A Novel Approach to Energy Optimization:Efficient Path Selection in Wireless Sensor Networks with Hybrid ANN

In pursuit of enhancing the Wireless Sensor Networks(WSNs)energy efficiency and operational lifespan,this paper delves into the domain of energy-efficient routing protocols.InWSNs,the limited energy resources of Sensor Nodes(SNs)are a big challenge for ensuring their efficient and reliable operation.WSN data gathering involves the utilization of a mobile sink(MS)to mitigate the energy consumption problem through periodic network traversal.The mobile sink(MS)strategy minimizes energy consumption and latency by visiting the fewest nodes or predetermined locations called rendezvous points(RPs)instead of all cluster heads(CHs).CHs subsequently transmit packets to neighboring RPs.The unique determination of this study is the shortest path to reach RPs.As the mobile sink(MS)concept has emerged as a promising solution to the energy consumption problem in WSNs,caused by multi-hop data collection with static sinks.In this study,we proposed two novel hybrid algorithms,namely“ Reduced k-means based on Artificial Neural Network”(RkM-ANN)and“Delay Bound Reduced kmeans with ANN”(DBRkM-ANN)for designing a fast,efficient,and most proficient MS path depending upon rendezvous points(RPs).The first algorithm optimizes the MS’s latency,while the second considers the designing of delay-bound paths,also defined as the number of paths with delay over bound for the MS.Both methods use a weight function and k-means clustering to choose RPs in a way that maximizes efficiency and guarantees network-wide coverage.In addition,a method of using MS scheduling for efficient data collection is provided.Extensive simulations and comparisons to several existing algorithms have shown the effectiveness of the suggested methodologies over a wide range of performance indicators.

Enabling Efficient Data Transmission in Wireless Sensor Networks-Based IoT Application

The use of the Internet of Things(IoT)is expanding at an unprecedented scale in many critical applications due to the ability to interconnect and utilize a plethora of wide range of devices.In critical infrastructure domains like oil and gas supply,intelligent transportation,power grids,and autonomous agriculture,it is essential to guarantee the confidentiality,integrity,and authenticity of data collected and exchanged.However,the limited resources coupled with the heterogeneity of IoT devices make it inefficient or sometimes infeasible to achieve secure data transmission using traditional cryptographic techniques.Consequently,designing a lightweight secure data transmission scheme is becoming essential.In this article,we propose lightweight secure data transmission(LSDT)scheme for IoT environments.LSDT consists of three phases and utilizes an effective combination of symmetric keys and the Elliptic Curve Menezes-Qu-Vanstone asymmetric key agreement protocol.We design the simulation environment and experiments to evaluate the performance of the LSDT scheme in terms of communication and computation costs.Security and performance analysis indicates that the LSDT scheme is secure,suitable for IoT applications,and performs better in comparison to other related security schemes.

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.

A blockchain-empowered authentication scheme for worm detection in wireless sensor network

Wireless Sensor Network(WSN)is a distributed sensor network composed a large number of nodes with low cost,low performance and self-management.The special structure of WSN brings both convenience and vulnerability.For example,a malicious participant can launch attacks by capturing a physical device.Therefore,node authentication that can resist malicious attacks is very important to network security.Recently,blockchain technology has shown the potential to enhance the security of the Internet of Things(IoT).In this paper,we propose a Blockchain-empowered Authentication Scheme(BAS)for WSN.In our scheme,all nodes are managed by utilizing the identity information stored on the blockchain.Besides,the simulation experiment about worm detection is executed on BAS,and the security is evaluated from detection and infection rate.The experiment results indicate that the proposed scheme can effectively inhibit the spread and infection of worms in the network.

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。

Comparison of Energy Harvesting Systems for Wireless Sensor Networks

Wireless sensor networks (WSNs) offer an attractive solution to many environmental,security,and process monitoring problems.However,one barrier to their fuller adoption is the need to supply electrical power over extended periods of time without the need for dedicated wiring.Energy harvesting provides a potential solution to this problem in many applications.This paper reviews the characteristics and energy requirements of typical sensor network nodes,assesses a range of potential ambient energy sources,and outlines the characteristics of a wide range of energy conversion devices.It then proposes a method to compare these diverse sources and conversion mechanisms in terms of their normalised power density.

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。

Clustering routing algorithm of wireless sensor networks based on Bayesian game

To avoid uneven energy consuming in wireless sen- sor networks, a clustering routing model is proposed based on a Bayesian game. In the model, Harsanyi transformation is introduced to convert a static game of incomplete information to the static game of complete but imperfect information. In addition, the existence of Bayesian nash equilibrium is proved. A clustering routing algorithm is also designed according to the proposed model, both cluster head distribution and residual energy are considered in the design of the algorithm. Simulation results show that the algorithm can balance network load, save energy and prolong network lifetime effectively.

An Approach of Distributed Joint Optimization for Cluster-based Wireless Sensor Networks

Wireless sensor networks(WSNs) are energyconstrained,so energy saving is one of the most important issues in typical applications.The clustered WSN topology is considered in this paper.To achieve the balance of energy consumption and utility of network resources,we explicitly model and factor the effect of power and rate.A novel joint optimization model is proposed with the protection for cluster head.By the mean of a choice of two appropriate sub-utility functions,the distributed iterative algorithm is obtained.The convergence of the proposed iterative algorithm is proved analytically.We consider general dual decomposition method to realize variable separation and distributed computation,which is practical in large-scale sensor networks.Numerical results show that the proposed joint optimal algorithm converges to the optimal power allocation and rate transmission,and validate the performance in terms of prolonging of network lifetime and improvement of throughput.

