Clustered Wireless Sensor Networks Research Articles

Retraction Note to: Implementation of thermal aware wireless sensor network clustering algorithm based on fuzzy and spider optimized cluster head selection

Retraction Note to: Implementation of thermal aware wireless sensor network clustering algorithm based on fuzzy and spider optimized cluster head selection

Adaptive multitask clustering algorithm based on distributed diffusion least-mean-square estimation

Distributed estimation with multitask clustering in wireless sensor networks has always been a challenging and attractive topic. When the multitask estimation problem becomes complex and general, such as more complex parameters and more general networks, many multitask clustering algorithms may experience significant performance degradation. To overcome these situations, a highly robust multitask clustering algorithm is proposed in this paper, which can be applied to more general and complex multitask estimation problems. The proposed algorithm is considered as a hybrid system. One subsystem is used to provide a reference estimation, which is further used to perform multitask clustering. Based on the clustering network, the other subsystem is performed to get a distributed adaptive estimation. In addition, each node is considered as a relaying node, so it can obtain the relaying information across the network for enhancing the performance. The convergence performance of the proposed algorithms is analyzed in the mean and mean-square senses. Simulation results demonstrate the robustness and accuracy of the proposed algorithms as compared to other multitask clustering algorithms.

Correction to: Histogram and entropy oriented image coding for clustered wireless multimedia sensor networks (WMSNS)

Correction to: Histogram and entropy oriented image coding for clustered wireless multimedia sensor networks (WMSNS)

Swarm Intelligence with Adaptive Neuro-Fuzzy Inference System-Based Routing Protocol for Clustered Wireless Sensor Networks.

Wireless sensor network (WSN) comprises numerous compact-sized sensor nodes which are linked to one another. Lifetime maximization of WSN is considered a challenging problem in the design of WSN since its energy-limited capacity of the inbuilt batteries exists in the sensor nodes. Earlier works have focused on the design of clustering and routing techniques to accomplish energy efficiency and thereby result in an increased lifetime of the network. The multihop route selection process can be treated as an NP-hard problem and can be solved by the use of computational intelligence techniques such as fuzzy logic and swarm intelligence (SI) algorithms. With this motivation, this article aims to focus on the design of swarm intelligence with an adaptive neuro-fuzzy inference system-based routing (SI-ANFISR) protocol for clustered WSN. The proposed SI-ANFISR technique aims to determine the cluster heads (CHs) and optimal routes for multihop communication in the network. To accomplish this, the SI-ANFISR technique primarily employs a weighted clustering algorithm to elect CHs and construct clusters. Besides, the SI-ANFISR technique involves the design of an ANFIS model for the selection process, which make use of three input parameters, namely, residual energy, node degree, and node history. In order to optimally adjust the membership function (MF) of the ANFIS model, the squirrel search algorithm (SSA) is utilized. None of the earlier works have used ANFIS with SSA for the routing process. The design of SSA to tune the MFs of the ANFIS model for optimal routing process in WSN shows the novelty of the study. The experimental validation of the SI-ANFISR technique takes place, and the results are inspected under different aspects. The simulation results highlighted the significant performance of the SI-ANFISR technique compared to the recent techniques with a maximum throughput of 43838 kbps and residual energy of 0.4800J, respectively.

Bio-Inspired Hybrid Optimization Algorithms for Energy Efficient Wireless Sensor Networks: A Comprehensive Review

Researchers are facing significant challenges to develop robust energy-efficient clustering and routing protocols for Wireless Sensor Networks (WSNs) in different areas such as military, agriculture, education, industry, environmental monitoring, etc. WSNs have made an everlasting imprint on everyone’s lives. The bulk of existing routing protocols has focused on cluster head election while disregarding other important aspects of routing including cluster formation, data aggregation, and security, among others. Although cluster-based routing has made a significant contribution to tackling this issue, the cluster head (CH) selection procedure may still be improved by integrating critical characteristics. Nature-inspired algorithms are gaining traction as a viable solution for addressing important challenges in WSNs, such as sensor lifespan and transmission distance. Despite this, the sensor node batteries cannot be changed when installed in a remote or unsupervised area due to their wireless nature. As a result, numerous researches are being done to lengthen the life of a node span. The bulk of existing node clustering techniques suffers from non-uniform cluster head distribution, an imbalanced load difficulty within clusters, concerning left-out nodes, coverage area, and placement according to a recent study. Metaheuristic algorithms (DE, GA, PSO, ACO, SFO, and GWO) have the advantages of being simple, versatile, and derivation-free, as well as effectively utilizing the network’s energy resource by grouping nodes into clusters to increase the lifespan of the entire network. In this paper, we explore recently used hybridization techniques (DE-GA, GA-PSO, PSO-ACO, PSO-ABC, PSO-GWO, etc.) for bio-inspired algorithms to improve the energy efficiency of WSNs. This paper also discusses how critical issues can be addressed by speeding up the implementation process, how more efficient data can be transferred, as well as how energy consumption can be reduced by using bio-inspired hybrid optimization algorithms.

