Evolutionary algorithms for cluster heads election in wireless sensor networks: Performance comparison

SummaryThe arbitrary distribution of sensor nodes and irregularity of the routing path led to unordered data, which is complex to handle in a wireless sensor network (WSN). To increase WSN lifetime, data aggregation models are developed to minimize energy consumption or ease the computational burden of nodes. The compressive sensing (CS) provides a new technique for prolonging the WSN lifetime. A hybrid optimized model is devised for cluster head (CH) selection and CS‐based data aggregation in WSN. The method aids to balance the energy amidst different nodes and elevated the lifetime of the network. The hybrid golden circle inspired optimization (HGCIO) is considered for cluster head (CH) selection, which aids in selecting the CH. The CH selection is done based on fitness functions like distance, energy, link quality, and delay. The routing is implemented with HGCIO to transmit the data projections using the CH to sink and evenly disperse the energy amidst various nodes. After that, compressive sensing is implemented with the Bayesian linear model. The convolutional neural network‐long short term memory (CNN‐LSTM) is employed for the data aggregation process. The proposed HGCIO‐based CNN‐LSTM provided the finest efficiency with a delay of 0.156 s, an energy of 0.353 J, a prediction error of 0.044, and a packet delivery ratio (PDR) of 76.309%.

Clustering is a useful mechanism in wireless sensor networks that helps to cope with scalability problems and, if combined with in-network data aggregation, may increase the energy efficiency of the network. At the same time, by assigning a special role to the cluster head nodes, clustering makes the network more vulnerable to attacks. In particular, disabling a cluster head by physical destruction or jamming may render the entire cluster inoperable temporarily until the problem is detected and a new cluster head is elected. Hence, the cluster head nodes may be attractive targets of attacks, and one would like to make it difficult for an adversary to identify them. The adversary can try to identify the cluster head nodes in various ways, including the observation of the cluster head election process itself and the analysis of the traffic patterns after the termination of the cluster head election. In this paper, we focus on the former problem, which we call the private cluster head election problem. This problem has been neglected so far, and as a consequence, existing cluster head election protocols leak too much information making the identification of the elected cluster head nodes easy even for a passive external observer. We propose the first private cluster head election protocol for wireless sensor networks that is designed to hide the identity of the elected cluster head nodes from an adversary that can observe the execution of the protocol.

Ensuring stability and extending network lifetime in Wireless Sensor Networks (WSNs) achieved through significantly reduced energy consumption is considered as a potential challenge. The selection of Cluster Head (CH) during the process of clustering is determined to be highly complicated in spite of its role in facilitating efficient and balanced energy consumption in the network. In this paper, Hybrid Stochastic Ranking and Opposite Differential Evolution enhanced Firefly Algorithm (HSRODE-FFA)-based clustering protocol is proposed for handling the issues of location-based CH selection approaches that select duplicate nodes with increased computation and poor selection accuracy. This HSRODE-FFA clustering scheme includes the process of sampling for selecting the CHs from among the sensor nodes that exist in the sample population and address the problems introduced by different locations of nodes and CHs. It is proposed as an attempt to improve stability and lifetime of WSNs based on the merits of Stochastic Firefly Ranking (SFR) that enhances the exploration capability of Firefly Algorithm (FFA). The hybridization of the enhanced FFA with Opposition Differential Evolution (ODE) aids in speeding and ensuring optimal exploitation in the selection of CHs. The proposed HSRODE-FFA thereby maintains a balance between the rate of exploitation and exploration for deriving mutual benefit of rapid and potential selection of CHs from the sampling population. The experimental results of the proposed HSRODE-FFA scheme confirm an enhanced stability period and network lifetime of 16.21% and 13.86% respectively in contrast to the benchmarked Harmony Search and Firefly Algorithm-based Cluster Head Selection (HSFFA-CHS), Krill Herd Optimization and Genetic Algorithm-based Cluster Head Selection (KHOGA-CHS), Particle Swarm Optimization with Energy Centers Searching-based Cluster Head Selection (PSO-ECS-CHS) and Spider Monkey Optimization-based Cluster Head Selection (SMO-CHS) schemes.