TDOA-based Sybil attack detection scheme for wireless sensor networks

As wireless sensor networks （WSN） are deployed in fire monitoring, object tracking applications, security emerges as a central requirement. A case that Sybil node illegitimately reports messages to the master node with multiple non-existent identities （ID） will cause harmful effects on decision-making or resource allocation in these applications. In this paper, we present an efficient and lightweight solution for Sybil attack detection based on the time difference of arrival （TDOA） between the source node and beacon nodes. This solution can detect the existence of Sybil attacks, and locate the Sybil nodes. We demonstrate efficiency of the solution through experiments. The experiments show that this solution can detect all Sybil attack cases without missing.

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.

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。

Autoregressive moving average model as a multi-agent routing protocol for wireless sensor networks

A prediction-aided routing algorithm based on ant colony optimization mode （PRACO） to achieve energy-aware data-gathering routing structure in wireless sensor networks （WSN） is presented. We adopt autoregressive moving average model （ARMA） to predict dynamic tendency in data traffic and deduce the construction of load factor, which can help to reveal the future energy status of sensor in WSN. By checking the load factor in heuristic factor and guided by novel pheromone updating rule, multi-agent, i. e. , artificial ants, can adaptively foresee the local energy state of networks and the corresponding actions could be taken to enhance the energy efficiency in routing construction. Compared with some classic energy-saving routing schemes, the simulation results show that the proposed routing building scheme can ① effectively reinforce the robustness of routing structure by mining the temporal associability and introducing multi-agent optimization to balance the total energy cost for data transmission, ② minimize the total communication consumption, and ③prolong the lifetime of networks.

Research on Time Synchronization Method Under Arbitrary Network Delay in Wireless Sensor Networks

To cope with the arbitrariness of the network delays,a novel method,referred to as the composite particle filter approach based on variational Bayesian(VB-CPF),is proposed herein to estimate the clock skew and clock offset in wireless sensor networks.VB-CPF is an improvement of the Gaussian mixture kalman particle filter(GMKPF)algorithm.In GMKPF,Expectation-Maximization(EM)algorithm needs to determine the number of mixture components in advance,and it is easy to generate overfitting and underfitting.Variational Bayesian EM(VB-EM)algorithm is introduced in this paper to determine the number of mixture components adaptively according to the observations.Moreover,to solve the problem of data packet loss caused by unreliable links,we propose a robust time synchronization(RTS)method in this paper.RTS establishes an autoregressive model for clock skew,and calculates the clock parameters based on the established autoregressive model in case of packet loss.The final simulation results illustrate that VB-CPF yields much more accurate results relative to GMKPF when the network delays are modeled in terms of an asymmetric Gaussian distribution.Moreover,RTS shows good robustness to the continuous and random dropout of time messages.

Power-efficient topologies for wireless sensor networks with fixed communication range

The expectations for sensor networks are growing. The performance of wireless sensor networks （WSNs） is greatly influenced by their network topology. In this paper, we consider four patterned topologies that best support connectivity among these deployed sensor nodes in two-tiered WSNs. The theoretical and simulation results show that the triangle-based topology has smaller cell number, shorter maximum hop length, less total energy consumption, and better performance than other topologies. The analysis carried out in this paper could provide the guidelines for network deployment and protocol design in the future applications.

A consensus time synchronization protocol in wireless sensor network

Time synchronization is one of the base techniques in wireless sensor networks(WSNs).This paper proposes a novel time synchronization protocol which is a robust consensusbased algorithm in the existence of transmission delay and packet loss.It compensates for transmission delay and packet loss firstly,and then,estimates clock skew and clock offset in two steps.Simulation and experiment results show that the proposed protocol can keep synchronization error below 2μs in the grid network of 10 nodes or the random network of 90 nodes.Moreover,the synchronization accuracy in the proposed protocol can keep constant when the WSN works up to a month.

Cost Minimization of Wireless Sensor Networks with Unlimited-lifetime Energy for Monitoring Oil Pipelines

Cyber-physical-system（CPS） has been widely used in both civil and military applications.Wireless sensor network（WSN） as the part and parcel of CPS faces energy problem because sensors are battery powered,which results in limited lifetime of the network.To address this energy problem,we take advantage of energy harvesting device（EHD） and study how to indefinitely prolong oil pipeline monitoring network lifetime by reasonable selecting EHD.Firstly,we propose a general strategy worst case-energy balance strategy（WC-EBS）,which defines worst case energy consumption（WCEC） as the maximum energy sensor node could expend for oil pipeline monitoring WSN.When the energy collected by EHD is equal or greater than WCEC,network can have an unlimited lifetime.However,energy harvesting rate is proportional to the price of EHD,WC-EBS will cause high network cost.To reduce network cost,we present two optimization strategies,optimization workloadenergy balance strategy（OW-EBS） and optimization first nodeenergy balance strategy（OF-EBS）.The main idea of OW-EBS is to cut down WCEC by reducing critical node transmission workload;OF-EBS confirms critical node by optimizing each sensor node transmission range,then we get the optimal energy harvesting rate in OF-EBS.The experimental results demonstrate that OF-EBS can indefinitely extend network lifetime with lower cost than WC-EBS and OW-EBS,and energy harvesting rate P in each strategy satisfies Pof-EBS≤P（OW-EBS）≤PWC-EBS.