Histogram and entropy oriented image coding for clustered wireless multimedia sensor networks (WMSNS)

Histogram and entropy oriented image coding for clustered wireless multimedia sensor networks (WMSNS)

A Novel Energy-Aware Routing in Wireless Sensor Network Using Clustering Based on Combination of Multiobjective Genetic and Cuckoo Search Algorithm

The development of various applications of wireless sensor networks has aroused great interest in using these types of networks in various fields. These networks, without infrastructure and self-organization, are easily deployed in most environments and collect information about environmental phenomena for analysis and proper response to accidents and send them to the basic centers. They do. Wireless sensor networks are made up of some sensor nodes that both act as sensors and act as relay nodes concerning to each other. On the other hand, the lack of infrastructure in these networks has limited resources so that the nodes of the battery are fed with limited energy. Due to the location of networks in difficult and impassable areas, it is not possible to recharge or replace the node battery. Therefore, saving energy consumption in this type of network is one of the most important challenges. Since the rate of energy consumption when sensing information and receiving data packets from another node is a fixed value, so sensor nodes have the highest energy consumption when sending data. Therefore, routing methods try to reduce energy consumption based on systematic approaches. One of the most promising solutions to reduce energy consumption in wireless sensor networks is to cluster the nodes and select the threaded node based on the data transfer parameters so that the average energy consumption in the nodes is reduced and the network lifetime is increased. Therefore, in this research, a new optimization approach using multiobjective genetic algorithm and cuckoo algorithm for clustering wireless sensor networks is presented. In this study, in order to select clustered nodes from a multiobjective genetic algorithms based on reducing intracluster distances and reducing energy consumption in cluster member nodes and near-optimal routing based on cuckoo optimization algorithm to transfer information between nodes have been used in the direction of the cavity. The implementation results show that considering the evolutionary capabilities of the multiobjective genetic algorithm and the cuckoo optimization algorithm, the proposed method in terms of energy consumption, efficiency, delivery rate, and packet transmission latency, compared to previous methods, has improved.

Muti-stage Key Management Scheme for Cluster based WSN

Secured communication over the Wireless Sensor Network (WSN) is one of the prime concerns nowadays as the wireless communication medium suffers under a wide range of networks. For establishing secured communication over WSN, literature has suggested the key level management protocol, where each transmission is established based on the availability of secured key. This work develops a key management protocol, namely Multi stage key management (MSKM) protocol, for the secured communication over the clustered WSN. The entire protocol is implemented in three stages, such as pre-deployment, key generation, and key authentication and verification. In the first stage, the nodes are provided with the identity, and then, the second stage uses the homomorphic encryption model, for generating the necessary key to the communication. Finally, a mathematical model is developed in this work with several factors, such as a hashing function, homomorphic encryption, dynamic passwords, profile sequence, random number, and EX-OR functions. The proposed MSKM protocol establishes the secured communication over the WSN by authenticating the entities. The entire work is compared with several states of art techniques and evaluated based on several metrics. The proposed MSKM protocol achieved values of 0.122 kb, 0.929, 2.332 kb, and 14.586 joules for the communication overhead, detection accuracy, key memory storage, and energy, respectively.