Wireless sensor networks (WSN) play a key role in enabling wireless communication technology among several nodes to remotely communicate and exchange information. WSN consists of tiny sensor nodes equipped with battery, scattered in an area to gather information around an environment and send to data collection node known as sink or base station (BS). WSN have been widely used in various applications like agriculture, fire detection, health care and military and has become imperative necessity for future revolutionary area like UAV (unmanned aerial vehicles), IoT (Internet of things) and smart cities which employs large scale sensor nodes. However sensor nodes are limited to battery, memory, low computational power, resource and bandwidth. Continues sensing of events, makes node to drain its battery faster and goes dead fast. For resource constrained WSN, hierarchical cluster based approaches are considered as energy efficient and improves network performance for large scale WSN. Minimizing energy consumption and extending network lifetime are major challenging issues of WSN, clustering methods with optimized routing have offered solution to optimize energy utilization. To balance energy consumption and improve network lifetime many existing hierarchical clustering approaches have been proposed, however existing method does not consider rotation of cluster head (CH) and considers cluster head selection based on residual energy and distance parameter. In this paper we propose an improved energy efficient cluster tree (IEECT) based routing to improve energy efficiency of hierarchical cluster. IEECT considers modification of existing LEACH (Low energy adaptive clustering hierarchy) protocol to improved energy efficient LEACH by considering energy parameters like residual node energy and average network energy. IEECT accounts optimal number of cluster head (CH) and selection of CH is done using threshold value among sensor nodes. Proposed IEECT combines tree based routing and data aggregation scheme to maintain desirable quality of service. Simulation experiments are carried out by using network simulator. Performance of IEECT is evaluated in terms of PDR, delay, energy consumption, network lifetime and overhead.

A UAV-assisted CH election framework for secure data collection in wireless sensor networks

Wireless sensor networks (WSNs) enable large-scale event monitoring; however, their performance is often constrained by low throughput. This study aims to develop a cluster-based routing protocol by implementing the multi-hopping clustering partition (MHCP) method. The MHCP process consists of three main stages: (i) cluster head (CH) selection, (ii) evaluation of node proximity to their respective CHs, and (iii) cluster partitioning to reduce intra-cluster variation. Four clusters were formed and interconnected through multi-hop communication, achieving throughput values of 142.0033, 244.1318, 119.0804, and 305.6159, respectively. In addition to the development of MHCP, the scientific contribution of this study is strengthened through the integration of the LEACH protocol and the K-means algorithm as a complementary methodological approach. LEACH improves energy efficiency through adaptive CH rotation, while K-means optimizes spatial node grouping. The combination of these methods ensures a balance between energy consumption and spatial proximity, resulting in improved throughput and extended network lifetime. Experimental results demonstrate that the proposed MHCP protocol achieves higher throughput than the conventional LEACH protocol across all clusters while maintaining acceptable delay and packet loss. These findings confirm that the integration of multi-hop communication and cluster partitioning effectively enhances data transmission efficiency and overall network performance in WSNs.

The present cluster head selection is fixed as a static node, so the cluster head becomes malicious the entire cluster communication is discarded in wireless sensor networks (WSNs). As per our new approach, the cluster heads are using fault tolerance schemes supports for selection cluster head through the game theory. This process may take additional energy to detect malicious cluster heads and to recover the network communication in the cluster. This article proposes fault-tolerant backup cluster head (BCH) selection influenced by game theory in wireless sensor networks WSNs by utilizing less energy. In WSNs, the malicious cluster heads can be exhibited efficiently using game theory through the different network conditions such as scalability, mobility, and energy utilization. Due to scalable WSNs, the cluster node is increasing in the cluster head becomes malicious thus network lifetime is reduced. So, the fault-tolerant BCH technique is incorporated to increase the lifetime of WSNs. So, the efficiency of the sensor network is reduced due to the cooperation of malicious and normal nodes. The malicious cluster heads being detected based on node parameters and the clustered network operations are carried out through fault-tolerant backup cluster head in WSNs. The simulation results have obtained for the proposed scheme compared with the network parameters such as dead nodes, alive nodes, and average energy of active nodes in the network.KeywordsWireless sensor networkGame theoryFault-tolerant networkCluster head selectionBackup cluster head

