考虑船舶动态位置的海底WSN节点能耗控制算法

由于海底环境并不具备连续稳定的电力供应,使得传感器节点通常依赖于有限的内部能量,而船舶在海底的动态位置会令节点密度布局变得更加复杂,从而导致WSN节点能量消耗不均衡。为此,提出考虑船舶动态位置的海底WSN节点能耗控制算法。建立海底WSN结构模型,利用多项式卡尔曼滤波器预测船舶轨迹;划分海底WSN簇结构,动态调整各簇的节点数量,控制海底WSN节点密度,建立船舶位置动态变化能耗模型,估算各簇节点在不同时间段的能耗需求,使其均衡消耗WSN节点能量,从而实现节点能耗控制。实验结果表明,本文算法应用后,有效平衡了节点能耗,保持连通性和覆盖性,提高海底WSN整体性能,稳定运行时间更长且失效节点增长较稳定。

基于改进RSSI测距的WSN机动目标跟踪算法

针对扩展卡尔曼滤波(EKF)在机动目标跟踪中传感器对机动目标进行测距时存在的噪声问题,提出了一种基于改进RSSI测距的WSN机动目标跟踪算法。首先,利用鱼鹰优化算法迭代寻优适合BP神经网络的权值和阈值;其次,将传感器的接收信号强度值作为神经网络的输入值,距离作为输出值对神经网络进行训练;最后,利用扩展卡尔曼滤波算法进行定位跟踪,利用匀速圆周运动模型(CM)进行滤波跟踪。使用EKF算法和RSSI算法与文中算法进行比较,以均方根误差为评价指标。经过仿真实验对比表明,所提算法相较于上述算法均方根误差分别降低了21%和56%。

A Layered Energy-Efficient Multi-Node Scheduling Mechanism for Large-Scale WSN

In recent years, target tracking has been considered one of the most important applications of wireless sensornetwork (WSN). Optimizing target tracking performance and prolonging network lifetime are two equally criticalobjectives in this scenario. The existing mechanisms still have weaknesses in balancing the two demands. Theproposed heuristic multi-node collaborative scheduling mechanism (HMNCS) comprises cluster head (CH)election, pre-selection, and task set selectionmechanisms, where the latter two kinds of selections forma two-layerselection mechanism. The CH election innovatively introduces the movement trend of the target and establishesa scoring mechanism to determine the optimal CH, which can delay the CH rotation and thus reduce energyconsumption. The pre-selection mechanism adaptively filters out suitable nodes as the candidate task set to applyfor tracking tasks, which can reduce the application consumption and the overhead of the following task setselection. Finally, the task node selection is mathematically transformed into an optimization problem and thegenetic algorithm is adopted to form a final task set in the task set selection mechanism. Simulation results showthat HMNCS outperforms other compared mechanisms in the tracking accuracy and the network lifetime.

Value Function Mechanism in WSNs-Based Mango Plantation Monitoring System

Mango fruit is one of the main fruit commodities that contributes to Taiwan’s income.The implementation of technology is an alternative to increasing the quality and quantity of mango plantation product productivity.In this study,a Wireless Sensor Networks(“WSNs”)-based intelligent mango plantation monitoring system will be developed that implements deep reinforcement learning(DRL)technology in carrying out prediction tasks based on three classifications:“optimal,”“sub-optimal,”or“not-optimal”conditions based on three parameters including humidity,temperature,and soil moisture.The key idea is how to provide a precise decision-making mechanism in the real-time monitoring system.A value function-based will be employed to perform DRL model called deep Q-network(DQN)which contributes in optimizing the future reward and performing the precise decision recommendation to the agent and system behavior.The WSNs experiment result indicates the system’s accuracy by capturing the real-time environment parameters is 98.39%.Meanwhile,the results of comparative accuracy model experiments of the proposed DQN,individual Q-learning,uniform coverage(UC),and NaÏe Bayes classifier(NBC)are 97.60%,95.30%,96.50%,and 92.30%,respectively.From the results of the comparative experiment,it can be seen that the proposed DQN used in the study has themost optimal accuracy.Testing with 22 test scenarios for“optimal,”“sub-optimal,”and“not-optimal”conditions was carried out to ensure the system runs well in the real-world data.The accuracy percentage which is generated from the real-world data reaches 95.45%.Fromthe resultsof the cost analysis,the systemcanprovide a low-cost systemcomparedtothe conventional system.

