An improved ant colony optimization-based approach with mobile sink for wireless sensor networks

Traditional wireless sensor networks (WSNs) with one static sink node suffer from the well-known hot spot problem, that of sensor nodes near the static sink bear more traffic load than outlying nodes. Thus, the overall network lifetime is reduced due to the fact some nodes deplete their energy reserves much faster compared to the rest. Recently, adopting sink mobility has been considered as a good strategy to overcome the hot spot problem. Mobile sink(s) physically move within the network and communicate with selected nodes, such as cluster heads (CHs), to perform direct data collection through short-range communications that requires no routing. Finding an optimal mobility trajectory for the mobile sink is critical in order to achieve energy efficiency. Taking hints from nature, the ant colony optimization (ACO) algorithm has been seen as a good solution to finding an optimal traversal path. Whereas the traditional ACO algorithm will guide ants to take a small step to the next node using current information, over time they will deviate from the target. Likewise, a mobile sink may communicate with selected node for a relatively long time making the traditional ACO algorithm delays not suitable for high real-time WSNs applications. In this paper, we propose an improved ACO algorithm approach for WSNs that use mobile sinks by considering CH distances. In this research, the network is divided into several clusters and each cluster has one CH. While the distance between CHs is considered under the traditional ACO algorithm, the mobile sink node finds an optimal mobility trajectory to communicate with CHs under our improved ACO algorithm. Simulation results show that the proposed algorithm can significantly improve wireless sensor network performance compared to other routing algorithms.     

基于压缩感知的传感器网络中概率负载均衡的数据路由协议

基于混合压缩感知(CS)理论,提出一种负载有效的路由协议.考虑分簇网络结构,簇内节点传输原始数据到簇头,簇头对数据进行压缩再通过最小生成树发送到sink.为防止簇头节点负载不均衡造成网络不能正常通信,提出负载度的概念并设计基于CS的负载均衡策略;然后,研究概率负载均衡策略以均衡所有节点的负载流量;最后,提出分布式补偿算法构建分簇网络并实现数据汇聚功能.仿真结果表明,所提出方法在提高网络生存时间及能耗均衡方面均优于传统方法.     

Hybrid metaheuristic algorithm for optimal cluster head selection in wireless sensor network

Clustering is one of the major techniques for maximizing the network lifetime in wireless sensor networks (WSNs). Here, the sensor nodes (SNs) are grouped into clusters and the cluster heads (CHs) are selected for each cluster. CHs gather data from particular cluster nodes and then forward it to Base Station (BS). However, the selection of CHs is the major issue in this scenario. The sensor nodes consume more energy for the data transmission and also affect the lifetime of the network. The clustering technique is used to provide the energy-efficient data transmission that consumes less energy and also increases the network lifetime. This paper aims to propose a new energy-aware CH selection framework by hierarchical routing in WSN via a hybrid optimization algorithm. Moreover, the selection of CH is carried out under the consideration of energy, distance, delay and Quality of Service (QoS) as well. For selecting the optimal CH, a new hybrid algorithm named as Particle Distance Updated Sea Lion Optimization (PDU-SLnO) algorithm is introduced that combines the concept of Sea Lion Optimization (SLnO) and Particle swarm optimization (PSO) algorithm. Finally, the performance of adopted method is computed over other traditional models with respect to certain metrics.     

Approximation schemes for load balanced clustering in wireless sensor networks

Clustering sensor nodes is an efficient technique to improve scalability and life time of a wireless sensor network (WSN). However, in a cluster based WSN, the leaders (cluster heads) consume more energy due to some extra load for various activities such as data collection, data aggregation, and communication of the aggregated data to the base station. Therefore, balancing the load of the cluster heads is a crucial issue for the long run operation of the WSNs. In this paper, we first present a load balanced clustering scheme for wireless sensor networks. We show that the algorithm runs in O(nlogn) time for n sensor nodes. We prove that the algorithm is optimal for the case in which the sensor nodes have equal load. We also show that it is a polynomial time 2-approximation algorithm for the general case, i.e., when the sensor nodes have variable load. We finally improve this algorithm and propose a 1.5-approximation algorithm for the general case. The experimental results show the efficiency of the proposed algorithm in terms of the load balancing of the cluster heads, execution time, and the network life.     

