Clustered Wireless Sensor Networks Research Articles

Fuzzy‐based techniques for clustering in wireless sensor networks (WSNs): Recent advances, challenges, and future directions

SummaryA wireless sensor network (WSN) is a network of tiny sensors deployed to collect data. These sensors are powered with batteries that have limited power. Recharging and/or replacement of these batteries, however, are not always feasible. Over the past few years, WSN applications are being deployed in diverse fields such as military, manufacturing, healthcare, agriculture, and so on. With the ever‐increasing applications of WSNs, improving the energy efficiency of the WSNs still remains to be a challenge. Applying fuzzy logic to the problem of clustering exploits the uncertainty associated with the factors that affect the lifetime of these sensors and enables the development of models that would improve their performance in real‐world applications. We present a comprehensive review of various fuzzy‐based techniques for clustering in WSNs whose main goal is to optimize energy usage in WSNs while simultaneously improving their overall performance.

THSI-RP: A two-tier hybrid swarm intelligence based node clustering and multi-hop routing protocol optimization for wireless sensor networks

Wireless sensor networks exploit clustering and routing techniques to improve energy efficiency, but these methods are generally considered as a non-deterministic polynomial (NP-hard) problem. To tackle this problem, we propose a novel two-tier hybrid swarm intelligence-based hierarchical routing protocol (THSI-RP). In the first tier, THIS-RP incorporates a hybrid swarm intelligence algorithm that combines Grey Wolf Optimization (GWO) and Marine Predators Algorithm (MPA) for the clustering algorithm. By incorporating the wolf factors from GWO into MPA as elite predators, we enhance the network search efficiency. This algorithm achieves adaptive optimal clustering with a controllable scale, considering the residual energy of nodes, relative distance between nodes, and node centrality. In the second tier, the routing algorithm uses a hybrid SI algorithm based on GWO and the graph model. First, we integrate distance and energy factors to dynamically select forwarding nodes using GWO. Then, based on the distance and energy balance principle, a weight cost function is established and combined with the minimum spanning tree method to construct a communication routing tree between the forwarding node and the base station to achieve optimized inter-cluster multi-hop routing. Simulation results demonstrate that THSI-RP outperforms several typical routing protocols in various metrics over EEUC, LPSO, LGWO, and LACO. For instance, THSI-RP achieves percentage improvement total packets by 290%, 10.9%, 8.9%, 19.9%, respectively. It also performs better in FND by 88.7%, 10.9%, 8.6%, and 16.3%, in HDN by 86.6%, 8.1%, 6.3%, and 17.1%, and in ADN by 41.7%, 8.2%, 6.4%, 18.3%.

Energy centric reputation index and fuzzy-based clustering for wireless sensor networks

Energy conservation is essential for a wireless sensor network to enhance its network lifetime. Clustering is one of the most important energy conservation techniques that prolong the WSN by providing balanced energy depletion across the network. Various clustering strategies have been proposed in the past to enhance the efficiency of the network till now. In the last two decades, fuzzy inference system based clustering solution has been proven to provide promising solutions. The existing fuzzy-based clustering algorithms consider the naïve network parameters as input that saves only a limited amount of energy in the network. However, in wireless sensor networks, the requirement is to make them highly energy efficient through an effective clustering strategy. To achieve effective clustering there is a need for energy-centric network parameters for the clustering instead of naïve network parameters. In this article, a novel energy-centric reputation index is proposed in which the naïve network parameters are unified with energy parameters in the background. Further, the reputation index scores are passed into a fuzzy-based approach to obtain the fitness value of the node chosen as the cluster head. Thus, in this paper, an energy-centric reputation index and fuzzy-based clustering(ECRIF) model is proposed for wireless sensor networks. The proposed model provides a highly energy-efficient wireless sensor network and is designed to work in two phases. In phase 1, three reputation indexes concerning internodal distance, distance to the sink, and the degree of the node is calculated while considering their energy consumption pattern in the background. In phase 2, the three reputation indexes are passed onto the fuzzy system as fuzzy input parameters to obtain the fitness value of the nodes for cluster head selection. Simulation results confirm that the proposed protocol enhances the overall lifetime of the network on an average by 70% in terms of the first node dies, 61% in terms of half node dies, and 78% in terms of the last node dies when compared with two other existing clustering models.