WSN Mobile Target Tracking Based on Improved Snake-Extended Kalman Filtering Algorithm

A wireless sensor network mobile target tracking algorithm(ISO-EKF)based on improved snake optimization algorithm(ISO)is proposed to address the difficulty of estimating initial values when using extended Kalman filtering to solve the state of nonlinear mobile target tracking.First,the steps of extended Kalman filtering(EKF)are introduced.Second,the ISO is used to adjust the parameters of the EKF in real time to adapt to the current motion state of the mobile target.Finally,the effectiveness of the algorithm is demonstrated through filtering and tracking using the constant velocity circular motion model(CM).Under the specified conditions,the position and velocity mean square error curves are compared among the snake optimizer(SO)-EKF algorithm,EKF algorithm,and the proposed algorithm.The comparison shows that the proposed algorithm reduces the root mean square error of position by 52%and 41%compared to the SOEKF algorithm and EKF algorithm,respectively.

Stochastic Ranking Improved Teaching-Learning and Adaptive Grasshopper Optimization Algorithm-Based Clustering Scheme for Augmenting Network Lifetime in WSNs

In Wireless Sensor Networks(WSNs),Clustering process is widely utilized for increasing the lifespan with sustained energy stability during data transmission.Several clustering protocols were devised for extending network lifetime,but most of them failed in handling the problem of fixed clustering,static rounds,and inadequate Cluster Head(CH)selection criteria which consumes more energy.In this paper,Stochastic Ranking Improved Teaching-Learning and Adaptive Grasshopper Optimization Algorithm(SRITL-AGOA)-based Clustering Scheme for energy stabilization and extending network lifespan.This SRITL-AGOA selected CH depending on the weightage of factors such as node mobility degree,neighbour's density distance to sink,single-hop or multihop communication and Residual Energy(RE)that directly influences the energy consumption of sensor nodes.In specific,Grasshopper Optimization Algorithm(GOA)is improved through tangent-based nonlinear strategy for enhancing the ability of global optimization.On the other hand,stochastic ranking and violation constraint handling strategies are embedded into Teaching-Learning-based Optimization Algorithm(TLOA)for improving its exploitation tendencies.Then,SR and VCH improved TLOA is embedded into the exploitation phase of AGOA for selecting better CH by maintaining better balance amid exploration and exploitation.Simulation results confirmed that the proposed SRITL-AGOA improved throughput by 21.86%,network stability by 18.94%,load balancing by 16.14%with minimized energy depletion by19.21%,compared to the competitive CH selection approaches.

基于改进DDPG算法的WSN优化策略研究

DDPG算法是Actor-Critic和DQN算法的结合体,作为目前深度强化学习中最为经典的算法之一,被广泛应用于WSN。针对DDPG算法训练效率低、收敛速度慢、同步误差大等问题,提出一种基于加权信息熵的深度确定性策略梯度算法。该算法提前对训练数据进行权重分配,根据权重比例训练数据,并将结果通过神经网络集成。实验结果表明,相较于DQN和DDPG算法,WIE-DDPG算法的训练效率较高、收敛速度较快、同步误差较小。

Effective data transmission through energy-efficient clustering and Fuzzy-Based IDS routing approach in WSNs

