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.

A Novel Negative Multinomial Distribution Based Energy-Efficient Reputation Modeling for Wireless Sensor Networks(WSNs)

In wireless sensor networks(WSNs),nodes are usually powered by batteries.Since the energy consumption directly impacts the network lifespan,energy saving is a vital issue in WSNs,especially in the designing phase of cryptographic algorithms.As a complementary mechanism,reputation has been applied to WSNs.Different from most reputation schemes that were based on beta distribution,negative multinomial distribution was deduced and its feasibility in the reputation modeling was proved.Through comparison tests with beta distribution based reputation in terms of the update computation,results show that the proposed method in this research is more energy-efficient for the reputation update and thus can better prolong the lifespan of WSNs.

Hybrid Marine Predators Optimization and Improved Particle Swarm Optimization-Based Optimal Cluster Routing in Wireless Sensor Networks(WSNs)

Wireless Sensor Networks(WSNs)play an indispensable role in the lives of human beings in the fields of environment monitoring,manufacturing,education,agriculture etc.,However,the batteries in the sensor node under deployment in an unattended or remote area cannot be replaced because of their wireless existence.In this context,several researchers have contributed diversified number of cluster-based routing schemes that concentrate on the objective of extending node survival time.However,there still exists a room for improvement in Cluster Head(CH)selection based on the integration of critical parameters.The meta-heuristic methods that concentrate on guaranteeing both CH selection and data transmission for improving optimal network performance are predominant.In this paper,a hybrid Marine Predators Optimization and Improved Particle Swarm Optimizationbased Optimal Cluster Routing(MPO-IPSO-OCR)is proposed for ensuring both efficient CH selection and data transmission.The robust characteristic of MPOA is used in optimized CH selection,while improved PSO is used for determining the optimized route to ensure sink mobility.In specific,a strategy of position update is included in the improved PSO for enhancing the global searching efficiency of MPOA.The high-speed ratio,unit speed rate and low speed rate strategy inherited by MPOA facilitate better exploitation by preventing solution from being struck into local optimality point.The simulation investigation and statistical results confirm that the proposed MPOIPSO-OCR is capable of improving the energy stability by 21.28%,prolonging network lifetime by 18.62%and offering maximum throughput by 16.79%when compared to the benchmarked cluster-based routing schemes.

基于IPSO-BP神经网络的WSNs数据融合算法

针对无线传感器网络(WSNs)数据融合算法中反向传播(BP)神经网络存在对初值敏感、收敛速度慢、易陷入局部最优解等问题,提出基于改进粒子群优化BP(IPSO-BP)神经网络的WSNs数据融合算法。首先,用细菌觅食算法的趋化、迁徙算子对粒子群优化(PSO)算法进行改进;然后,用IPSO算法优化BP神经网络的权值和阈值,再引入到WSNs数据融合中,簇成员节点负责采集监测数据,在簇首节点通过优化后的BP神经网络对数据进行特征提取,并将融合结果发送至汇聚节点。仿真结果表明:IPSO-BP算法能有效提高融合精度和收敛速度,减少冗余数据传输,延长网络生命周期。

基于改进镜面反射优化算法的WSNs覆盖优化

针对无线传感器网络(WSNs)节点部署存在覆盖盲区大和均匀分布性差等不足,提出一种改进镜面反射优化算法(MSRA)的WSNs覆盖优化方法。首先,采用种群策略将算法单一种群扩充为多种群,并引入Piecewice映射初始化种群,提高种群初始质量和搜索空间的遍历性;其次,融合了改进的Tent映射和莱维飞行方式对个体位置进行变异,提高算法跳出局部最优能力;然后,利用莱维飞行策略扰动最优个体位置,以平衡算法的全局探索与局部开发能力。仿真结果表明:相比镜面反射优化算法(SRA)和对比算法,所提算法寻优能力更优,有效地改善了WSNs节点分布的均匀性和覆盖盲区,提高了覆盖率。

Weak node protection to maximize the lifetime of wirelesssensor networks

Wireless sensor networks （WSN） provide an approachto collecting distributed monitoring data and transmiting them tothe sink node. This paper proposes a WSN-based multi-hop networkinfrastructure, to increase network lifetime by optimizing therouting strategy. First, a network model is established, an operatingcontrol strategy is devised, and energy consumption characteristicsare analyzed. Second, a fast route-planning algorithm isproposed to obtain the original path that takes into account the remainingenergy of communicating nodes and the amount of energyconsumed in data transmission. Next, considering the amount ofenergy consumed by an individual node and the entire network,a criterion function is established to describe node performanceand to evaluate data transmission ability. Finally, a route optimizingalgorithm is proposed to increase network lifetime by adjusting thetransmission route in protection of the weak node （the node withlow transmission ability）. Simulation and comparison experimentalresults demonstrate the good performance of the proposed algorithmsto increase network lifetime.

