Clustered Wireless Sensor Networks Research Articles

An Improved Lifetime and Energy Consumption with Enhanced Clustering in WSNs

A wireless sensor network (WSN) is made up of sensor nodes that communicate via radio waves in order to conduct sensing functions. In WSN, the location of the base station is critical. Although base stations are fixed, they may move in response to data received from sensor nodes under specific conditions. Clustering is a highly efficient approach of minimising energy use. The issues of extending the life of WSNs and optimising their energy consumption have been addressed in this paper. It has been established that integrating mobile sinks into wireless sensor networks extends their longevity. Thus, this research proposes an optimal clustering and routing technique for optimising the energy usage and lifetime of WSNs. To minimise energy consumption, this research employs movable and stationary sink nodes. The K-Medoid clustering model is used to generate the initial number of nodes in the various clusters. After that, the cluster head is chosen using a hybrid Interval Type-2 Fuzzy technique that takes three aspects into account: residual energy, node centrality, and neighbourhood. A highly efficient backup cluster head (CH) collecting system can provide in significant energy savings while also prolonging the system’s life. Finally, better Reinforcement learning combined with a Genetic algorithm routing protocol is used to ensure effective data delivery. The suggested approach’s efficacy is evaluated in comparison to earlier approaches utilising residual node energy, delay or average delay, packet delivery ratio, throughput, network longevity, average energy consumption, and multiple alive nodes. In experiments, the proposed strategy outperforms existing strategies.

МОДЕЛЮВАННЯ ТА УДОСКОНАЛЕННЯ СЕНСОРНОЇ МЕРЕЖІ СИСТЕМИ ОБЛІКУ СПОЖИВАННЯ ЕНЕРГЕТИЧНИХ РЕСУРСІВ У МІКРОРАЙОНІ

The research is aimed at the development of new algorithms for the selection of a main node and methods of placement of sensor nodes for the effective construction of wireless sensor networks (WSNs), therefore it is relevant.The analysis of the literature made it possible to establish that many scientists were engaged in the research of methods of building smart energy systems for a smart city management. However, the researchers did not analyze the potential and real threat of data transmission from energy consumption control sensors.The purpose of the research is to improve the accuracy of control and accounting of energy resources consumed by residents for the neighborhood using wireless sensor networks.The article analyzes the state of the art in the field of research on wireless sensor networks (WSNs), determines the most important characteristics and structure of WSNs in the organization of the automated system of control and accounting of energy resources (ASCOE). An analysis of the existing algorithms for routing, self-organization and selection of ta main node of the cluster in wireless sensor networks of sensors for controlling the consumption of energy parameters by the consumer was carried out. The duration of the network's life cycle, the period of stability and the bandwidth of the BSM was evaluated based on the ratio between the radius of coverage and the radius of the communication range.Studies have shown that the applied algorithm for selecting the main node of the cluster based on fuzzy logic using Voronoi diagrams significantly increases the life cycle of the sensor network compared to the LEACH and Fuzzy C-Means algorithms

Efficient Cluster Heads Selection Based on Index-Modulation in Wireless Sensor Networks

Index-Modulation (IM) has been recently emerged as a promising direction towards improving error performance and spectral efficiency in wireless networks. To this end, IM-based Wireless Sensor Networks (IM-WSN) has been recently applied as a decision gathering scheme in clustered Wireless Sensor Networks, by which a significant improvement in the decision error rate (DER) is achieved. Later, following the same IM principle, efficient IM-WSN scheme (EIM-WSN) has been introduced, but with the emphasis on reducing the computational complexity of IM-WSN while improving the error performance. Although in these schemes cluster heads (CHs) vary dynamically, still they suffer from the unfair distribution of the CH role among nodes. As a result, nodes energy will be unequally dissipated, and hence the network lifetime will be deteriorated. In this study, aiming at balancing the CH role among cluster nodes, a new decision reporting scheme, namely index-shift, is proposed. The performance of proposed scheme is analysed in terms of DER and network lifetime and compared with the previous IM reporting schemes, IM-WSN and EIM-WSN. Analytical and simulation results show that the proposed scheme demonstrates fair distribution of CH role among all cluster nodes. This, in turn, led to extend the network lifetime up to 50% compared to other schemes, in addition to acheive DER that is better than IM-WSN and almost identical to EIM-WSN.