Wireless sensor networks(WSN)gather information and sense information samples in a certain region and communicate these readings to a base station(BS).Energy efficiency is considered a major design issue in the WSNs,and can be addressed using clustering and routing techniques.Information is sent from the source to the BS via routing procedures.However,these routing protocols must ensure that packets are delivered securely,guaranteeing that neither adversaries nor unauthentic individuals have access to the sent information.Secure data transfer is intended to protect the data from illegal access,damage,or disruption.Thus,in the proposed model,secure data transmission is developed in an energy-effective manner.A low-energy adaptive clustering hierarchy(LEACH)is developed to efficiently transfer the data.For the intrusion detection systems(IDS),Fuzzy logic and artificial neural networks(ANNs)are proposed.Initially,the nodes were randomly placed in the network and initialized to gather information.To ensure fair energy dissipation between the nodes,LEACH randomly chooses cluster heads(CHs)and allocates this role to the various nodes based on a round-robin management mechanism.The intrusion-detection procedure was then utilized to determine whether intruders were present in the network.Within the WSN,a Fuzzy interference rule was utilized to distinguish the malicious nodes from legal nodes.Subsequently,an ANN was employed to distinguish the harmful nodes from suspicious nodes.The effectiveness of the proposed approach was validated using metrics that attained 97%accuracy,97%specificity,and 97%sensitivity of 95%.Thus,it was proved that the LEACH and Fuzzy-based IDS approaches are the best choices for securing data transmission in an energy-efficient manner.

基于RSSI-SO算法的WSN定位研究

无线传感器网络是一种基于无线通信技术的分布式传感器网络,广泛应用于环境监测、智能家居、智能交通等领域。在WSN中,节点的位置信息对于实现很多应用非常重要。因此,WSN定位算法是WSN研究的重要内容之一。鉴于此,提出了一种基于RSSI与蛇优化算法的WSN定位研究,利用节点之间的信号强度和蛇优化算法来实现节点的定位,最后进行误差分析。在实验环境中进行了对比实验,结果表明,本文提出的方法相比于三边测量定位算法,具有更高的准确度。

基于WSN+UWB的室内人员定位系统设计

随着社会发展,家长对幼儿安全越来越重视,幼儿园环境安全与否直接关系到儿童的健康及生命。为保障园区内幼儿状况及安全,文章提出了一种室内人员定位系统设计,该系统在满足基本定位需求的同时,采用了超宽带(Ultra Wide Band,UWB)技术,具有穿透性强、抗干扰和功耗低等优势,其性能表现已超过蓝牙和Wi-Fi,并以UWB技术作为主要技术依据,将无线传感网络(Wireless Sensor Network,WSN)技术与UWB技术相结合,设计了一款智能化的人员看护及定位系统,测试结果证明了该系统设计可满足园区内人员监测及定位警报的需求。

基于WSNs的煤矿井下人员安全定位研究

针对现有煤矿井下人员安全定位方法存在的精度低、偏差大等问题,提出一种基于WSNs的井下人员定位方法。WSNs网络由大量随机布置的低成本无线传感器节点构成,在组网便捷性、功能性和稳定性等方面具有更大的优势。从结构设计方面来看,WSNs网络包括了普通传感器节点、汇聚节点、网关节点和后台上位机。其中普通节点负责采集其通信覆盖半径范围内相关数据信息,传递给汇聚节点。WSNs网络中的节点具有无线自组织的优势,其动态拓扑结构处于不断地变化之中且网络的稳定性较强。在基于WSNs网络传感器节点定位过程中,会存在一定偏差,为此,基于三角质心法可以实现井下人员的精准定位,提升煤矿井下人员作业过程的安全性。

