交通路灯监控系统的无线传感网链状路由算法

在交通路灯监控系统中为节省网络节点能耗和降低数据传输时延,提出一种无线传感网链状路由算法（CRASMS）。该算法根据节点和监控区域的信息将监控区域分成若干个簇区域,在每一个簇区域中依次循环选择某个节点为簇头节点,通过簇头节点和传感节点的通信建立簇内星型网络,最终簇头节点接收传感节点数据,采用数据融合算法降低数据冗余,通过簇头节点间的多跳路由将数据传输到Sink节点并将用户端的指令传输到被控节点。仿真结果表明：CRASMS算法保持了PEGASIS算法在节点能耗方面和LEACH算法在传输时延方面的优点,克服了PEGASIS 算法在传输时延方面和LEACH算法在节点能耗方面的不足,将网络平均节点能耗和平均数据传输时延保持在较低水平。在一定的条件下,CRASMS算法比LEACH和PEGASIS算法更优。     

Hybrid Chameleon Search and Remora Optimization Algorithm-based Dynamic Heterogeneous load balancing clustering protocol for extending the lifetime of wireless sensor networks

Clustering is an indispensable strategy that helps towards the extension of lifetime of each sensor nodes with energy stability in wireless sensor networks (WSNs). This clustering process aids in sustaining energy efficiency and extended network lifetime in sensitive and critical real-life applications that include landslide monitoring and military applications. The dynamic characteristics of WSNs and several cluster configurations introduce challenge in the process of searching an ideal network structure, a herculean challenge. In this paper, Hybrid Chameleon Search and Remora Optimization Algorithm-based Dynamic Clustering Method (HCSROA) is proposed for dynamic optimization of wireless sensor node clusters. It utilized the global searching process of Chameleon Search Algorithm for selecting potential cluster head (CH) selection with balanced trade-off between intensification and extensification. It determines an ideal dynamic network structure based on factors that include quantity of nodes in the neighborhood, distance to sink, predictable energy utilization rate, and residual energy into account during the formulation of fitness function. It specifically achieved sink node mobility through the integration of the local searching capability of Improved Remora Optimization Algorithm for determining the optimal points of deployment over which the packets can be forwarded from the CH of the cluster to the sink node. This proposed HCSROA scheme compared in contrast to standard methods is identified to greatly prolong network lifetime by 29.21% and maintain energy stability by 25.64% in contrast to baseline protocols taken for investigation.     

基于移动Agent和WSN的突发事件场景数据收集算法研究

该文针对无线传感器网络应用于突发事件监测场景的能量消耗和网络延迟问题,提出了基于移动Agent的无线传感器网络簇式数据收集算法.动态成簇过程基于事件严重程度,并由其决定簇的生命周期和覆盖范围.Sink和簇头之间形成以Sink节点为簇头的虚拟簇.移动Agent迁移路径规划过程中下一跳节点的选取基于节点剩余能量、路径损耗及受刺激强度.移动Agent通过节点遍历的方式完成对所有簇内成员节点信息的收集.仿真结果表明,相对于C/S数据收集模型,基于移动Agent的模型具有更好的节能效果,并能一定程度地减少网络延迟,尤其适用于大规模无线传感器网络应用.     

A particle swarm optimization based energy efficient cluster head selection algorithm for wireless sensor networks

Clustering has been proven to be one of the most efficient techniques for saving energy of wireless sensor networks (WSNs). However, in a hierarchical cluster based WSN, cluster heads (CHs) consume more energy due to extra overload for receiving and aggregating the data from their member sensor nodes and transmitting the aggregated data to the base station. Therefore, the proper selection of CHs plays vital role to conserve the energy of sensor nodes for prolonging the lifetime of WSNs. In this paper, we propose an energy efficient cluster head selection algorithm which is based on particle swarm optimization (PSO) called PSO-ECHS. The algorithm is developed with an efficient scheme of particle encoding and fitness function. For the energy efficiency of the proposed PSO approach, we consider various parameters such as intra-cluster distance, sink distance and residual energy of sensor nodes. We also present cluster formation in which non-cluster head sensor nodes join their CHs based on derived weight function. The algorithm is tested extensively on various scenarios of WSNs, varying number of sensor nodes and the CHs. The results are compared with some existing algorithms to demonstrate the superiority of the proposed algorithm.     