Clustered Wireless Sensor Network in Precision Agriculture via Graph Theory

Food security and sustainable development is making a mandatory move in the entire human race. The attainment of this goal requires man to strive for a highly advanced state in the field of agriculture so that he can produce crops with a minimum amount of water and fertilizer. Even though our agricultural methodologies have undergone a series of metamorphoses in the process of a present smart-agricultural system, a long way is ahead to attain a system that is precise and accurate for the optimum yield and profitability. Towards such a futuristic method of cultivation, this paper proposes a novel method for monitoring the efficient flow of a small quantity of water through the conventional irrigation system in cultivation using Clustered Wireless Sensor Networks (CWSN). The performance measure is simulated the creation of edge-fixed geodetic clusters using Mat lab’s Cup-carbon tool in order to evaluate the suggested irrigation process model’s performance. The findings of blocks 1 and 2 are assessed. Each signal takes just a little amount of energy to communicate, according to the performance. It is feasible to save energy while maintaining uninterrupted communication between nodes and cluster chiefs. However, the need for proper placement of a dynamic control station in WSN still exists for maintaining connectivity and for improving the lifetime fault tolerance of WSN. Based on the minimum edge fixed geodetic sets of the connected graph, this paper offers an innovative method for optimizing the placement of control stations. The edge-fixed geodetic cluster makes the network fast, efficient and reliable. Moreover, it also solves routing and congestion problems.

Fuzzy with Metaheuristics Based Routing for Clustered Wireless Sensor Networks

Wireless sensor network (WSN) plays a vital part in real time tracking and data collection applications. WSN incorporates a set of numerous sensor nodes (SNs) commonly utilized to observe the target region. The SNs operate using an inbuilt battery and it is not easier to replace or charge it. Therefore, proper utilization of available energy in the SNs is essential to prolong the lifetime of the WSN. In this study, an effective Type-II Fuzzy Logic with Butterfly Optimization Based Route Selection (TFL-BOARS) has been developed for clustered WSN. The TFL-BOARS technique intends to optimally select the cluster heads (CHs) and routes in the clustered WSN. Besides, the TFL-BOARS technique incorporates Type-II Fuzzy Logic (T2FL) technique with distinct input parameters namely residual energy (RE), link quality (LKQ), trust level (TRL), inter-cluster distance (ICD) and node degree (NDE) to select CHs and construct clusters. Also, the butterfly optimization algorithm based route selection (BOARS) technique is derived to select optimal set of routes in the WSN. In addition, the BOARS technique has computed a fitness function using three parameters such as communication cost, distance and delay. In order to demonstrate the improved energy effectiveness and prolonged lifetime of the WSN, a wide-ranging simulation analysis was implemented and the experimental results reported the supremacy of the TFL-BOARS technique.

Blockchain Enabled Metaheuristic Cluster Based Routing Model for Wireless Networks

With recent advancements made in wireless communication techniques, wireless sensors have become an essential component in both data collection as well as tracking applications. Wireless Sensor Network (WSN) is an integral part of Internet of Things (IoT) and it encounters different kinds of security issues. Blockchain is designed as a game changer for highly secure and effective digital society. So, the current research paper focuses on the design of Metaheuristic-based Clustering with Routing Protocol for Blockchain-enabled WSN abbreviated as MCRP-BWSN. The proposed MCRP-BWSN technique aims at deriving a shared memory scheme using blockchain technology and determine the optimal paths to reach the destination in clustered WSN. In MCRP-BWSN technique, Chimp Optimization Algorithm (COA)-based clustering technique is designed to elect a proper set of Cluster Heads (CHs) and organize the selected clusters. In addition, Horse Optimization Algorithm (HOA)-based routing technique is also presented to optimally select the routes based on fitness function. Besides, HOA-based routing technique utilizes blockchain technology to avail the shared memory among nodes in the network. Sensor nodes are treated as coins whereas the ownership handles the sensor nodes and Base Station (BS). In order to validate the enhanced performance of the proposed MCRP-BWSN technique, a wide range of simulations was conducted and the results were examined under different measures. Based on the performance exhibited in simulation outcomes, the proposed MCRP-BWSN technique has been established as a promising candidate over other existing techniques.

