Energy-Efficient Routing Protocols for Wireless Sensor Networks: Architectures, Strategies, and Performance

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%.

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.

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.

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

Prolonged network lifetime, scalability and efficient load balancing are essential for optimal performance of a wireless sensor network. Clustering provides an effective way of extending the lifetime of a sensor network. Clustering is the process that divides sensor networks into smaller localized group (called clusters) of members with a cluster head. Clustering protocols need to elect optimal number of clusters in hierarchically structured wireless sensor networks. Any clustering scheme that elects clusters uniformly (irrespective of the distance from Base Station) incurs excessive energy usage on clusters proximal and distant to Base Station. In single hop networks a gradual increment in the energy depletion rate is observed as the distance from the cluster head increases[17]. This work focuses on the analysis of wasteful energy consumption within a uniform cluster head election model (EPEM) and provides an analytical solution to reduce the overall consumption of energy usage amongst the clusters elected in a wireless sensor network. A circular model of sensor network is considered, where the sensor nodes are deployed around a centrally located Base Station. The sensor network is divided into several concentric rings centred at the Base Station. A model, Unequal Probability Election Model (UEPEM), which elects cluster heads non-uniformly is proposed. The probability of cluster head election depends on the distance from the Base Station. UEPEM reduces the overall energy usage by about 21% over EPEM. The performance of UEPEM improves as the number of rings is increased.

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.

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

Energy efficiency has become a primary issue in wireless sensor networks (WSN). The sensor networks are powered by battery and thus they turn out to be dead after a particular interval. Hence, enhancing the data dissipation in energy efficient manner remains to be more challenging for increasing the life span of sensor devices. It has been already proved that the clustering method could improve or enhance the life span of WSNs. In the clustering model, the selection of cluster head (CH) in each cluster regards as the capable method for energy efficient routing, which minimizes the transmission delay in WSN. However, the main problem dealt with the selection of optimal CH that makes the network service prompt. Till now, more research works have been processing on solving this issue by considering different constraints. Under this scenario, this paper attempts to develop a new clustering model with optimal cluster head selection by considering four major criteria like energy, delay, distance, and security. Further, for selecting the optimal CHs, this paper proposes a new hybrid algorithm that hybridizes the concept of dragon fly and firefly algorithm algorithms, termed fire fly replaced position update in dragonfly. Finally, the performance of the proposed work is carried out by comparing with other conventional models in terms of number of alive nodes, network energy, delay and risk probability.

In Wireless Sensor Network (WSN), unreasonable node clustering algorithm will inevitably lead to premature death, unawareness of regional information.To address problems of traditional LEACH algorithm, An improved LEACH multi-hop routing algorithm based on Energy Distance Threshold (EDT) is presented. It mainly improves from aspects of cluster number, cluster head selection and inter-cluster communication. The improved clustering hierarchy algorithm considers the nodes Energy Distance Threshold as an important factor in selection then chooses the best cluster head within the area. The selection of cluster head is based on re-select manner, which dynamically and effectively select cluster heads by control cluster number and combing with EDT. After cluster head completes local data fusion and processing, it look for adjacent cluster head as next hop closer to base station with larger EDT if the head is far away from station. The next hop repeats above process till the base station is arrived. Such manner of multi-hop among clusters enables communication among clusters and base stations so as to avoid direct communication between head and station. The method reduces energy consumption and also balances energy. Simulation results show the improved LEACH algorithm has higher energy utilizing rate, and it helps prolong network’s lifetime.

Wireless Sensor Networks (WSNs) have made significant strides in recent years owing to their growth in terms of equipment and cost reduction. Several services have been designed, based on the application and network architecture. The LEACH (Low Energy Adaptive Clustering Hierarchy) mechanism is one of the most considerable energy protecting solutions in WSN environments. This hierarchical protocol aggregates and forwards data from cluster members to a fixed Sink node using the cluster head feature of the nodes. We developed a new set of routing mechanisms called CB-LEACH by introducing movement possibility to the Sink node and balancing the cluster head election decision taking into consideration not just the distances between the nodes but the residual energy of the potential cluster head candidates, as well. In this paper, we modulate the balance factor of the cluster head election to conform to different rules and we analyzed the behaviour of the new system and its most important features.