An algorithm for enhancing coverage and network lifetime in cluster-based Wireless Sensor Networks

Majority of wireless sensor networks (WSNs) clustering protocols in literature have focused on extending network lifetime and little attention has been paid to the coverage preservation as one of the QoS requirements along with network lifetime. In this paper, an algorithm is proposed to be integrated with clustering protocols to improve network lifetime as well as preserve network coverage in heterogeneous wireless sensor networks (HWSNs) where sensor nodes can have different sensing radii and energy attributes. The proposed algorithm works in proactive way to preserve network coverage and extend network lifetime by efficiently leveraging mobility to optimize the average coverage rate using only the nodes that are already deployed in the network. Simulations are conducted to validate the proposed algorithm by showing improvement in network lifetime and enhanced full coverage time with less energy consumption

SEPCS: Prolonging Stability Period of Wireless Sensor Networks using Compressive Sensing

Compressive sensing (CS) is an emerging theory thatis based on the fact that a small number of linear projections of asparse data contains enough information for reconstruction. CScan break through the asymmetry between the data acquisitionand information processing that makes a great challenge to therestriction energy and computation consumption of the sensornodes. In this paper, we propose a routing protocol called SEPCSfor clustered wireless sensor networks (WSNs) using CS. SEPCScombines a new clustering strategy with CS theory for prolongingstability period and network lifetime in WSNs. Our simulationresults show that the proposed protocol can effectively prolongthe stability period and network lifetime compared with existingprotocols.

Multi-level energy efficient cooperative scheme for ring based clustering in wireless sensor network

Abstract Wireless Sensor Network (WSN) is composed of different sensors attached to the same wireless medium. Sensor node data is usually forwarded to the base station, where it has been stored, evaluated, and processed. Ring Routing, a new energy-efficient distributed routing protocol aimed at larger-scale WSN applications with mobile sink and motionless sensor nodes. The structure ring is quickly converted, and they are capable of switching positions with regular nodes in a simple and effective manner, thereby minimizing the hotspot problem. In the network system, the central node is considered, and ring nodes with a certain distance are established while creating a ring structure. Since the sink nodes are powered by batteries, they deplete their batteries quicker than the other nodes, which is attributable to the accumulation of data traffic into the sink. Furthermore, when the ring configuration is disrupted due to ring node expiration, data transfer becomes tedious and causes significant delay. As a ring node starts to exhaust, its neighbor takes over as the ring node, and this selection of the regular node as the ring node is done optimally based on Multiple-Fitness Parameters such as Four-level energy consumption (4En), Distance (distance between the ring node and the regular node Dist ring − reg and distance between network centers to regular node Δ D i s t C − r i n g ${\Delta}Dis{t}_{C-ring}$ ) and delay S. Moreover, this optimal selection of the regular node to act as ring node will be carried out by a new hybrid algorithm referred to as Cuckoo Search Updated Sealion’s Attacking Technique (CSUSAT) that combines the standard crow search and sea lion optimization algorithm. At last, the supremacy of the presented approach is proved over other models.

Design and Analysis of Soft Computing Based Improved Routing Protocol in WSN for Energy Efficiency and Lifetime Enhancement

Mobile wireless sensor networks have been developed as a result of recent advancements in wireless technologies. Sensors in the network are low-cost and have a short battery life, in addition to their mobility. They are more applicable in terms of the essential properties of these networks. These networks have a variety of uses, including search and rescue operations, health and environmental monitoring, and intelligent traffic management systems, among others. According to the application requirements, mobile wireless sensor nodes are energy limited equipment, so energy conservation is one of the most significant considerations in the design of these networks. Aside from the issues posed by sensor node mobility, we should also consider routing and dynamic clustering. According to studies, cluster models with configurable parameters have a substantial impact on reducing energy usage and extending the network's lifetime. As a result, the primary goal of this study is to describe and select a smart method for clustering in mobile wireless sensor networks utilizing evolutionary algorithms in order to extend the network's lifetime and ensure packet delivery accuracy. For grouping sensor nodes in this work, the Genetic Algorithm is applied initially, followed by Bacterial Conjugation. The simulation's results show a significant increase in clustering speed acceleration. The speed of the nodes is taken into account in the suggested approach for calibrating mobile wireless sensor nodes.

Efficient Energy Utilization Algorithm through Energy Harvesting for Heterogeneous Clustered Wireless Sensor Network