DUCF: Distributed load balancing Unequal Clustering in wireless sensor networks using Fuzzy approach

Data gathering in wireless sensor networks (WSN) consumes more energy due to large amount of data transmitted. In direct transmission (DT) method, each node has to transmit its generated data to the base station (BS) which leads to higher energy consumption and affects the lifetime of the network. Clustering is one of the efficient ways of data gathering in WSN. There are various kinds of clustering techniques, which reduce the overall energy consumption in sensor networks. Cluster head (CH) plays a vital role in data gathering in clustered WSN. Energy consumption in CH node is comparatively higher than other non CH nodes because of its activities like data aggregation and transmission to BS node. The present day clustering algorithms in WSN use multi-hopping mechanism which cost higher energy for the CH nodes near to BS since it routes the data from other CHs to BS. Some CH nodes may die earlier than its intended lifetime due to its overloaded work which affects the performance of the WSN. This paper contributes a new clustering algorithm, Distributed Unequal Clustering using Fuzzy logic (DUCF) which elects CHs using fuzzy approach. DUCF forms unequal clusters to balance the energy consumption among the CHs. Fuzzy inference system (FIS) in DUCF uses the residual energy, node degree and distance to BS as input variables for CH election. Chance and size are the output fuzzy parameters in DUCF. DUCF assigns the maximum limit (size) of a number of member nodes for a CH by considering its input fuzzy parameters. The smaller cluster size is assigned for CHs which are nearer to BS since it acts as a router for other distant CHs. DUCF ensures load balancing among the clusters by varying the cluster size of its CH nodes. DUCF uses Mamdani method for fuzzy inference and Centroid method for defuzzification. DUCF performance was compared with well known algorithms such as LEACH, CHEF and EAUCF in various network scenarios. The experimental results indicated that DUCF forms unequal clusters which ensure load balancing among clusters, which again improves the network lifetime compared with its counterparts.     

Energy-Efficient Routing Using Novel Optimization with Tabu Techniques for Wireless Sensor Network

Wireless Sensor Network (WSN) consists of a group of limited energy source sensors that are installed in a particular region to collect data from the environment. Designing the energy-efficient data collection methods in large-scale wireless sensor networks is considered to be a difficult area in the research. Sensor node clustering is a popular approach for WSN. Moreover, the sensor nodes are grouped to form clusters in a cluster-based WSN environment. The battery performance of the sensor nodes is likewise constrained. As a result, the energy efficiency of WSNs is critical. In specific, the energy usage is influenced by the loads on the sensor node as well as it ranges from the Base Station (BS). Therefore, energy efficiency and load balancing are very essential in WSN. In the proposed method, a novel Grey Wolf Improved Particle Swarm Optimization with Tabu Search Techniques (GW-IPSO-TS) was used. The selection of Cluster Heads (CHs) and routing path of every CH from the base station is enhanced by the proposed method. It provides the best routing path and increases the lifetime and energy efficiency of the network. End-to-end delay and packet loss rate have also been improved. The proposed GW-IPSO-TS method enhances the evaluation of alive nodes, dead nodes, network survival index, convergence rate, and standard deviation of sensor nodes. Compared to the existing algorithms, the proposed method outperforms better and improves the lifetime of the network.     