A hybrid elephant herding optimization and cultural algorithm for energy‐balanced cluster head selection scheme to extend the lifetime in WSNs

Clustering‐based optimal cluster head selection in wireless sensor networks (WSNs) is considered as the efficient technique essential for improving the network lifetime. But enforcing optimal cluster head selection based on energy stabilization, reduced delay, and minimized distance between sensor nodes always remain a crucial challenge for prolonging the network lifetime in WSNs. In this paper, a hybrid elephant herding optimization and cultural algorithm for optimal cluster head selection (HEHO‐CA‐OCHS) scheme is proposed to extend the lifetime. This proposed HEHO‐CA‐OCHS scheme utilizes the merits of belief space framed by the cultural algorithm for defining a separating operator that is potent in constructing new local optimal solutions in the search space. Further, the inclusion of belief space aids in maintaining the balance between an optimal exploitation and exploration process with enhanced search capabilities under optimal cluster head selection. This proposed HEHO‐CA‐OCHS scheme improves the characteristic properties of the algorithm by incorporating separating and clan updating operators for effective selection of cluster head with the view to increase the lifetime of the network. The simulation results of the proposed HEHO‐CA‐OCHS scheme were estimated to be superior in percentage of alive nodes by 11.21%, percentage of dead nodes by 13.84%, residual energy by 16.38%, throughput by 13.94%, and network lifetime by 19.42% compared to the benchmarked cluster head selection schemes.     

无线传感器网络能量高效混合MAC算法

针对无线传感器网络MAC协议中存在的能耗问题,提出了能量高效的无线传感器网络混合MAC(EEH-MAC)算法,采用基于TDMA机制的时槽系数动态调整簇内节点的时槽大小来降低数据的传输时延;同时,对部分不需要数据传输的节点不分配时槽来减少能耗;按簇内节点剩余能量系数形成时槽分配顺序来减少状态转换的能耗;在簇头之间采用CSMA/CA机制的随机分配策略进行通信.仿真结果表明,EEH-MAC协议能有效减少能耗并延长网络生命周期.     

基于BWAS的无线传感器网络静态分簇路由算法

为提高路径搜索效率,避免动态分簇较多的能量消耗,提出了基于最优-最差蚂蚁系统(BWAS)的无线传感器网络静态分簇路由算法.BWAS是对蚁群算法的改进,在路径搜寻过程中评价出最优最差蚂蚁,引入奖惩机制,加快了路径搜索速度.通过无线传感器网络静态分簇、簇内动态选举簇头,在簇头节点间运用BWAS算法搜寻从簇头节点到汇聚节点的多跳最优路径,能减少路径寻优能量消耗,实现均衡能量管理,延长网络寿命,且具有较强的鲁棒性.通过与基于BWAS的动态分簇和基于蚁群算法的动态分簇路由的仿真实验相比较,证实了本算法的有效性.     

A function node-based Multiple Pairwise Keys Management protocol for Wireless Sensor Networks and energy consumption analysis

In this letter, a Function node-based Multiple Pairwise Keys Management (MPKMF)protocol for Wireless Sensor Networks (WSNs) is firstly designed, in which ordinary nodes and cluster head nodes are responsible for data collection and transmission, and function nodes are responsible for key management. There are more than one function nodes in the cluster consulting the key generation and other security decision-making. The function nodes are the second-class security center because of the characteristics of the distributed WSNs. Secondly, It is also described that the formation of function nodes and cluster heads under the control of the former, and five kinds of keys, I.e., individual key,pairwise keys, cluster key, management key, and group key. Finally, performance analysis and ex-periments show that, the protocol is superior in communication and energy consumption. The delay of establishing the cluster key meets the requirements, and a multiple pairwise key which adopts the coordinated security authentication scheme is provided.     

