Accelerated Particle Swarm Optimization Algorithm for Efficient Cluster Head Selection in WSN

Numerous wireless networks have emerged that can be used for short communication ranges where the infrastructure-based networks may fail because of their installation and cost.One of them is a sensor network with embedded sensors working as the primary nodes,termed Wireless Sensor Networks(WSNs),in which numerous sensors are connected to at least one Base Station(BS).These sensors gather information from the environment and transmit it to a BS or gathering location.WSNs have several challenges,including throughput,energy usage,and network lifetime concerns.Different strategies have been applied to get over these restrictions.Clustering may,therefore,be thought of as the best way to solve such issues.Consequently,it is crucial to analyze effective Cluster Head(CH)selection to maximize efficiency throughput,extend the network lifetime,and minimize energy consumption.This paper proposed an Accelerated Particle Swarm Optimization(APSO)algorithm based on the Low Energy Adaptive Clustering Hierarchy(LEACH),Neighboring Based Energy Efficient Routing(NBEER),Cooperative Energy Efficient Routing(CEER),and Cooperative Relay Neighboring Based Energy Efficient Routing(CR-NBEER)techniques.With the help of APSO in the implementation of the WSN,the main methodology of this article has taken place.The simulation findings in this study demonstrated that the suggested approach uses less energy,with respective energy consumption ranges of 0.1441 to 0.013 for 5 CH,1.003 to 0.0521 for 10 CH,and 0.1734 to 0.0911 for 15 CH.The sending packets ratio was also raised for all three CH selection scenarios,increasing from 659 to 1730.The number of dead nodes likewise dropped for the given combination,falling between 71 and 66.The network lifetime was deemed to have risen based on the results found.A hybrid with a few valuable parameters can further improve the suggested APSO-based protocol.Similar to underwater,WSN can make use of the proposed protocol.The overall results have been evaluated and compared with the existing approaches of sensor networks.

Cluster Head Selection Algorithm for UAV Assisted Clustered IoT Network Utilizing Blockchain

To guarantee the security of Internet of Things(IoT)devices,the blockchain tech⁃nology is often applied to clustered IoT networks.However,cluster heads(CHs)need to un⁃dertake additional control tasks.For battery-powered IoT devices,the conventional CH se⁃lection algorithm is limited.Based on the above problem,an unmanned aerial vehicle(UAV)network assisted clustered IoT system is proposed,and a corresponding UAV CH se⁃lection algorithm is designed.In this scheme,UAVs are selected as CHs to serve IoT clus⁃ters.The proposed CH selection algorithm considers the maximal transmit power,residual energy and distance information of UAVs,which can greatly extend the working life of IoT clusters.Through Monte Carlo simulation,the key performance indexes of the system,in⁃cluding energy consumption,average secrecy rate and the maximal number of data packets received by the base station(BS),are evaluated.The simulation results show that the pro⁃posed algorithm has great advantages compared with the existing CH selection algorithms.

Optimization of Head Cluster Selection in WSN by Human-Based Optimization Techniques

Wireless sensor networks(WSNs)are characterized by their ability to monitor physical or chemical phenomena in a static or dynamic location by collecting data,and transmit it in a collaborative manner to one or more processing centers wirelessly using a routing protocol.Energy dissipation is one of the most challenging issues due to the limited power supply at the sensor node.All routing protocols are large consumers of energy,as they represent the main source of energy cost through data exchange operation.Clusterbased hierarchical routing algorithms are known for their good performance in energy conservation during active data exchange in WSNs.The most common of this type of protocol is the Low-Energy Adaptive Clustering Hierarchy(LEACH),which suffers from the problem of the pseudo-random selection of cluster head resulting in large power dissipation.This critical issue can be addressed by using an optimization algorithm to improve the LEACH cluster heads selection process,thus increasing the network lifespan.This paper proposes the LEACH-CHIO,a centralized cluster-based energyaware protocol based on the Coronavirus Herd Immunity Optimizer(CHIO)algorithm.CHIO is a newly emerging human-based optimization algorithm that is expected to achieve significant improvement in the LEACH cluster heads selection process.LEACH-CHIO is implemented and its performance is verified by simulating different wireless sensor network scenarios,which consist of a variable number of nodes ranging from 20 to 100.To evaluate the algorithm performances,three evaluation indicators have been examined,namely,power consumption,number of live nodes,and number of incoming packets.The simulation results demonstrated the superiority of the proposed protocol over basic LEACH protocol for the three indicators.