The usefulness of wireless sensor networks has fascinated the world’s attention. Usage of low-power microcontrollers and wireless sensors to handle real-world problems such as environmental, medicinal, and structural monitoring has exploded. Wireless sensor nodes are extremely tiny and are designed for low-duty applications such as recording physical characteristics. Wireless sensor network operations such as sensing, calculations, and communication take extensively more energy than these low-powered sensor nodes. They are used both in attainable and inaccessible areas and are usually powered by batteries. Since the sensor is powered by batteries, replacing and charging the battery after its depletion are challenging. Manual battery replacement is hampered by geographical restrictions, which results in significant reduction of wireless sensor network performance and longevity. As a result, this study addresses the energy-constrained wireless sensor networks by creating a technological model to a heterogeneous clustered wireless sensor network in an outdoor application using solar-enabled energy harvesting photovoltaic cells. Due to lack of energy, this effort was employed to overcome the challenges of relatively restricted processing performance and limited radio frequency transmission bandwidth. The program was created to efficiently utilize the energy produced from solar panels and charge the batteries in a variety of ways. The algorithm also controls the discharging process. According to the results of extensive investigation and experimentation, the sensor is continuously operated even when there is no solar light. The batteries are charged on bright day and discharged at night and on overcast days. The algorithm is used to govern the energy supplement to rechargeable lithium-ion polymer batteries as well as a load (sensor). In the worst scenario (no solar light/cloudy, and sensors reporting data every 30 minutes), the present cluster head sensor with 6 ordinary nodes in the cluster lasts just 16.6667 days. But the life duration of the newly designed model algorithm has been raised to 54.16667 days. The normal sensor node’s life duration has also been enhanced from 50 to 91.66667 days.

Algorithm for energy-efficient interaction of wireless sensor network nodes

The actual problem of developing methods of interaction in wireless sensor networks focused on energy saving is discussed. It is shown that the operation of a wireless sensor network is built taking into account compromise mechanisms that make it possible to extend the life of the network in the presence of low-power sensor nodes on which the network is built. It is concluded that it is necessary to introduce new algorithms into the operation of wireless sensor networks, which make it possible to reduce the number of operations when calculating a route, transmitting data, or other operations without losing functionality, but contributing to a reduction in energy consumption. The paper proposes one of such algorithms that develops the idea of clustering wireless sensor networks in order to reduce the power consumption of sensor nodes by transferring some of the functions to the head nodes of the clusters. Unlike the well-known adaptive clustering algorithm with low energy consumption LEACH, the proposed algorithm is based on swarm intelligence and allows choosing not only the head nodes of clusters in the current round of functioning of the wireless sensor network, but also promising nodes that become heads of clusters in subsequent rounds. If we consider that one cycle of the wireless sensor network consists of a certain predetermined number of rounds, then the procedure for searching for cluster heads can be performed not at the beginning of each round, but only at the beginning of each cycle of the wireless sensor network. It is shown that the determination of the heads of wireless sensor network clusters in the future allows to reduce the total energy consumption and thereby increase the duration of the network life cycle. The advantage of adding the bee swarm algorithm to the wireless sensor network clustering procedure is demonstrated in terms of such indicators as the time of death of the first sensor node, the dependence of the number of functioning nodes on the network operation time and the data packet delivery coefficient. The wireless sensor network clustering procedure with the addition of the bee swarm algorithm to select cluster heads for the future can be useful when deploying a wireless sensor network in real applications.

RFDCS: A reactive fault detection and classification scheme for clustered wsns

RFDCS: A reactive fault detection and classification scheme for clustered wsns

A Survey on Cluster Head Selection and Cluster Formation Methods in Wireless Sensor Networks

In recent years, wireless sensor networks (WSNs) have been growing rapidly because of their ability to sense data, communicate wirelessly, and compute data efficiently. These networks contain small and low-powered sensor nodes that organize and configure themselves to carry out their functions. Even though WSNs are cheap, easy to deploy, flexible, and efficient, there are some challenges in terms of energy efficiency and network lifetime. Clustering in WSNs is the most reliable solution for the challenges, in which nodes are grouped into few clusters, and a cluster head (CH) is selected for data aggregation and data transfer to the base station (BS). However, there are still many challenges such as energy hole and isolated node problems that exist because of inefficient CH selection and cluster formation methods. In this work, we comprehensively reviewed various nonmetaheuristic and metaheuristic methods for CH selection and cluster formation that are used in networks from various environmental settings, for a better understanding of how the aforementioned problems are tackled by some authors. Moreover, the methods’ parameter settings, advantages, limitations, and future directions are presented with a brief performance summary of the approaches.