ACRA: Adaptive meta-heuristic based Clustering and Routing Algorithm for IoT-assisted wireless sensor network

ACRA: Adaptive meta-heuristic based Clustering and Routing Algorithm for IoT-assisted wireless sensor network

Energy balanced reliable and effective clustering for underwater wireless sensor networks

While sending data from a submerged wireless sensor network, more power is necessary due to the additional distance the signal must travel as well as the higher amount of energy lost along the route. Also, the acoustic medium generated by the shadow and the acoustic animals alters the transmission between the source and destination nodes. Multipath fading, significant path loss, and various interferences are some of the difficulties that come with communication at larger depths. The connection strength was variable, there was a low bandwidth, a high salt content, high pressure, high temperature, a sinkhole, and the route curved. These variables influence a variety of network aspects, including energy efficiency, link quality, and packet delivery ratio (PDR). The Energy Balanced Reliable and Effective Clustering (EBREC) method was created to ensure that data packets reach at their destination intact. This was done in order to avoid the complications caused by these considerations. PDR and transmission loss reduction are other targets of the presented effort. The anticipated EBREC’s performance is assessed by comparing it to many already defined benchmarks. According to the evaluation report, the simulation results reveal that EBREC performs very well when compared to the other methods in terms of Residual Energy of 0.615 J, Consumed Energy of 2.3 mJ, Throughput of 10Mbps, PDR of 97.6 %, Network Lifetime of 1750 sec, Number of Alive Nodes of 91%. Hence, the simulation results reveal that the EBREC technique beats competing approaches.

Unsupervised Learning-Based WSN Clustering for Efficient Environmental Pollution Monitoring.

Wireless Sensor Networks (WSNs) have been adopted in various environmental pollution monitoring applications. As an important environmental field, water quality monitoring is a vital process to ensure the sustainable, important feeding of and as a life-maintaining source for many living creatures. To conduct this process efficiently, the integration of lightweight machine learning technologies can extend its efficacy and accuracy. WSNs often suffer from energy-limited devices and resource-affected operations, thus constraining WSNs' lifetime and capability. Energy-efficient clustering protocols have been introduced to tackle this challenge. The low-energy adaptive clustering hierarchy (LEACH) protocol is widely used due to its simplicity and ability to manage large datasets and prolong network lifetime. In this paper, we investigate and present a modified LEACH-based clustering algorithm in conjunction with a K-means data clustering approach to enable efficient decision making based on water-quality-monitoring-related operations. This study is operated based on the experimental measurements of lanthanide oxide nanoparticles, selected as cerium oxide nanoparticles (ceria NPs), as an active sensing host for the optical detection of hydrogen peroxide pollutants via a fluorescence quenching mechanism. A mathematical model is proposed for the K-means LEACH-based clustering algorithm for WSNs to analyze the quality monitoring process in water, where various levels of pollutants exist. The simulation results show the efficacy of our modified K-means-based hierarchical data clustering and routing in prolonging network lifetime when operated in static and dynamic contexts.

Low energy split-cluster wireless acceleration sensors for motion and posture change recognition in sports

The rise of the national fitness trend has led more and more people to join the ranks of physical fitness, and the current physical education resources and human resources have not been able to keep pace with the development of sports. Due to the lack of guidance, many beginners of sports are not standardized, and their movements are not effective and even cause injuries. Sports action recognition technology can make up for this lack and realize the action recognition and guidance for sports players. In this paper, we build a sports movement and posture recognition model based on acceleration sensors, introduce Selectable Mode Vocoder (SMV) algorithm to optimize the performance, and ensure the effectiveness of sensor data transmission through a low energy multi-layer clustered wireless sensor network. The experiment mainly compares the generation results of the first energy depleted node in the entire network with the number of rounds with energy depleted nodes reaching 10 %. The experimental results show that the low-energy multi-layer clustered wireless sensor network effectively shortens the data transmission path, reduces the energy consumption of nodes, extends the network life time, ensures the data transmission effectiveness, and provides network guarantees for sports action recognition. In addition, in the comparison experiments, the action recognition model in this paper shows better recognition effect and efficiency with obvious performance advantages.