Type II Fuzzy Logic Based Cluster Head Selection for Wireless Sensor Network

Wireless Sensor Network(WSN)forms an essential part of IoT.It is embedded in the target environment to observe the physical parameters based on the type of application.Sensor nodes inWSN are constrained by different features such as memory,bandwidth,energy,and its processing capabilities.In WSN,data transmission process consumes the maximum amount of energy than sensing and processing of the sensors.So,diverse clustering and data aggregation techniques are designed to achieve excellent energy efficiency in WSN.In this view,the current research article presents a novel Type II Fuzzy Logic-based Cluster Head selection with Low Complexity Data Aggregation(T2FLCH-LCDA)technique for WSN.The presented model involves a two-stage process such as clustering and data aggregation.Initially,three input parameters such as residual energy,distance to Base Station(BS),and node centrality are used in T2FLCH technique for CH selection and cluster construction.Besides,the LCDA technique which follows Dictionary Based Encoding(DBE)process is used to perform the data aggregation at CHs.Finally,the aggregated data is transmitted to the BS where it achieves energy efficiency.The experimental validation of the T2FLCH-LCDAtechnique was executed under three different scenarios based on the position of BS.The experimental results revealed that the T2FLCH-LCDA technique achieved maximum energy efficiency,lifetime,Compression Ratio(CR),and power saving than the compared methods.

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.

AN EFFICIENT UE CLUSTER HEAD SELECTION ALGORITHM IN WIRELESS SENSOR NETWORKS AND CELLULAR NETWORKS

Wireless Sensor Networks (WSNs) have been applied in many different areas. Energy efficient algorithms and protocols have become one of the most challenging issues for WSN. Many researchers focused on developing energy efficient clustering algorithms for WSN, but less research has been concerned in the mobile User Equipment (UE) acting as a Cluster Head (CH) for data transmission between cellular networks and WSNs. In this paper, we propose a cellular-assisted UE CH selection algorithm for the WSN, which considers several parameters to choose the optimal UE gateway CH. We analyze the energy cost of data transmission from a sensor node to the next node or gateway and calculate the whole system energy cost for a WSN. Simulation results show that better system performance, in terms of system energy cost and WSNs life time, can be achieved by using interactive optimization with cellular networks.

A Novel Improved Artificial Bee Colony and Blockchain-Based Secure Clustering Routing Scheme for FANET

Flying Ad hoc Network(FANET)has drawn significant consideration due to its rapid advancements and extensive use in civil applications.However,the characteristics of FANET including high mobility,limited resources,and distributed nature,have posed a new challenge to develop a secure and ef-ficient routing scheme for FANET.To overcome these challenges,this paper proposes a novel cluster based secure routing scheme,which aims to solve the routing and data security problem of FANET.In this scheme,the optimal cluster head selection is based on residual energy,online time,reputation,blockchain transactions,mobility,and connectivity by using Improved Artificial Bee Colony Optimization(IABC).The proposed IABC utilizes two different search equations for employee bee and onlooker bee to enhance convergence rate and exploitation abilities.Further,a lightweight blockchain consensus algorithm,AI-Proof of Witness Consensus Algorithm(AI-PoWCA)is proposed,which utilizes the optimal cluster head for mining.In AI-PoWCA,the concept of the witness for block verification is also involved to make the proposed scheme resource efficient and highly resilient against 51%attack.Simulation results demonstrate that the proposed scheme outperforms its counterparts and achieves up to 90%packet delivery ratio,lowest end-to-end delay,highest throughput,resilience against security attacks,and superior in block processing time.