Trust- and energy-aware cluster head selection in a UAV-based wireless sensor network using Fit-FCM

Due to the emerging applications of unmanned aerial vehicle (UAV)-based technologies, UAV-based wireless communication techniques, such as UAV-based coverage extension, UAV-based data distribution and UAV-based relaying, are being used to collect information in different processing sectors. In particular, UAV-based data gathering and distribution can be executed using a UAV-based wireless sensor network (WSN). In UAV-based WSNs, the cluster heads (CHs) serve important functions in both data gathering and data transfer between members and UAVs. Due to the important functions of CHs, many attackers attempt hack CH nodes. Typically, a hacked CH utilizes excess energy compared to a normal CH since it performs the CH function of delivering information to a sink greedily. To resolve this, this paper develops a novel UAV-based CH selection (CHS) algorithm for use in WSNs, namely, the Fitness-based Fuzzy C-Means (Fit-FCM) algorithm, which gathers the remaining energy of nodes and utilizes the energy for selecting new CHs while neglecting the nodes with the lowest energy. Initially, UAV-based WSN nodes are simulated, and then, CHS is performed using the developed Fit-FCM algorithm, in which fitness functions such as energy, distance and trust are considered. After CHS, information is transmitted through the selected CHs. Experimental results demonstrate that the developed Fit-FCM achieves better results in terms of distance, energy, and trust, with values of 51.9076 m, 0.4882 J, and 0.536439, respectively.

     

Energy Efficient Gravitational Search Algorithm and Fuzzy Based Clustering With Hop Count Based Routing For Wireless Sensor Network

In Wireless sensor networks, energy efficiency is the significant attribute to be improved. Clustering is the major technique to enhance energy efficiency. Using this technique, sensor nodes in the network region are grouped as several clusters and cluster head (CH) is chosen for each and every cluster. This CH gathers data packet from the non-CH members inside the cluster and forwards the collected data packet to the base station. However, the CH may drain its energy after a number of transmissions. So, we present the Energy efficient Gravitational search algorithm (GSA) and Fuzzy based clustering with Hop count based routing for WSN in this paper. Initially, CH is selected using Gravitational Search Algorithm (GSA), based on its weight sensor nodes are joined to the CH and thus cluster is formed. Among the selected CHs in the network, supercluster head (SCH) is selected using a fuzzy inference system (FIS). This selected SCH gathers the data packet from all CHs and forwards it to the sink or base station. For transmission, the efficient route is established based on the hop count of the sensor nodes. Simulation results show that the performance of our proposed approach is superior to the existing work in terms of delivery ratio and energy efficiency.

     

A fault tolerant optimal relay node selection algorithm for Wireless Sensor Networks using modified PSO

Wireless Sensor Networks (WSNs) have energy-constraints that restricts to achieve prolonged network lifetime. To optimize energy consumption of sensor nodes, clustering is one of the efficient techniques for minimization of energy conservation in WSNs. This technique sends the collected data towards the SINK based on cluster head (CH) nodes that leads to the saving of energy. WSNs have been faced a crucial issue of fault tolerance and the overall data communication is collapsed due to the failure of cluster head. Various fault-tolerance clustering methods are available for WSNs, but they are not selected the backup nodes properly. The backup nodes’ closeness or location to the other remaining nodes is not considered in these methods. They may increase network overhead with the backup nodes accessibility. A fault-tolerance cluster-based routing method is presented in this paper that aims on providing fault tolerance for relay selection in addition to the data aggregation method for clustered WSNs. The proposed method utilizes backup mechanism & the Particle Swarm Optimization (PSO) to achieve this. Based on the distance from sink, residual energy, and link delay parameters, the CHs are chosen and the network is categorized into the clusters. The Backup CHs are selected by estimating the centrality among the nodes. As a part of intra-cluster communication for reducing the aggregation overhead among CHs, the Aggregator (AG) nodes are deployed in every cluster. So that they act as the bridge between the member nodes and CHs. These AG nodes aggregates the information from member nodes and deliver it to the CHs. The PSO with modified fitness function is used to identify the best relays between AG and member nodes. The proposed mechanism is compared with existing techniques such as EM-LEACH AI-Sodairi and Ouni (2018), QEBSR Rathee et al. (2019), QOS-IHC Singh and Singh (2019), and ML-SEEP Robinson et al. (2019). The simulation results proved that the proposed mechanism reduces overhead by 55% and improve the energy consumption & throughput by 40% & 60% respectively.     