Nodes clustering and multi-hop routing protocol optimization using hybrid chimp optimization and hunger games search algorithms for sustainable energy efficient underwater wireless sensor networks

Clustering and routing processes in underwater wireless sensor networks (UWSNs) are challenging tasks in the underwater environment due to the multiplicity of sensor nodes, transmission bandwidth, and limited energy resources. In order to address the shortcomings mentioned above, this paper proposes a novel hybrid Chimp Optimization and Hunger Games Search (ChOA-HGS) algorithms for clustering and multi-hop routing optimization in UWSNs. In this approach, first, the ChOA is used to choose cluster heads and efficiently structure clusters. Then, the HGS-based routing procedure is used to determine the network’s best pathways. The proposed approach combines the advantages of clustering and routing, resulting in optimal network lifetime and energy efficiency. The proposed ChOA-HGS is validated using a variety of measures after it is simulated using three different scenarios. In order to evaluate the performance of the ChOA-HGS, results are compared to PSO, MPSO, IPSO-GWO, TEEN, and LEACH. The results show that the ChOA-HGS outperformed other benchmarks in terms of lifetime and energy consumption.

A novel approach for clustering and routing in WSN using genetic algorithm and equilibrium optimizer

SummaryThe Internet of Things (IoT) is a new concept in the world of technology and information and has many applications in industry, communications, and various other fields. In the lowest layer of the IoT, wireless sensor networks (WSNs) play an important and pivotal role. WSN consists of a large number of sensors and is commonly used to monitor a target. It is important to reduce energy consumption in WSNs to extend network life, since it is usually impossible to replace sensor batteries. In this paper, a novel clustering and routing method is proposed. It is mainly based on genetic algorithms and equilibrium optimization. To improve scalability, the sensor nodes are clustered in the first phase using the genetic algorithm and then the best cluster heads are selected. It follows by the next step, where each node sends its collected data to the cluster head. The cluster heads first aggregate the received data and then use a multihop optimization routing algorithm by the new equilibrium optimization algorithm to send the data to the base station. Specifically, the main contribution of this work is to reduce energy consumption in WSN using a genetic algorithm for clustering and an equilibrium optimization algorithm for choosing the path between the cluster heads and the base station. The proposed method was simulated in MATLAB software, and it was compared with five other methods. It was observed that the proposed method outperforms other methods in terms of energy consumption, network lifespan, and the number of packages delivered.

Game theory based node clustering for cognitive radio wireless sensor networks

Cognitive radio wireless sensor networks (CRWSN) is a promising technology for developing bandwidth constrained applications. Future Internet of Things (IoT) applications may extensively use CRWSN. CRWSN consists of cognitive radio enabled sensor nodes which are energy constrained, in general. Hierarchical cluster based approach for overall network management is suitable for network stability and scalability. Thus node clustering is an important problem in CRWSN setup. Objective of this work is to develop a suitable node clustering algorithm for CRWSN, in which nodes are expected to be mobile. A node clustering protocol for CRWSN has been proposed in this paper. The proposed clustering protocol is based on evolutionary game theory (EGT). Initial clusters are formed through a simple partitioning approach. Eventually, initial clusters are merged to form the final clusters. After forming the clusters by the resourceful sink node, the cluster head nodes for respective clusters are determined. The sink node runs the EGT based algorithm to identify the most capable node as the cluster head. The strength of this approach is that while identifying the cluster head node, various parameters such as residual energy level, geographic location, mobility, and the probability of primary user (PU) arrival are considered. The clusters and therefore, the cluster head nodes are distributed uniformly in the geographical area. The proposed clustering protocol has been compared with LEACH, RARE and the spectrum aware clustering algorithm. The simulation results show that the proposed clustering protocol outperforms all these similar node clustering protocols. On average, the proposed protocol outperforms the benchmarks protocols by 25% in terms of number of high energy nodes selected as cluster head, by 37% in terms of uniform geographical distribution of cluster head nodes, by 23% in terms of total energy consumed during the simulation time, and by 27% in terms of network lifetime. The future scope of the work has been outlined.

Secure Critical Data Reclamation Scheme for Isolated Clusters in IoT-Enabled WSN

Internet of Things (IoT) comprises of a huge number of connected devices that can communicate within the same network and across the networks. IoT-enabled wireless sensor networks (WSNs) are getting growing interest due to its wide applicability in healthcare, patient monitoring, transportation, and surveillance. The main issue is that the network is mostly deployed in hostile environments where an attacker may physically destroy the CHs or other technical fault may occur. It isolates the cluster and causes loss of sensitive data from that region. This article presents a critical data reclamation (CDR) protocol that provides secure data transmission for isolated clusters. We present the data transfer and data aggregation algorithms for sensing nodes and data receiving and extraction at CH and sink. We performed extensive simulations using NS-2.35. The results prove the dominance of CDR in contrast to counterparts in terms of communication cost, energy consumption, and resilience.