A Wireless Sensor Network (WSN) represents a set of deployed sensors in an area to track or monitor specific activities. Sensors are utilized to gather certain data and forward it to the sink or a Base Station (BS). Sensors are operated by limited-energy batteries. Optimizing the consumed energy will prolong the WSN's lifetime. One approach to reducing the consumed energy is by applying a cluster-based routing protocol. Optimizing the Cluster Head (CH) selection represents another approach to prolonging the WSN lifetime. This paper proposes an optimization algorithm to select the CH. The residual energy, the distance between sensors, and the centrality of the sensor node are used as significant features in selecting the CHs in this paper. The proposed algorithm is based on Particle Swarm Optimization (PSO) algorithm and K-Means algorithm. This study proposed approach is called the PSO-K algorithm. It helps in reducing energy consumption and increasing the network lifetime. MATLAB is used to model and simulate this developed algorithm. the number of sensors, number of rounds, number of CHs, the first dead nodes, the half-dead nodes, and the last dead nodes are used to evaluate the WSN performance. A comparison with other closely related works such as LEACH, EAMMH, and iLEACH shows that the PSO-K algorithm is the best in energy consumption and network lifetime.

Multi-objective cluster head using self-attention based progressive generative adversarial network for secured data aggregation

The advantages of deploying mobile sinks (MSs) in wireless sensor networks (WSNs) have attracted tremendous attention recently, because mobility of a sink provides an efficient method to improve data gathering performance in WSNs. However, in such kind of WSN, the efficiency of data dissemination is obviously affected by the trajectory of the MS. Therefore, optimal moving schemes of the MS have been studied in many researches. Nevertheless, in many cases, the trajectory of the MS is not able to change ideally as previous studies assumed but depending on the environmental conditions. In this paper, we focus on the efficiency of data gathering in WSN with a MS whose trajectory cannot change ideally and propose a new data diffusion scheme - implementing a new Pre-Diffusion Algorithm (PDA) in a hierarchical WSN aimed at improving data gathering efficiency. This hierarchical WSN is composed of several clusters, and datatransmission is done by higher powered cluster head (CH) node. Our scheme predetermines the cluster area where the MS is going to arrive in most probability in next period of time. MS fulfills data converging in the cluster head (CH)whose distance to the MS may the smallest compared with other CHs when data gathering starts. Thus more data can be collected with less trajectory length of MS. Simulation results show that our proposed scheme improves data gathering efficiency as compared to conventional scheme.

Hybrid based cluster head selection for maximizing network lifetime and energy efficiency in WSN

Energy minimization in sensor nodes is the problem in wireless sensor networks (WSNs). The most widely accepted method to preserve energy of sensor nodes is clustering. In cluster based networks, energy consumption is higher in the nodes which are closer to the sink as compared to the nodes which are located farther away from base station (BS). Various unequal clustering algorithms were proposed in the past to correct this issue. The major drawbacks in the findings are that the nodes, which join a specific cluster head, cause overburdening of the cluster head. In this paper, we present an algorithm namely energy efficient unequal sector clustering (EUSC) using multi-sector and unequal clustering approaches which improve the network’s energy efficiency. EUSC divides the network into multiple sectors based on nodes distances from BS. Selection of cluster heads in each sector is based on nodes distances to BS, residual energy and neighbor nodes proximities in that sector. Selection of a relay cluster head for data transmission is also based on residual energy, nodes distances to BS and queue length of nodes. In this paper, we have carried out some theoretical analysis of the nodes energy consumption in each sector and derived an expression for optimal number of clusters in each sector to minimize nodes energy consumption. Various simulations were carried out with MATLAB package to differentiate the competency of the proposed EUSC algorithm with that of the existing protocols ECHA and ‘PSO based protocol’. Simulation results in different network scenarios indicate that EUSC has given much improved performance than ECHA and ‘PSO based protocol’ in terms of number of clusters formed during each round, network lifetime and energy efficiency.