A chain-cluster based routing algorithm for wireless sensor networks

Wireless sensor networks (WSNs) are an emerging technology for monitoring physical world. Different from the traditional wireless networks and ad hoc networks, the energy constraint of WSNs makes energy saving become the most important goal of various routing algorithms. For this purpose, a cluster based routing algorithm LEACH (low energy adaptive clustering hierarchy) has been proposed to organize a sensor network into a set of clusters so that the energy consumption can be evenly distributed among all the sensor nodes. Periodical cluster head voting in LEACH, however, consumes non-negligible energy and other resources. While another chain-based algorithm PEGASIS (power- efficient gathering in sensor information systems) can reduce such energy consumption, it causes a longer delay for data transmission. In this paper, we propose a routing algorithm called CCM (Chain-Cluster based Mixed routing), which makes full use of the advantages of LEACH and PEGASIS, and provide improved performance. It divides a WSN into a few chains and runs in two stages. In the first stage, sensor nodes in each chain transmit data to their own chain head node in parallel, using an improved chain routing protocol. In the second stage, all chain head nodes group as a cluster in a self- organized manner, where they transmit fused data to a voted cluster head using the cluster based routing. Experimental results demonstrate that our CCM algorithm outperforms both LEACH and PEGASIS in terms of the product of consumed energy and delay, weighting the overall performance of both energy consumption and transmission delay.     

Fuzzy rule-based faulty node classification and management scheme for large scale wireless sensor networks

In a wireless sensor network (WSNs), probability of node failure rises with increase in number of sensor nodes within the network. The, quality of service (QoS) of WSNs is highly affected by the faulty sensor nodes. If faulty sensor nodes can be detected and reused for network operation, QoS of WSNs can be improved and will be sustainable throughout the monitoring period. The faulty nodes in the deployed WSN are crucial to detect due to its improvisational nature and invisibility of internal running status. Furthermore, most of the traditional fault detection methods in WSNs do not consider the uncertainties that are inherited in the WSN environment during the fault diagnosis period. Resulting traditional fault detection methods suffer from low detection accuracy and poor performance. To address these issues, we propose a fuzzy rule-based faulty node classification and management scheme for WSNs that can detect and reuse faulty sensor nodes according to their fault status. In order to overcome uncertainties that are inherited in the WSN environment, a fuzzy logic based method is utilized. Fuzzy interface engine categorizes different nodes according to the chosen membership function and the defuzzifier generates a non-fuzzy control to retrieve the various types of nodes. In addition, we employed a routing scheme that reuses the retrieved faulty nodes during the data routing process. We performed extensive experiments on the proposed scheme using various network scenarios. The experimental results are compared with the existing algorithms to demonstrate the effectiveness of the proposed algorithm in terms of various important performance metrics.     

无线传感器网络分簇路由节能研究

在大规模传感和环境监测中,节约能源延长传感器节点生命已成为无线传感器网络最重要的研究课题之一。提供合理的能源消耗和改善无线网络生命周期的传感器网络系统,必须设计一种新的有效的节能方案和节能路由体系。方案采用一种聚类算法减少无线传感器网络的能量消耗,创建一种cluster-tree分簇路由结构的传感器网络。该方案主要目标是做一个理想的分簇分配,减少传感器节点之间的数据传输距离,降低传感器节点能源消耗,延长寿命。实验结果表明,该方案有效地降低了能源消耗从而延长无线传感器网络生命。     

Distributed unequal clustering algorithm in large-scale wireless sensor networks using fuzzy logic