Joint Clustering and Routing Optimisation for Low-power Wireless Sensor Networks

Wireless sensor networks (WSNs) have been one of the fields that have attracted a lot of attentions from many scientific researchers in recent years. The sensor nodes of the network are fixed or moved to detect the environment and impart the data accumulated from the remote monitored regions via wireless connections. It is indicated in complex environments such as forests, deep seas, urban areas, etc., the sensor nodes in WSNs are usually tiny and battery-driven devices. Thus, energy-effective data accumulation methods required to improve the network’s lifetime are very necessary. In this paper, we propose a joint technique of fuzzy clustering and heuristic ant routing (FCHAR) to save the energy for low-power WSNs. Simulation results are shown to demonstrate the benefits of the proposed FCHAR compared to other conventional ones.

Survey on Various Aspects of Clustering in Wireless Sensor Networks Employing Classical, Optimization, and Machine Learning Techniques

A wide range of academic scholars, engineers, scientific and technology communities are interested in energy utilization of Wireless Sensor Networks (WSNs). Their extensive research is going on in areas like scalability, coverage, energy efficiency, data communication, connection, load balancing, security, reliability and network lifespan. Individual researchers are searching for affordable methods to enhance the solutions to existing problems that show unique techniques, protocols, concepts, and algorithms in the wanted domain. Review studies typically offer complete, simple access or a solution to these problems. Taking into account this motivating factor and the effect of clustering on the decline of energy, this article focuses on clustering techniques using various wireless sensor networks aspects. The important contribution of this paper is to give a succinct overview of clustering.

Pareto Multi-objective Termite Colony Optimization Based EDT Clustering for Wireless Chemical Sensor Network

Pareto Multi-objective Termite Colony Optimization Based EDT Clustering for Wireless Chemical Sensor Network

Trust-based clustering and best route selection strategy for energy efficient wireless sensor networks

Energy efficiency is one of the most researchable topics of the energy constrained Wireless Sensor Networks (WSN). Due to the widespread usage of WSN in real-time applications, network lifetime is the serious concern and numerous research solutions are presented in the literature for handling this issue. Several techniques are employed to handle energy consumption and the most promising techniques are clustering and routing other than data compression. Hence, this paper presents a clustering and routing-based solution for energy efficient WSN with trust notion as its base. The nodes are clustered with the help of Dempster-Shafer Theory (DST), while the best route is selected by employing Lion Optimization Algorithm (LOA). The work efficiency of the proposed work is proven with reasonable outcomes with respect to different performance measures. This work proves an average packet delivery rate of 94.24%, average latency of 13.43 s and 191 alive nodes at the end of 500th second better network lifetime when compared to the existing works.

An Extensive Analysis of Wireless Sensor Network Clustering Routing Methods Based on LEACH

The creation and use of sensor networks has piqued a lot of interest. Remote sensor network is an assortment of countless little hubs which goes about as switches too. The limited power supply that these nodes carry is non-replaceable and non-rechargeable, making energy consumption a significant issue. In order to extend the network's lifespan, energy conservation is a crucial issue. The choice of routing method is crucial to regulating energy consumption because the sensor nodes also function as routers. Energy Efficient Routing in Wireless Sensor Networks is the subject of this paper's analysis and research, which also includes a description of the wireless sensor network framework.