A vehicular adhoc network (VANET) is a mobile adhoc network in which moving road vehicles are network nodes. The major problem faced in VANET is transmitting the safety message or an ALERT message between the vehicles to avoid collisions. The large number of vehicles moving in a particular area will cause the data congestion that results in a long delay on delivering the messages. Clustering the vehicles into groups is an efficient approach to broadcast the messages with a less delay and high delivering ratio. The performance of the clusters is greatly influenced by the selection of Cluster Heads (CHs). In this paper, the formation of clusters and cluster head selection is formulated as optimization problem in order to maximize the efficiency of the network energy and link quality. The formulated problem has been solved using two approaches: Density based Clustering and the cluster heads selection using Differential Evolution (DE) Algorithm. By combining these approaches the safety or Alert messages will be transmitted easily to all the vehicles by dynamically adapting transmission power and contention window size. The performance of the proposed ESMBS protocol is compared and evaluated against the existing AdvB and CBAPA protocols. The proposed scheme can control various performance metrics such as the collision, packet delivery ratio, packet delay ratio, latency, transmission time and throughput. The proposed scheme broadcasts the alert or safety messages by reducing the probability of collisions.

Wireless sensor networks (WSN) are a yield of advancement in information technology and the requirement of large-scale communication infrastructures. Routing of data via selected paths is a critical task in WSN as process need to be carried on under resource constraint situations. This route identification problem can be better handled by employing appropriate heuristic bio-inspired computational intelligence optimization method. The most frequently applied routing is hierarchical routing algorithm is Low Energy Adaptive Clustering Hierarchy (LEACH) algorithm which has limitations in identifying energy efficient inter and intra route communication, identification of number of cluster head (CH), an eminent node to communicate to CH and Base Station (BS), selection of CH, and computing residual energy level, etc. Hence, researchers are focusing on boosting the capability of LEACH clustering algorithm by applying heuristic bio-inspired computational intelligence optimization methods. The proposed work is an attempt in this direction through applying heuristic bio-inspired Grey Wolf Optimization algorithm (GWO) for improving the performance of LEACH algorithm. In this paper, focus is given to increase the overall network time by adapting two modifications to conventional algorithms (i) selection of vice cluster head (VCH) in addition to CH (VCH node will replace the CH when CH when CH node goes down due to unexpected reasons as sensor node work under critical and uninterruptable environments and (ii) selection of intra and inter relay nodes (intra relay node will enhance the life span during CH data gathering and inter relay node will further enhance the life span of CH by acting as a mediator between CH an BS). The Spyder-py3 tool is used to simulate the proposed algorithms, LEACH Binary Grey Wolf search based Optimization (LEACH-BGWO) and LEACH Discrete Grey Wolf search based Optimization (LEACH-DGWO) protocols. The proposed work is compared with cluster based LEACH algorithm, chain based power-efficient gathering in sensor information systems (PEGASIS) algorithm, bio-inspired GWO and Genetic Algorithm data Aggregation (GADA) LEACH protocols. The results prove that both proposed algorithms outperformed other conventional algorithms in terms of prolonged network lifespan and increased throughput. Among proposed algorithms LEACH-BGWO outperformed LEACH-DGWO

Two-tier particle swarm optimization protocol for clustering and routing in wireless sensor network