法对传感网络大差异节点部署效果较好,节点分布均匀,重复覆盖较少,节点利用率为95%,覆盖率达到99.0%以上,该方法有效优化了网络节点部署效果,达到了最佳覆盖。

基于区块链技术的光纤传感器物联网节点部署模型

光纤传感器物联网节点部署是保证物联网高效运行的关键,但部署过程中极易出现物联网覆盖率较低和节点能耗较高等问题。因此,为了有效解决这一问题,提出基于区块链技术的光纤传感器物联网节点部署模型。利用区块链技术构建物联网联合环境感知模型,获取光纤传感器物联网环境的感知信息;根据感知到的环境信息构建光纤传感器物联网节点部署优化模型,降低节点能量消耗和部署时间;采用非支配排序遗传(Non-dominated Sorting Genetic Algorithm-Ⅱ,NSGA-Ⅱ)算法求解该模型,实现光纤传感器物联网节点的最优部署。实验结果表明,所提方法能够提高物联网运行寿命,且物联网覆盖率在75%以上、节点剩余率在0.6%以上,部署效率高。

动态环境下无线Mesh网络的路由器节点部署

在无线Mesh网络(Wireless Mesh network,WMN)中,Mesh路由器(Mesh router,MR)的节点部署对于网络性能有很大的影响.尤其是当Mesh客户端(Mesh client,MC)是动态移动时,MR会根据动态网络拓扑调整其位置,这种动态环境下所带来的能耗问题以及节点的负载均衡问题都变得更加难以解决.本文提出一种应用模拟退火(Stimulated annealing,SA)算法与差分进化(Differential evolution,DE)算法的混合优化算法DY-DESA来处理动态环境下的路由器节点部署问题.部署这些路由器时考虑的性能指标是客户端覆盖率、网络连通性以及节点的负载均衡指数.仿真结果表明,运用新提出的算法对无线Mesh网络中路由器节点进行部署,其在客户端覆盖率、网络连通性以及节点的负载均衡指数等方面都有所提升.

一种基于5G-ICN的缓存容量分配方法

随着第五代移动通信网络(5G)与以信息为中心的网络(ICN)的发展,业内提出的5G-ICN架构备受关注,其异构性更加突出,现有的ICN同质缓存容量分配方法已不适用。为了满足该架构异构缓存的容量分配需求,该文提出了一种基于节点重要性的5G-ICN混合组网缓存容量分配方法。采用量化节点位置属性与流量属性的方法计算节点的重要性,结合实际存储容量设置缓存部署单元进行仿真试验,结果表明,与现有方案相比,该文容量分配方法提升了19%的内容命中率与30%的时延。

基于重构SPT的单链路故障路由保护方法

为了减少故障对网络运行带来的影响,提出了一种基于重构SPT的单链路故障路由保护算法SLFRPRSPT。该算法在最短路径树SPT的基础上实现,通过制定一系列定义和规则,对SPT进行重构,搜索节点关系发生改变的节点,为每个节点计算最佳备份下一跳节点,从而达到提高路由可用性的目的。经过实验验证,其在网络拓扑中故障保护率可以达到1,并且具有较低的路径拉伸度,可以有效避免单链路故障带来的影响。该方案支持增量部署和逐跳转发,便于实现。

基于梯度下降算法的无线传感网络节点部署模型研究

针对无线传感网络中节点部署最优化问题,提出一种基于Adam梯度下降算法的优化节点部署模型。首先,引入网络节点感知模型以及离散化区域覆盖计算方法;然后,以部署节点数量最小化为目标函数以及网络覆盖率作为约束条件建立0-1规划模型;最后,基于Adam梯度下降算法对提出的最优化模型进行求解。为验证所提出模型的有效性,对提出的数学模型进行模拟仿真。结果显示:相较于其他经典的节点部署模型,所提模型求得的部署方案在确保覆盖率高于阈值的基础上使得所需节点数量更少且算法收敛速度更快。

基于端边协同的节点部署和资源分配联合优化方法

随着物联网技术的快速发展,边缘计算作为一种重要的数据处理方式,在多样化的应用场景中展现出其独特的优势。在这种背景下,边缘服务器的位置部署和资源分配成为了提高任务处理效率的关键因素。然而,这一过程面临着终端设备的广泛分布和边缘服务器的异构特性带来的显著挑战。为了有效解决这些问题,提出了一种基于端边协同的节点部署和资源分配联合优化方法,旨在全面提升边缘计算系统的整体性能。首先,利用分层聚类算法,根据终端设备在功能和地理位置上的相似性,将它们有效地划分为若干个区域。随后,根据边缘服务器的处理能力、存储空间和网络带宽等关键指标,决定每个区域内最适合的边缘节点。最后,通过联合优化节点部署和资源利用率来指导任务的分配情况。为了验证所提方法的有效性,在公开数据集上对不同方法进行了仿真实验。实验结果表明,相比现有方法,所提方法可以将负载均衡水平提升30%以上,并将任务处理的时延和能耗降低10%以上,这在提升边缘计算系统的可持续性和效率方面具有重要意义。

基于多策略混合改进HHO算法的WSN节点覆盖优化

针对监测区域内无线传感器网络节点部署容易出现分布不均匀、有效覆盖率低等问题,提出一种多策略混合改进哈里斯鹰算法的WSN节点覆盖优化策略。利用Fuch无限折叠混沌初始化、自适应精英个体对立学习、正余弦优化和高斯与拉普拉斯最优解变异策略对标准哈里斯鹰优化算法的性能进行改进。利用改进算法求解WSN节点覆盖优化问题,以监测区域网络覆盖率最大为目标,对节点部署位置寻优。实验结果表明,改进策略能够得到更高的网络覆盖率,减少传感节点冗余,延长网络生存时间。