Node clustering and data aggregation in wireless sensor network using sailfish optimization

Node clustering and data aggregation in wireless sensor network using sailfish optimization

Improved African Buffalo Optimization-Based Energy Efficient Clustering Wireless Sensor Networks using Metaheuristic Routing Technique

Improved African Buffalo Optimization-Based Energy Efficient Clustering Wireless Sensor Networks using Metaheuristic Routing Technique

Performance Analysis by Improving Energy Efficiency in IoT Based Wireless Sensor Network for Routing Algorithm

Objectives: To improve the energy efficiency in IoT-based wireless sensor networks. To increase the sensor node lifetime in any wireless sensor network (WSN) based on the Energy-efficient cluster and routing in WSN. The advancement in the electronic industry has to develop the low powerconsuming and low-cost devices for multiple purposes. Methods: A new algorithm is proposed to improve the energy efficiency in IoT-based WSN for routing algorithms. The low power consuming devices are wisely chosen for the respected application and their power management. This study is intended to provide efficient routing algorithms with less energy consumption in wireless sensor networks. Findings: A wireless sensor network deployed with a number of non-static nodes in a communication network are beneficial for numerous fields such as the military, medical sector, structural health monitoring, transportation industries, etc. In this paper, the performance parameters like average throughput, average end-to-end delay, packet delivery ratio, average node energy, and normalized routing load are analyzed. Novelty: We designed and simulated the Distributed IoT-based wireless network system to analyze the information of nodes roaming in the vicinity of the zone area. In proposed technique energy efficiency is increased compared to the conventional algorithm. In node centric routing protocol, energy efficiency is increased from 55% to 68%. In data centric routing protocol, energy efficiency is increased from 65% to 75%. In source-initiated routing protocol, energy efficiency is increased from 68% to 80%. In destination-initiated routing protocol, energy efficiency is increased from 72% to 85%. Keywords: Ad Hoc OnDemand stance Vector (AODV); DestinationSequenced DistanceVector Routing (DSDV); Dynamic source routing (DSR); MQ Telemetry Transport (MQTT); Quality of Services (QoS); Wireless Sensor Network (WSN)

Energy and Distance Aware Multi-Objective Firebug Swarm Optimization Based Clustering and Routing in Wireless Sensor Networks

In recent years, Wireless Sensor Networks (WSNs) have received increased international attention due to advancements in the communication, electronics, and information fields. These sensor networks integrate a huge number of sensor nodes to track rapidly changing physical events. The key improvement is that these nodes can be easily positioned in any environment due to their small size. Therefore, maintaining network connectivity is crucial in WSNs. If some nodes become unavailable, the connectivity of the routing path fails, resulting in significant packet loss in the WSN. Therefore, many types of research in WSNs have focused on energy efficiency, where energy consumption is minimized to improve the network lifetime. Here, Energy and Distance-aware Multi-Objective Firebug Swarm Optimization (ED-MOFSO) is projected to achieve an energy efficient process. Furthermore, ED-MOFSO minimizes delays to enhance performance measures. From the overall simulation, it shows that ED-MOFSO achieves improved metrics, including residual energy (14.29 J), delay (11.1 ms), packet delivery ratio (0.994), routing overhead (0.10), and throughput (1.233 Mbps) when compared to conventional Elephant Herding Optimization (EHO) greedy and Ant Colony Optimization Integrated Glow-worm Swarm Optimization (ACI-GSO).

Enhancing Parameters of LEACH Protocol for Efficient Routing in Wireless Sensor Networks

The increasing popularity of wireless sensor networks can be attributed to their unique characteristics, which include high density, low power consumption, software programmability, and long-term accuracy. This paper presents a thorough analysis of key parameters for sensor network applications, including network lifetime, packet delivery ratio, energy-efficient transmission, and dead node ratio. This paper examines the effectiveness of clustering in wireless sensor networks for achieving efficiency. The research focuses on the Low-Energy Adaptive Clustering Hierarchy (LEACH) protocol and proposes an enhanced algorithm that improves network lifetime, throughput, and the number of living nodes. This paper evaluates the performance of the improved LEACH protocol and compares it to other protocols currently in use. The results indicate that the proposed method significantly improves the performance of the protocol, making it a promising method for efficient routing in wireless sensor networks. This study is an important contribution to the field of wireless sensor networks and can aid in the creation of more efficient and effective protocols for various sensor network applications.