Distributed fuzzy logic based energy‐aware and coverage preserving unequal clustering algorithm for wireless sensor networks

In an energy‐constrained wireless sensor networks (WSNs), clustering is found to be an effective strategy to minimize the energy depletion of sensor nodes. In clustered WSNs, network is partitioned into set of clusters, each having a coordinator called cluster head (CH), which collects data from its cluster members and forwards it to the base station (BS) via other CHs. Clustered WSNs often suffer from the hot spot problem where CHs closer to the BS die much early because of high energy consumption contributed by the data forwarding load. Such death of nodes results coverage holes in the network very early. In most applications of WSNs, coverage preservation of the target area is a primary measure of quality of service. Considering the energy limitation of sensors, most of the clustering algorithms designed for WSNs focus on energy efficiency while ignoring the coverage requirement. In this paper, we propose a distributed clustering algorithm that uses fuzzy logic to establish a trade‐off between the energy efficiency and coverage requirement. This algorithm considers both energy and coverage parameters during cluster formation to maximize the coverage preservation of target area. Further, to deal with hot spot problem, it forms unequal sized clusters such that more CHs are available closer to BS to share the high data forwarding load. The performance of the proposed clustering algorithm is compared with some of the well‐known existing algorithms under different network scenarios. The simulation results validate the superiority of our algorithm in network lifetime, coverage preservation, and energy efficiency.     

A cluster-based power-efficient MAC scheme for event-driven sensing applications

In developing an architecture for wireless sensor networks (WSNs) that is extensible to hundreds of thousands of heterogeneous nodes, fundamental advances in energy efficient communication protocols must occur. In this paper, we first propose an energy-efficient and robust intra-cluster communication bit-map assisted (BMA) MAC protocol for large-scale cluster-based WSNs and then derive energy models for BMA, conventional TDMA, and energy efficient TDMA (E-TDMA) using two different approaches. We use simulation to validate these analytical models. BMA is intended for event-driven sensing applications, that is, sensor nodes forward data to the cluster head only if significant events are observed. It has low complexity and utilizes a dynamic scheduling scheme. Clustering is a promising distributing technique used in large-scale WSNs, and when combined with an appropriate MAC scheme, high energy efficiency can be achieved. The results indicate that BMA can improve the performance of wireless sensor networks by reducing energy expenditure and packet latency. The performance of BMA as an intra-cluster MAC scheme relative to E-TDMA depends on the sensor node traffic offer load and several other key system parameters. For most sensor-based applications, the values of these parameters can be constrained such that BMA provides enhanced performance.     

DUCR: Distributed unequal cluster‐based routing algorithm for heterogeneous wireless sensor networks

Designing energy efficient communication protocols for wireless sensor networks (WSNs) to conserve the sensors' energy is one of the prime concerns. Clustering in WSNs significantly reduces the energy consumption in which the nodes are organized in clusters, each having a cluster head (CH). The CHs collect data from their cluster members and transmit it to the base station via a single or multihop communication. The main issue in such mechanism is how to associate the nodes to CHs and how to route the data of CHs so that the overall load on CHs are balanced. Since the sensor nodes operate autonomously, the methods designed for WSNs should be of distributed nature, i.e., each node should run it using its local information only. Considering these issues, we propose a distributed multiobjective‐based clustering method to assign a sensor node to appropriate CH so that the load is balanced. We also propose an energy‐efficient routing algorithm to balance the relay load among the CHs. In case any CH dies, we propose a recovery strategy for its cluster members. All our proposed methods are completely distributed in nature. Simulation results demonstrate the efficiency of the proposed algorithm in terms of energy consumption and hence prolonging the network lifetime. We compare the performance of the proposed algorithm with some existing algorithms in terms of number of alive nodes, network lifetime, energy efficiency, and energy population.     

无线传感网络布局的虚拟力导向微粒群优化策略

无线传感网络通常由固定传感节点和少量移动传感节点构成,动态无线传感网络布局优化有利于提高无线传感网络覆盖率和目标检测概率,是无线传感网络研究的关键问题之一.传统的虚拟力算法在优化过程中容易受固定传感节点的影响,无法实现全局优化.本文结合虚拟力算法和微粒群算法,提出一种面向无线传感网络布局的虚拟力导向微粒群优化策略.该策略通过无线传感节点间的虚拟力影响微粒群算法的速度更新过程,指导微粒进化,加快算法收敛.实验表明,虚拟力导向微粒群优化策略能快速有效地实现无线传感节点布局优化.与微粒群算法和虚拟力算法相比,虚拟力导向微粒群优化策略不仅网络覆盖率高,且收敛速度快,耗时少.     