Clustering is a promising and popular approach to organize sensor nodes into a hierarchical structure, reduce transmitting data to the base station by aggregation methods, and prolong the network lifetime. However, a heavy traffic load may cause the sudden death of nodes due to energy resource depletion in some network regions, i.e., hot spots that lead to network service disruption. This problem is very critical, especially for data-gathering scenarios in which Cluster Heads (CHs) are responsible for collecting and forwarding sensed data to the base station. To avoid hot spot problem, the network workload must be uniformly distributed among nodes. This is achieved by rotating the CH role among all network nodes and tuning cluster size according to CH conditions. In this paper, a clustering algorithm is proposed that selects nodes with the highest remaining energy in each region as candidate CHs, among which the best nodes shall be picked as the final CHs. In addition, to mitigate the hot spot problem, this clustering algorithm employs fuzzy logic to adjust the cluster radius of CH nodes; this is based on some local information, including distance to the base station and local density. Simulation results demonstrate that, by mitigating the hot spot problem, the proposed approach achieves an improvement in terms of both network lifetime and energy conservation.     

Energy-aware node placement,topology control and MAC scheduling for wireless sensor networks

In the WSNs, the nodes closer to the sink node have heavier traffic load for packet forwarding because they do not only collect data within their sensing range but also relay data for nodes further away. The unbalanced power consumption among sensor nodes may cause network partition. This paper proposes efficient node placement, topology control, and MAC scheduling protocols to prolong the sensor network lifetime, balance the power consumption of sensor nodes, and avoid collision. Firstly, a virtual tree topology is constructed based on Grid-based WSNs. Then two node-placement techniques, namely Distance-based and Density-based deployment schemes, are proposed to balance the power consumption of sensor nodes. Finally, a collision-free MAC scheduling protocol is proposed to prevent the packet transmissions from collision. In addition, extension of the proposed protocols are made from a Grid-based WSN to a randomly deployed WSN, enabling the developed energy-balanced schemes to be generally applied to randomly deployed WSNs. Simulation results reveal that the developed protocols can efficiently balance each sensor node’s power consumption and prolong the network lifetime in both Grid-based and randomly deployed WSNs.     

A Novel Fault Tolerance Energy-Aware Clustering Method via Social Spider Optimization (SSO) and Fuzzy Logic and Mobile Sink in Wireless Sensor Networks (WSNs)

In recent years, the application of WSNs has been remarkably increased and notable developments and advances have been achieved in this regard. In particular, thanks to smart, cheaper and smaller nodes, different types of information can be detected and gathered in different environments and under different conditions. As the popularity of WSNs has increased, the problems and issues related to networks are examined and investigated. As a case in point, routing issue is one of the main challenges in this regard which has a direct impact on the performance of sensor networks. In WSN routing, sensor nodes send and receive great amounts of information. As a result, such a system may use lots of energy which may reduce network lifetime. Given the limited power of a battery, certain method and approaches are needed for optimizing power consumption. One such approach is to cluster sensor nodes; however, improper clustering increases the load imposed on the clusters around the sink. Hence, for proper clustering, smart algorithms need to be used. Accordingly, in this paper, a novel algorithm, namely social spider optimization (SSO) algorithm is proposed for clustering sensor network. It is based on the simulation of the social cooperative behavior of spiders. In the proposed algorithm, nodes imitate a group of spiders who interact with each other according to biological rules of colony. Furthermore, fuzzy logic based on the two criteria of battery level and distance to sink is used for determining the fitness of nodes. On the other hand in WSNs with a fixed sink, since the nodes near the sink share multi-hop routes and data and integrated towards the sink, these nodes are more likely to deplete their battery energy than other nodes of the network. Also In this paper, mobile sink was suggested for dealing with this problem. For investigating and demonstrating the performance of the proposed method, we compared it with DCRRP and NODIC protocol. The results of simulation indicated better performance of the proposed method in terms of power consumption, throughput rate, end-to-end delay and signal to noise ratio and has higher failure tolerance especially in terms of sensor nodes’ failure.     