Energy efficiency is critical in Wireless Sensor Networks (WSNs), pivotal for Internet of Things (IoT) applications like environmental monitoring and smart cities. Clustering protocols like Low-Energy Adaptive Clustering Hierarchy (LEACH) extend network lifetime through Cluster Head (CH) rotation, but random CH selection often leads to suboptimal energy use. This study evaluates eight hybrid Particle Swarm Optimization-based LEACH (PSO-LEACH) variants to optimize CH selection and clustering: FA-PSO-LEACH (Firefly Algorithm), CSA-PSO-LEACH (Crow Search Algorithm), PSO-LEACH with CBTEERA, Adaptive PSO-LEACH, ABC-PSO-LEACH (Artificial Bee Colony), ACO-PSO-LEACH (Ant Colony Optimization), WOA-PSO-LEACH (Whale Optimization Algorithm), and Hybrid GA-PSO-LEACH (Genetic Algorithm). The primary research objective is to compare these variants in terms of energy efficiency, network lifetime, and clustering quality to identify the most effective strategies for prolonging WSN operation in IoT environments. Specifically, this paper addresses the research question: Which hybrid PSO-LEACH variant provides the optimal balance between early-stage stability and long-term energy conservation in a simulated WSN with 50 nodes? Each variant uses a unique metaheuristic for CH selection based on residual energy and distance, with PSO refining clustering. A Python simulation modelled a 100m × 100m WSN with 50 nodes, assessing network lifetime (First Node Died, FND; Half Nodes Died, HND; Last Node Died, LND), energy consumption, and clustering quality. Results show ABC-PSO-LEACH leading with FND at 57 rounds and LND at 462 rounds, followed by WOA-PSO-LEACH (HND 148, LND 406), surpassing others (e.g., PSO-LEACH with CBTEERA: FND 10, LND 280). WOA-PSO-LEACH’s balanced energy distribution excels in mid-to-late stages, despite computational overhead. These bio-inspired hybrids outperform traditional LEACH, with ABC-PSO-LEACH and WOA-PSO-LEACH ideal for prolonged WSN operation. Future work aims to enhance early-stage performance and explore dynamic topologies.

The main benefit of selecting a suitable node as cluster head (CH) in clustering for wireless mobile sensor networks (MWSNs) is to prolong the network lifetime. But the safe selection of CH is a challenging task by taking security into account. Mostly CH selection algorithms in MWSN do not consider security when selecting CH. We have proposed secure CH selection algorithm by calculating weight of each node to deal with secure selection using minimum energy consumption. The weight of node is a combination of different metrics including trust metric (behaviors of sensor node) which promotes a secure decision of a CH selection; in terms of this, the node will never be a malicious one. The trust metric is definitive and permits the proposed clustering algorithm to keep away from any malignant node in the area to select a CH, even if the rest of the parameters are in its favor. Other metrics of node include waiting time, connectivity degree, and distance among nodes. The selection of CHs is completed utilizing weights of member nodes. The preparatory outcomes acquired through simulation exhibit the adequacy of our proposed scheme as far as average rate of avoiding malicious node as a CH, energy efficiency, and some other performance parameters are concerned.

The Routing Approaches in Mobile Ad-hoc Network have almost entered the saturated arena. But still there are available options of perfection in this meadow. The task of improving the clustering technique is one of the fields which is under rated as a research topic for further improvement of routing. Clustering based MANET routing protocols, which are popular in terms of efficiency, simplicity and usability mostly use circular based clustering techniques. And the election of cluster head is done through the use conventional approaches. This particular paper is mainly focused to launch a new-fangled clustering technique through which it is possible to improve the routing performance of existing protocols. Another important aspect of this paper is to provide a cluster head selection algorithm which can effectively maintain the clusters and provides more stability. Proposed clustering idea is useful for geographically related nodes effectively in different turf of routing. To maintain the clusters and their stability, it provides a new idea to select cluster head within the cluster, also the election of secondary cluster head for avoiding further election immediately after the unavailability of primary cluster head. This idea is evaluated in network simulator and it outperforms the existing clustering techniques.

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

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