ROTEE: Remora Optimization and Tunicate swarm algorithm-based Energy-Efficient cluster-based routing for EH-enabled heterogeneous WSNs

Since the development of Wireless Sensor Networks (WSNs), the limited battery of the sensor nodes has been an unavoidable concern. Hence, to keep the WSNs operational for a longer possible duration, the recharging of node's battery through harvesting the ambient energy from surroundings (for an example, solar energy) has been proposed. In this work, we focus not only on utilizing the energy harvesting (EH)-enabled sensor nodes for routing purposes but also introduce a novel hybrid optimization ROATSA that uses Remora Optimization Algorithm (ROA) and Tunicate Swarm Algorithm (TSA) for energy-efficient cluster-based routing. The proposed work is termed as ROA and TSA-based Energy-Efficient Cluster-based Routing for EH-enabled WSN (ROTEE). Hybrid ROATSA is chosen due to enhanced convergence and exploitation capabilities. To reduce the financial burden on the network, we use only four EH-enabled nodes and locate them at each periphery of the network, equidistant to each other and the other nodes are 3-level energy heterogeneous sensor nodes. The selection of cluster head (CH) is optimized through ROATSA by considering profile index of each node by evaluating them at energy, distance, load balancing, node density, the delay involved, and network's average energy. The proposed work ROTEE shows supreme performance against the recently proposed clustering techniques.     

Self-attention based generative adversarial network with Aquila optimization algorithm espoused energy aware cluster head selection in WSN

WSNs have a wide range of applications, and the effective Wireless Sensor Network (WSN) design includes the best energy optimization techniques. The nodes in wireless sensor networks run on batteries. The existing cluster head selection methods do not take into account the latency and rate of wireless network traffic when optimizing the node's energy constraints. To overcome these issues, a self-attention based generative adversarial network (SabGAN) with Aquila Optimization Algorithm (AqOA) is proposed for Multi-Objective Cluster Head Selection and Energy Aware Routing (SabGAN-AqOA-EgAwR-WSN) for secured data transmission in wireless sensor network. The proposed method implements the routing process through cluster head. SabGAN classifiers are utilized to select the CH based on firm fitness functions, including delay, detachment, energy, cluster density, and traffic rate. After the selection of the cluster head, the malicious node gains access to the cluster. Therefore, the ideal path selection is carried out by three parameters: trust, connectivity, and degree of amenity. These three parameters are optimized under proposed AqOA. The data are transferred to the base station with the support of optimum trust path. The proposed SabGAN-AqOA-EgAwR-WSN method is activated in NS2 simulator. Finally, the proposed SabGAN-AqOA-EgAwR-WSN method attains 12.5%, 32.5%, 59.5%, and 32.65% higher alive nodes; 85.71%, 81.25%, 82.63%, and 71.96% lower delay; and 52.25%, 61.65%, 37.83%, and 20.63% higher normalized network energy compared with the existing methods.     

Energy‐Efficient Adaptive Dynamic Sensor Scheduling for Target Monitoring in Wireless Sensor Networks

Due to uncertainties in target motion and randomness of deployed sensor nodes, the problem of imbalance of energy consumption arises from sensor scheduling. This paper presents an energy‐efficient adaptive sensor scheduling for a target monitoring algorithm in a local monitoring region of wireless sensor networks. Owing to excessive scheduling of an individual node, one node with a high value generated by a decision function is preferentially selected as a tasking node to balance the local energy consumption of a dynamic clustering, and the node with the highest value is chosen as the cluster head. Others with lower ones are in reserve. In addition, an optimization problem is derived to satisfy the problem of sensor scheduling subject to the joint detection probability for tasking sensors. Particles of the target in particle filter algorithm are resampled for a higher tracking accuracy. Simulation results show this algorithm can improve the required tracking accuracy, and nodes are efficiently scheduled. Hence, there is a 41.67% savings in energy consumption.     