室内障碍环境下空气质量监测异构WSN部署

针对室内空气质量中污染性气体众多、浓度分布不均,单一传感器无法有效监测,而且室内障碍物会对传感器部署位置造成影响的问题,通过改进北方苍鹰优化算法(improved northern goshawk optimization,INGO)对障碍下异构传感器进行部署研究。首先,采用SPM混沌映射对种群进行初始化,以解决原始北方苍鹰算法初始化种群多样性不高、覆盖率低、冗余度高的问题;其次,使用非线性步长权重改进Lévy飞行策略,对种群位置进行更新;最后,融合柯西变异和反向学习,解决算法后期种群易陷入局部最优的问题。结果表明,改进的优化算法在无障碍和障碍环境下覆盖率分别达到了94.2%和93.0%,与其他学者在无障碍环境下提出的算法进行对比,覆盖率分别提高了0.8%,1.2%,2.8%,7.1%。INGO算法能够对室内障碍环境下的空气质量监测传感器进行最优部署,为室内空气质量监测等复杂环境异构传感器的部署问题提供科学依据。

基于动态自适应蜣螂算法的WSN覆盖优化

针对无线传感器网络随机部署导致覆盖率低的问题,文中提出一种动态自适应蜣螂算法(ASSDBO)的WSN覆盖优化方法。首先,采用改进Tent混沌映射初始化种群,增强种群多样性;其次,采用自适应螺旋搜索策略改进蜣螂滚球行为,增强算法的全局搜索能力和收敛速度,并且在蜣螂繁殖和觅食行为中引入动态正弦边界收敛因子,平衡算法的全局搜索和局部搜索;最后,在窃贼蜣螂中引入白鲸算法引导的蜣螂动态偷窃策略,增强迭代前期种群多样性和全局搜索能力,在后期专注于局部搜索同时具备跳出局部最优的能力。通过四个基准测试函数测试表明,ASSDBO算法具有较快的收敛速度和较高的求解精度,将ASSDBO应用到WSN覆盖优化问题上,仿真结果表明,ASSDBO算法相比于改进麻雀算法(ISSA-ICR)、改进灰狼算法(IGWO)、改进粒子群算法(IPSO),覆盖率分别提升了4.7%、6.4%、7.5%。

改进猎人猎物优化算法在WSN覆盖中的应用

针对传统无线传感器网络(WSN)节点部署覆盖盲区大、分布不均等问题,提出一种改进的猎人猎物优化(IHPO)算法优化网络覆盖。首先,在猎物位置更新阶段,引入差分进化(DE)思想并借助动态比例因子进行交叉变异,从而增强种群信息交流;其次,在全局最优位置更新阶段,由α稳定分布提出自适应α变异对全局最优位置进行扰动,从而平衡不同时期算法的性能需求;最后,利用自适应α变异扰动的全局最优位置引导种群完成动态反向学习,从而增加种群的全局搜索能力和多样性。在WSN覆盖问题中,使用IHPO优化的网络节点分布更均匀、覆盖率更高,在传感器感知能力不足时能达到92.56%的覆盖率,对比原始HPO算法优化的节点提高了25.74%,对比改进粒子群优化(IPSO)算法、改进灰狼优化算法(IGWO)优化的节点分别提高了13.98%、16.41%。同时,IHPO算法优化的节点能耗更均衡,在路由测试中的网络工作时间可以延长至2500轮次。

基于改进行军蚁搜索优化器的异构WSNs覆盖增强策略

为了提高无线传感器网络(Wireless Sensor Networks,WSNs)随机部署后的覆盖效果,并且降低传感器节点在二次部署过程中的移动能耗,提出一种适用于感知半径异构的WSNs覆盖增强策略。该策略以行军蚁的种群规模作为节点部署方案,种群位置作为节点位置变量,建立了行军蚁优化器与WSNs覆盖问题的映射关系,并根据传感器节点的初始部署位置改进初始化行为,引入虚拟力导向作用改进攻击行为,基于巢穴位置改进蚁桥行为,从而在提高WSNs覆盖效果的基础上优化了传感器节点二次部署的移动能耗。仿真结果表明,相比于虚拟力算法、采样的异构WSNs覆盖优化算法与行军蚁优化器算法,所提算法的覆盖率较高,传感器节点二次部署过程中的移动距离最小。此外,当覆盖区域面积与节点规模变化时,所提算法在覆盖效果与移动能耗方面也具有较强的鲁棒性。