无人机协助WSNs中的节能数据采集策略

在无线传感器网络(WSNs)中,无人机(UAV)已经成为一种有效的数据采集手段,但在采集过程中产生的大量能耗严重降低WSNs的寿命。针对WSNs中数据采集能量消耗大的问题,设计了一种基于传感器节点(SNs)唤醒调度和无人机轨迹设计的节能数据采集策略。基于该方案,构建了无人机与SNs能耗加权和最小化问题,该问题被描述为混合整数非线性规划(MINLP)问题,通常难以求解。针对该问题,提出一种基于路径离散化和连续凸逼近的联合优化算法,得到满足KKT条件的局部最优解。实验结果表明,所提出的方案能实现显著的网络节能,且能实现无人机能耗与SNs能耗之间的灵活权衡。

基于全神经网络增强算法的WSNs故障预警与检测

针对现有WSNs故障检测算法存在的故障分类检测率低、耗时长、节点能耗控制差等问题,提出一种全神经网络增强故障预警与检测算法;全神经网络的神经元节点与临近层的节点连接,形成具有强大故障数据训练功能的深度网络结构,选择平滑性更好的sigmoid函数作为模型的激活函数,并基于感知机合理调节相邻两个隐含层之间的阈值权重,降低模型的训练损失;采用Adam优化算法抑制模型的梯度膨胀和梯度消失等异常情况,并消除训练中产生的数据冗余,以降低故障数据训练中产生的虚预警;实验结果显示:提出算法的总体故障检测率和不同类型故障的分类检测率都优于传统算法,此外全神经网络增强算法在节点故障检测耗时和能耗控制方面,也具有显著优势。

An Optimized Data Fusion Paradigm for WSN Based on Neural Networks

Wireless sensor networks(WSNs)have gotten a lot of attention as useful tools for gathering data.The energy problem has been a fundamental constraint and challenge faced by many WSN applications due to the size and cost constraints of the sensor nodes.This paper proposed a data fusion model based on the back propagation neural network(BPNN)model to address the problem of a large number of invalid or redundant data.Using three layeredbased BPNNs and a TEEN threshold,the proposed model describes the cluster structure and filters out unnecessary details.During the information transmission process,the neural network’s output function is used to deal with a large amount of sensing data,where the feature value of sensing data is extracted and transmitted to the sink node.In terms of life cycle,data traffic,and network use,simulation results show that the proposed data fusion model outperforms the traditional TEEN protocol.As a result,the proposed scheme increases the life cycle of the network thereby lowering energy usage and traffic.

基于自适应粒子群优化的WSNs非均匀分簇路由算法

针对无线传感器网络(WSNs)中存在的能量空洞问题,提出了一种基于自适应粒子群优化(PSO)的WSNs非均匀分簇路由(APSO-NUCR)算法。在簇首选举阶段,引入一个能量阈值从网络中筛选出候选簇首,候选簇首根据竞争半径构造规模大小不一的簇。引入PSO算法来确定簇首,建立基于节点能量和位置的适应度函数,根据适应度值来确定最终簇首。根据粒子当前适应度值计算惯性权重和学习因子,更新速度和位置,平衡算法的全局和局部搜索能力。在数据传输阶段,簇内采用单跳传输方式,簇间数据传输基于Prim最小生成树的最优多跳路径。仿真结果表明:APSO-NUCR算法在利用能量和均衡网络能耗方面有良好的性能,延长了网络寿命。

基于VGG-19和卡尔曼预处理的WSNs测距方法

提出了一种基于VGG-19网络和卡尔曼预处理的无线传感器网络(WSNs)测距方法。首先,通过卡尔曼滤波方法实现接收信号强度指示(RSSI)数据的滤波处理过程,以提高方法的稳定性;然后,利用VGG-19网络学习分析真实场景下拍摄的图像信息,以获得高精度的图像特征;最后,根据图像数据和实际信号衰减数据,自动提取不同地面环境下信号传播的高层语义特征,从而完成距离测量。基于实测平台获取的数据进行实验分析,结果表明:在4种复杂程度不一的场景下,所提模型的距离估计误差最大不超过1 m,并且测距耗时为34.92 ms,整体性能优于其他对比模型。

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.