Clustering algorithms for maximizing the lifetime of wireless sensor networks with energy-harvesting sensors

Motivated by recent developments in wireless sensor networks (WSNs), we present several efficient clustering algorithms for maximizing the lifetime of WSNs, i.e., the duration till a certain percentage of the nodes die. Specifically, an optimization algorithm is proposed for maximizing the lifetime of a single-cluster network, followed by an extension to handle multi-cluster networks. Then we study the joint problem of prolonging network lifetime by introducing energy-harvesting (EH) nodes. An algorithm is proposed for maximizing the network lifetime where EH nodes serve as dedicated relay nodes for cluster heads (CHs). Theoretical analysis and extensive simulation results show that the proposed algorithms can achieve optimal or suboptimal solutions efficiently, and therefore help provide useful benchmarks for various centralized and distributed clustering scheme designs.     

基于虚拟引力的WSNs路由协议设计与实现

数据采集是无线传感器网络(WSNs)主要功能之一,大规模的传感器网络采集并回收数据时容易出现节点负载不均衡,导致负载重的节点过早死亡.为了延长传感器网络的生存时间,本文提出了一种基于虚拟力的分簇路由协议(CRPVG),选取合适的节点出任簇首;根据簇首与普通节点的虚拟引力大小进行分簇;通过簇首之间多条传输将采集的数据包发送至基站节点.实验结果表明:提出的分簇路由协议在能耗均衡方面起到了较好的作用,延长了网络的生存时间.     

基于萤火虫算法的无线传感器网络节点重部署策略

节点部署是无线传感器网络研究的重要问题之一.针对节点部署过程中的能量空洞问题,提出了一种基于萤火虫算法(FA)的节点重部署(NRBFA)策略.首先,在节点随机部署的传感器网络中,利用k-means算法进行分簇并引入冗余节点;然后,利用FA移动冗余节点,以分担簇头(CH)负载并均衡网络中节点的能耗;最后,再次利用FA寻找...     

Dynamic cluster head for lifetime efficiency in WSN

Saving energy and increasing network lifetime are significant challenges in wireless sensor networks （WSNs）. In this paper, we propose a mechanism to distribute the responsibility of cluster-heads among the wireless sensor nodes in the same cluster based on the ZigBee standard, which is the latest WSN standard. ZigBee supports ad hoc on-demand vector （AODV） and cluster-tree routing protocols in its routing layer. However, none of these protocols considers the energy level of the nodes in the network establishing process or in the data routing process. The cluster-tree routing protocol supports single or multi-cluster networks. However, each single cluster in the multi-cluster network has only one node acting as a cluster head. These cluster-heads are fixed in each cluster during the network lifetime. Consequently, using these cluster-heads will cause them to die quickly, and the entire linked nodes to these cluster-heads will be disconnected from the main network. Therefore, the proposed technique to distribute the role of the cluster head among the wireless sensor nodes in the same cluster is vital to increase the lifetime of the network. Our proposed technique is better in terms of performance than the original structure of these protocols. It has increased the lifetime of the wireless sensor nodes, and increased the lifetime of the WSN by around 50% of the original network lifetime.     

HDEEC：面向异构网络的一种基于簇路由协议

有效地使用传感节点的能量进而延长网络寿命成为设计无线传感网路由协议的一项挑战性的工作.为了延长网络,现存的多数簇方案是面向同构网络.为此,面向异构网络,提出基于簇的分布式能量有效路由HDEEC(heterogeneous WSN distributed energy-efficient clustering)协议.HDEEC协议首先提出异构网络模型,考虑了普通节点、特优节点和超特优节点三级能量节点;然后,提出能量消耗模型;最后依据这两个模型,提出了簇头选择方案.HDEEC协议以平衡、有效方式动态改变节点被选为簇头的概率.仿真结果表明,提出的HDEEC协议能够有效延长网络寿命,比DEEC、DDEEC的网络寿命分别提高了72％、68％.