基于改进3DL算法的智能变电站人员WSN定位

为了提高智能变电站人员无线传感器网络(WSN)的定位精度,提出一种基于改进3DL算法的定位方法。通过对3DL算法的节点测距误差和定位误差进行补偿,降低3DL算法的节点测距误差和定位误差,实现3DL算法的改进。将改进的3DL算法应用于智能变电站的人员WSN中,可有效提高智能变电站人员WSN的定位精度。相较于改进前的3DL算法和传统极大似然估算法,所提方法具有更高的定位精度和定位覆盖率,可减小位置节点坐标计算误差累计的影响,更适用于非视距环境下智能变电站人员WSN定位。

基于自适应非线性因子杂草算法的WSN覆盖优化

无线传感器网络(WSN)的覆盖率与区域内的传感器节点分布密切关联,而现有传感器分布算法存在收敛速度慢、易陷入局部极值等问题。对此,提出了一种基于自适应非线性因子杂草算法(HA-IWO)的传感器节点分布优化方法。首先,在初始阶段,利用Halton序列产生偏差很小的初始点,使种群分布更均匀;其次,在种群扩散阶段,将非线性调和因子设置为根据迭代次数自适应产生,以调整搜索步长,解决算法易陷入局部最优的问题。最后,通过4组标准函数测试与WSN覆盖优化仿真对该算法进行验证。仿真实验表明:相比于标准杂草算法,改进后的算法具有收敛速度快、覆盖率高的优点,能有效解决WSN覆盖优化问题。

基于深度学习的WSN入侵检测系统研究与设计

传统浅层机器学习难以提取现今随时间变化的高维且非线性的海量数据的本质特征,入侵检测准确率低,算法复杂且训练时间过长。对此,采用分簇式网络拓扑,改进NCRP算法的路由协议降低网络能耗,在簇头节点使用SAESM进行压缩且异常检测提高簇头节点传送给基站节点的有效信息量及快速有效响应,在基站使用SLSTM的高级检测算法进行精准识别攻击行为与类型。分析仿真实验结果此算法入侵检测准确率较高,泛化能力较强,实时性好。

机械振动WSN大量数据自适应传输控制方法

针对机械振动无线传感器网络(wireless sensor network, WSN)在传输大量数据时,每个数据包均采用确认方式传输导致高传输能耗的问题,提出了一种大量数据自适应传输控制方法。首先,通过数据分组检测机制减少传输确认帧带来的额外能耗,并通过试验确定最佳分组数量,避免浪费WSN节点有限的存储资源;其次,利用帧控制域特定标志位实现丢包状态通知,在不引入额外数据帧的情况下告知发送节点丢包状态,避免反馈信息包下发冲突,并优化反馈信息包的退避指数,降低丢包节点因反馈信息包下发延迟导致的侦听能耗;最后,通过最小二乘法拟合丢包率与链路质量指示的关系,节点根据当前传输链路质量自适应选择最佳传输模式。试验结果表明,所提传输方法可有效降低数据传输能耗并提升传输速率。

一种能耗均衡的WSN高吞吐量路由协议研究

无线传感器网络(WSN)由若干个传感器节点通过多跳自组织的方式构成,这些节点的能量和处理能力都受到极大地限制,因此研究节能高效的路由协议具有较大的意义。为延长WSN的生存时间和提高网络吞吐量,提出了一种能耗均衡的WSN高吞吐量路由协议,该协议利用K⁃means聚类算法对节点进行分簇,并结合节点剩余能量和距离计算权重值,选择最佳节点作为簇首,此外,还利用无线收发器的参数和信道状况确定最佳固定分组大小。在数据传输阶段,使用Dijkstra算法计算多目标权重函数作为链路成本。实验结果表明该方案在网络节点节能方面优于CERP和TEEN路由协议,且提高了整个系统的吞吐量和生存时间。