基于混合特征学习数学模型的WSNs入侵检测研究

随着WSNs入侵方式的多样化,现有检测算法的准确性会显著下降,为此提出了基于混合特征学习数学模型的WSNs入侵检测方法。检测中先基于嗅探器定时获取传感器节点运行参数,并利用熵权法选取无线传感器网络节点混合特征因子,包括能耗率、丢包率、报文发送频率、报文接收频率,再从获取传感器节点运行参数中获得四个特征因子对应的数值。利用BP神经网络构建入侵检测数学模型,以混合特征因子数值为输入,确定WSNs入侵类型,以实现对WSNs入侵行为的精确检测。结果表明:基于强化学习的入侵检测模型,基于双向循环生成对抗网络的入侵检测模型相比,所研究模型应用下,F1-measure数值更大,由此说明该模型入侵检测更全面,检测结果更准确,模型的整体性能更好。

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和GSM的室内环境监测预警系统设计

针对传统监控系统的弊端,设计了一种基于无线传感器网络的实时室内环境监测预警系统,给出了系统的总体结构。系统以CC2430为无线传感器节点核心,对室内的CO体积分数、烟雾体积分数和温度进行实时采集和监测;以ARM微处理器S3C44B0X为控制核心,对采集的环境信息进行存储、分析和处理;利用SIM300模块实现数据的远程传输,当有火灾或者煤气泄漏发生时,用户通过手机及时接收报警信息。实验证明:该系统简单,经济,工作稳定,可以准确地进行室内的火灾和煤气泄漏的预警预报。

WSNs中的分簇式压缩感知

提出了基于无线传感器网络的分簇式压缩感知算法。首先研究了随机压缩采样技术在硬件资源受限的无线传感器网络节点中的技术解决方案。在此基础上提出了在簇头中生成随机采样序列并分发给簇成员,然后在簇成员中进行低速随机采样,最后在簇头中进行信号重构的分簇式压缩感知算法。声音信号的压缩感知实验表明,压缩感知技术能够在硬件资源受限的节点中实现,且提出的分簇式压缩感知算法在没有信息丢失的压缩采样下大大降低了通信能耗。

基于压缩感知的WSNs长生命周期数据收集方法

该文针对基于事件驱动的无线传感器网络(WSNs)数据收集查询的长期应用需求,基于压缩感知理论将混合压缩感知的数据收集技术与数据收集树的构建过程相结合,设计出一种长生命周期数据收集方法。该方法在数据收集查询到达时,构造一棵数据收集树。建树过程中,利用混合压缩感知思想,在分析转发节点和融合节点能耗的基础上,以收集查询后节点最小剩余能量最大化为目标,构造最大数据收集树集合。仿真实验表明,该方法能够充分利用节点能量资源,显著提高网络能量效率,达到延长网络生命周期的目标。

PSO优化的能耗均衡WSNs路由算法

无线传感器网络(WSNs)节点能量和计算能力有限,所以WSNs路由设计的重要目标是降低节点能源损耗。提出PSO优化的能耗均衡WSNs路由算法,用路径探测包收集链路状态信息,将路由优化计算在Sink节点处进行,用PSO进行路由优化,减少了节点负担。算法对PSO的运算规则结合路由问题进行了重新定义,在设计适用度函数时,考虑了节点的剩余能量,以达到能耗均衡。当网络有Qo S需求时,只需在路由探测包和适应度函数上加入相应参数即可,易于扩展。最后用仿真对算法的优越性进行了验证。

基于RSSI的WSNs加权质心定位算法的改进

针对无线传感器网络(WSNs)定位算法定位精度不高的问题,提出了一种基于RSSI测距的质心(Centroid)算法和加权质心(W-Centroid)定位算法相结合的新的定位方法 WR-Centroid。该算法主要通过RSSI测距得出4个参考节点到未知节点的距离,再任选3个距离为半径,以相应的参考节点为圆心画圆得到3个圆的交叠区域,构成一个三角形,求出这个三角形的质心。依照这种方法,求得4个质心坐标,利用加权质心定位算法求出未知节点的坐标。仿真结果表明:该算法比加权质心定位算法精度有很大的提高。

基于熵权法的WSNs性能指标权重决策方法

针对无线传感器网络(WSNs)性能评价中多元指标权重难以确定的问题,提出了利用熵权法对多评价指标因子赋权的权重决策方法,建立熵权法权重决策模型对网络性能指标进行客观评价。运用Opnet网络仿真软件,通过改变节点规模获取不同网络性能参数数据样本。数据分析表明:信息熵能够客观准确地反映各指标贡献信息量的大小,基于熵权的权重决策方法能够客观反映不同网络性能指标的相对重要程度。同时,该方法灵活性好,适应性强,可广泛地应用于其他类型网络的性能指标重要程度的评估。