Motion quality testing based on energy sensing data access algorithm in dynamically tunable cluster wireless sensor networks

Sports quality is the comprehensive performance of a person's sports knowledge system and sports ability, and the previous measurement methods can not fully obtain relevant information. Therefore, this paper constructs the sports quality evaluation index based on questionnaire survey and expert consultation, and obtains the sports quality measurement data through wireless sensor network. In order to ensure the real-time and effectiveness of data, this paper introduces a dynamically adjustable cluster strategy into the network aware data access algorithm. The load balancing performance of the three algorithms is compared. The results show that the algorithm improves the load balance of the network, increases the probability of successful data query, and extends the network life cycle. And the energy consumption is reduced. This makes the algorithm have a longer life cycle and better overall performance, and can ensure the validity, accuracy and effectiveness of the motion quality test data. The comprehensive requirements of 1000 m run and pull up test on athletes' sports quality are high. The average measurement score is low. In women's sports quality test, the correlation between sit ups and 50 m and 800 m runs is strong, which has certain requirements for the core strength of athletes. The analysis of college students' physical fitness measurement results shows that the measurement and evaluation technology in this paper can effectively measure and obtain physical fitness data, and analyze it, providing data support for the development of sports literacy projects.

Spread Spectrum based QoS aware Energy Efficient Clustering Algorithm for Wireless Sensor Networks

Wireless sensor networks (WSNs) are composed of small, resource-constrained sensor nodes that form self-organizing, infrastructure-less, and ad-hoc networks. Many energy-efficient protocols have been developed in the network layer to extend the lifetime and scalability of these networks, but they often do not consider the Quality of Service (QoS) requirements of the data flow, such as delay, data rate, reliability, and throughput. In clustering, the probabilistic and randomized approach for cluster head selection can lead to varying numbers of cluster heads in different rounds of data gathering. This paper presents a new algorithm called "Spread Spectrum based QoS aware Energy Efficient Clustering for Wireless sensor Networks" that uses spread spectrum to limit the formation of clusters and optimize the number of cluster heads in WSNs, improving energy efficiency and QoS for diverse data flows. Simulation results show that the proposed algorithm outperforms classical algorithms in terms of energy efficiency and QoS.

Outdoor Air Quality Monitoring with Enhanced Lifetime-enhancing Cooperative Data Gathering and Relaying Algorithm (E-LCDGRA) Based Sensor Network

The air continues to be an extremely substantial part of survival on earth. Air pollution poses a critical risk to humans and the environment. Using sensor-based structures, we can get air pollutant data in real-time. However, the sensors rely upon limited-battery sources that are immaterial to be alternated repeatedly amid extensive broadcast costs associated with real-time applications like air quality monitoring. Consequently, air quality sensor-based monitoring structures are lifetime-constrained and prone to the untimely loss of connectivity. Effective energy administration measures must therefore be implemented to handle the outlay of power dissipation. In this study, the authors propose outdoor air quality monitoring using a sensor network with an enhanced lifetime-enhancing cooperative data gathering and relaying algorithm (E-LCDGRA). LCDGRA is a cluster-based cooperative event-driven routing scheme with dedicated relay allocation mechanisms that tackle the problems of event-driven clustered WSNs with immobile gateways. The adapted variant, named E-LCDGRA, enhances the LCDGRA algorithm by incorporating a non-beaconaided CSMA layer-2 un-slotted protocol with a back-off mechanism. The performance of the proposed E-LCDGRA is examined with other classical gathering schemes, including IEESEP and CERP, in terms of average lifetime, energy consumption, and delay