A Novel Energy Aware Clustering Technique for Routing in Wireless Sensor Networks

Cluster-based architectures are one of the most practical solutions in order to cope with the requirements of large-scale wireless sensor networks (WSN). Cluster-head election problem is one of the basic QoS requirements of WSNs, yet this problem has not been sufficiently explored in the context of cluster-based sensor networks. Specifically, it is not known how to select the best candidates for the cluster head roles. In this paper, we investigate the cluster head election problem, specifically concentrating on applications where the energy of full network is the main requirement, and we propose a new approach to exploit efficiently the network energy, by reducing the energy consumed for cluster forming.

Enhanced Archimedes Optimization Algorithm for Clustered Wireless Sensor Networks

Wireless sensor networks(WSN)encompass a set of inexpensive and battery powered sensor nodes,commonly employed for data gathering and tracking applications.Optimal energy utilization of the nodes in WSN is essential to capture data effectively and transmit them to destination.The latest developments of energy efficient clustering techniques can be widely applied to accomplish energy efficiency in the network.In this aspect,this paper presents an enhanced Archimedes optimization based cluster head selection(EAOA-CHS)approach for WSN.The goal of the EAOA-CHS method is to optimally choose the CHs from the available nodes in WSN and then organize the nodes into a set of clusters.Besides,the EAOA is derived by the incorporation of the chaotic map and pseudo-random performance.Moreover,the EAOA-CHS technique determines a fitness function involving total energy consumption and lifetime of WSN.The design of EAOA for CH election in the WSN depicts the novelty of work.In order to exhibit the enhanced efficiency of EAOA-CHS technique,a set of simulations are applied on 3 distinct conditions dependent upon the place of base station(BS).The simulation results pointed out the better outcomes of the EAOA-CHS technique over the recent methods under all scenarios.

A LEACH-Head Expected Frequency Appraisal Algorithm for Water-Environment Monitoring Networks

Water-environment monitoring network (WMN) is a wireless sensor network based real-time system, which collects, transmits, analyzes and processes water-environment parameters in large area. Both cluster selection mechanisms and energy saving strategies play an important role on designing network routing protocols for the WMN. Since those existing routing algorithms can not be used directly in the WMN, we thus propose an improved version of LEACH, a LEACH-Head Expected Frequency Appraisal (LEACH-HEFA) algorithm, for the WMN in this paper. Simulation results show that the LEACH-HEFA can balance the energy consumption of nodes, rationalize the clustering process and prolong the network lifetime significantly in the WMN. It indicates that the LEACH-HEFA is suitable to the WMN.

基于节点信任的LEACH协议簇头选举改进算法

针对LEACH协议在选举簇头时没有考虑到节点的信任值和节点的剩余能量,提出了一种新的簇头选举改进算法,即在数据传输阶段同时计算节点的信任值,为下一轮选举簇头节点作参考,从而在相对延长了网络寿命的同时确保了数据的可靠性,加强了网络的安全。由于在计算节点的信任值过程中已包含节点,故不需要额外考虑节点的剩余能量。仿真结果表明,改进后的算法不仅在网络寿命上要优于LEACH算法,同时在安全性能分析上也优于LEACH协议。

无线传感器网络LEACH协议的改进

延长传感器网络的生存周期,降低传感器节点的能耗成为无线传感器网络(WSN)研究的重点。基于LEACH协议中簇头生成算法提出了改进、调整节点竞选簇头的阀值函数,并在非簇头节点选择适合自己的最优簇头时综合考虑了候选簇头节点的剩余能量以及距基站的距离等因素,给出了新的成簇机制的适合因子的计算公式。仿真结果表明,新型簇首选择机制能够有效平衡节点的能耗分布,延长节点与网络的寿命。