Squirrel Search Optimization-Based Cluster Head Selection Technique for Prolonging Lifetime in WSN’s

The rapid advancement of technologies of wireless sensor network is gaining maximized attentioned across the scientific community due to its reliable coverage in real life applications. It has evolved as an indispensable technology with diverisifed capabilities as it facilitates potential information to the end users regarding a region of target under real time monitoring process. However, the characteristics of WSNs such as resource-constrained nature and infrastructure-less deployment has the possibility of introducing diversified problems that influences the network performance. Moreover, the process of handling the issues of suitable cluster head selection, energy stability and network lifetime improvement are still considered as herculean task of concern. In this paper, a Squirrel Search Optimization-based Cluster Head Selection Technique (SSO-CHST) is proposed for prolonging the lifetime in the sensor networks by utilizing a gliding factor that aids in the better determination of cluster head selection during the process of data aggregation and dissemination. It estimates the fitness value of sensor nodes and arranges them in ascending order, such that the node with least fitness value is identified as the cluster memner. On the other hand, the sensor nodes with high fitness value is confirmed as the potential cluster head. The simulation results of the proposed SSO-CHST with minimum number of rounds used for selecting cluster head confirmed better throughput of 13.48% and improved network lifetime of 17.92% with minimized energy consumptions of 15.29%, remarkable to the benchmarked schemes.

     

自适应多传感器网络覆盖节点部署技术研究

基于多传感器节点圆形数据采集区域相切形成一系列相似三角形在几何原理上的有效性和可行性,提出了一种基于相似三角形的网络节点协同控制节点部署技术.首先把数据采集区域分割为多个几何相似的三角形,保证对监测区域无缝覆盖的同时聚集最少的传感器节点;其次在簇首节点与分簇区域内的传感器节点之间构建高效拓扑;然后建立一种自适应节点失效重建网络拓扑控制机制.仿真实验表明,该节点部署算法可以对监测区域实现完全无缝覆盖,保持网络整体性能高效以及数据传输的可靠性..     

无线传感器网络簇间节能路由算法

针对基于分簇网络的无线传感器网络簇间路由协议,让簇首和Sink节点直接通信或通过簇首节点转发数据造成能耗不均,节点过早死亡的缺陷。文中提出一种基于网关节点模型的无线传感器网络簇间路由算法,通过簇头与网关节点、网关节点自身建立虚电路,制定存储转发路由,将数据转发给Sink节点。并引入延时等待机制,增强了簇间信息的融合度,此算法适用于大规模无线传感器网络,有良好的可扩展性。仿真表明在能量节省等性能上与传统簇间路由算法相较有较大提高。     

基于全局信息的LEACH协议改进算法

传统LEACH协议在选举簇头节点时,采用动态簇头选举算法,每轮选举产生簇头节点个数为最佳簇头数的概率并不是最大,使得每轮选举的簇头数偏差较大,不能使节点能量达到最优化。针对这一问题,提出了改进的LEACH-P协议,该协议在簇头选举阶段通过簇头节点间的相互协作,以少量的能量消耗来获取全局信息,消除了LEACH协议簇头选举算法的盲目性,从而改善簇头选举算法。实验结果表明,LEACH—P协议相比传统LEACH协议有效地节省了节点能量,延长了网络生命周期。     

ETPS-MAC: Energy Traffic Priority Scheduling-based QoS-aware MAC protocol for hierarchical WSNs

The evolution of the wireless sensor network (WSN) in recent years has reached its greatest heights and applications are increasing day by day, one such application is Smart Monitoring Systems (SMSs) which is in vision of implementation in every urban and rural areas. The implementation of WSN architecture in SMS needs an intelligent scheduling mechanism that efficiently handles the dynamic traffic load without sacrificing the energy efficiency of network. This paper presents a centralized TDMA scheduling based medium access control (MAC) protocol, called Energy Traffic Priority Scheduling MAC (ETPS-MAC) that accommodates variable traffic load while maintaining Quality-of-Service (QoS) assurance in hierarchical WSNs. The ETPS-MAC protocol employs priority scheduling algorithm which considers two factors for assigning priority, the energy factor and the traffic load factor to avoid packet buffering and maintains minimum data packet delay in case of high traffic load. Moreover, a novel rank-based clustering mechanism in FPS-QMAC protocol prolongs the network lifetime by minimizing the distance between the cluster head (CH) and the base station (BS). Both analytical and simulation models demonstrate the superiority of the ETPS-MAC protocol in terms of energy consumption, transmission delay, data throughput and message complexity when compared with the existing TDMA based MAC protocols.