基于LEACH协议的能耗均衡路由算法

分析LEACH协议,指出其在簇头选择、簇头与基站通信方面存在的不足。针对上述问题,以均衡能耗和节省能量为出发点,结合LEACH-C的特点及Dijkstra算法对LEACH协议进行改进,提出LEACH-EB协议。仿真结果表明,LEACH-EB协议能有效节省能量,均衡能量消耗,延长网络生命周期。

一种基于节点剩余能量和位置的LEACH改进算法

针对典型的分簇路由协议LEACH随机选择簇头和成簇后没有考虑簇内成员节点位置的问题,提出了LEACH-ED算法。该算法对簇头的选择机制进行了优化,既考虑了节点能量,又能同时判断簇内成员节点与sink节点的距离。当某些簇内成员节点与sink节点距离较近时,不需经过簇头节点,而是直接和sink节点进行信息传递。仿真结果表明,改进的LEACH-ED算法网络生命周期与LEACH协议相比提高了近50%,同时网络中节点传输数据的能力也得到大幅度提高。

无线传感器网络路由协议LEACH的算法分析

基于LEACH路由算法,分析了无线传感器网络分簇路由机制,重点分析了LEACH及其两个改进算法LEACH-EI和LEACH-EA。用Matlab平台对LEACH算法和改进后的算法进行仿真分析,结果表明:改进后的算法在延长网络生命周期和减少能量消耗上比LEACH算法有了很大改善,LEACH-EI算法适用于比较小型的、且运行时间较短的网络,而LEACH-EA算法则适用于比较大型的网络。

无线传感器网络能耗均衡LEACH路由算法

针对经典LEACH路由协议选取簇头节点数量的随机性和分布不均匀性,提出了一种改进型的LEACH算法。通过考虑簇内节点的主要能量消耗以及引入节点剩余能量、节点消耗能量速度、网络平均剩余能量和网络平均消耗能量速度四个参数,以便合理地选择簇头数目和簇头节点,从而有效地降低能量消耗,延长网络生命周期。仿真结果表明,改进后的算法在一定程度上实现了簇头节点的能耗均衡,网络能耗降低了14.7%,网络生命周期延长了17.3%。

WSN中LEACH路由协议的改进及仿真

针对低功耗自适应聚类(LEACH)路由协议中簇头节点在空间上分布不均以及在远距离数据传输过程中能量消耗过多等不足,提出一种改进的LEACH路由协议LEACH-ZMH,其中采用基于区域的簇头选择和簇间多跳数据传输的方法。给出LEACH-ZMH的正确性证明和复杂性分析。NS2仿真表明,改进的协议有效延长了网络的存活时间,性能优于LEACH协议。

LEACH协议的簇首多跳与选择优化

针对WSN层次型路由协议中簇首单跳传输数据至汇聚节点、而部分簇首因传输距离过长导致能量过早耗尽从而影响整个网络寿命问题,提出了基于剩余能量对簇首优化选择和簇首至汇聚节点间多跳数据传输的改进算法.对首个节点死亡轮数和能量图像方面进行了分析和仿真,结果表明该改进算法可使全网负载更加均衡,并进一步延长了网络整体寿命.

一种基于LEACH的无线传感器路由算法及其仿真

针对LEACH协议的不足之处做了改进。改进算法通过考虑节点的剩余能量和簇首的平均能耗的关系来优化簇首的选举,采用次簇首机制传输数据。仿真结果显示,改进算法能够很好地均衡网络能量损耗分布,减少能量消耗,有效延长网络生存时间。

LEACH协议簇头选择算法的改进

LEACH协议存在簇头节点个数和位置分布不稳定的现象。在改进的LEACH-H协议在簇头节点的选举过程中,充分考虑了簇头节点剩余能量因素,设定了簇头的能量阀值,防止了低能量的节点成为簇头。在此基础上引进簇头调整过程,该过程通过排除紧密邻居簇头和增加必要的簇头,在一定程度上解决了LEACH协议存在的问题,从而达到均衡网络能量消耗,延长生存期的目的。网络仿真证明了新算法的可行性。