Improved Rat Swarm Based Multihop Routing Protocol for Wireless Sensor Networks

Wireless sensor networks (WSNs) encompass a massive set of sensor nodes, which are self-configurable, inexpensive, and compact. The sensor nodes undergo random deployment in the target area and transmit data to base station using inbuilt transceiver. For reducing energy consumption and lengthen lifetime of WSN, multihop routing protocols can be designed. This study develops an improved rat swarm optimization based energy aware multi-hop routing (IRSO-EAMHR) protocol for WSN. An important intention of the IRSO-EAMHR method is for determining optimal routes to base station (BS) in the clustered WSN. Primarily, a weighted clustering process is performed to group the nodes into clusters and select cluster heads (CHs). Next, the IRSO-EAMHR approach derives a fitness function containing three input parameters (residual energy, distance, and node degree) for routing process. The IRSO technique was designed by the integration of Levy movement concepts into the traditional RSO algorithm. The experimental result analysis of the IRSO-EAMHR technique is carried out and the outcomes are examined in various aspects. The simulation outcomes demonstrate the promising performance of the IRSO-EAMHR technique over the recent state of art approaches.

Design of Evolutionary Algorithm Based Unequal Clustering for Energy Aware Wireless Sensor Networks

Wireless Sensor Networks (WSN) play a vital role in several real-time applications ranging from military to civilian. Despite the benefits of WSN, energy efficiency becomes a major part of the challenging issue in WSN, which necessitate proper load balancing amongst the clusters and serves a wider monitoring region. The clustering technique for WSN has several benefits: lower delay, higher energy efficiency, and collision avoidance. But clustering protocol has several challenges. In a large-scale network, cluster-based protocols mainly adapt multi-hop routing to save energy, leading to hot spot problems. A hot spot problem becomes a problem where a cluster node nearer to the base station (BS) tends to drain the energy much quicker than other nodes because of the need to implement more transmission. This article introduces a Jumping Spider Optimization Based Unequal Clustering Protocol for Mitigating Hotspot Problems (JSOUCP-MHP) in WSN. The JSO algorithm is stimulated by the characteristics of spiders naturally and mathematically modelled the hunting mechanism such as search, persecution, and jumping skills to attack prey. The presented JSOUCP-MHP technique mainly resolves the hot spot issue for maximizing the network lifespan. The JSOUCP-MHP technique elects a proper set of cluster heads (CHs) using average residual energy (RE) to attain this. In addition, the JSOUCP-MHP technique determines the cluster sizes based on two measures, i.e., RE and distance to BS (DBS), showing the novelty of the work. The proposed JSOUCP-MHP technique is examined under several experiments to ensure its supremacy. The comparison study shows the significance of the JSOUCP-MHP technique over other models.

Neural Network Clustering and Swarm Intelligence-Based Routing Protocol for Wireless Sensor Networks: A Machine Learning Perspective.

With no requirement for an established network infrastructure, wireless sensor networks (WSNs) are well suited for applications that call for quick network deployment. Military training and emergency rescue operations are two prominent uses of WSNs. The individual network nodes must carry out routing and intrusion detection because there is no predetermined routing or intrusion detection in a wireless network. WSNs can only manage a certain volume of data, and doing so requires a significant amount of energy to process, transmit, and receive. Since sensors have a modest energy source and a constrained bandwidth, they cannot transmit all of their data to a base station for processing and analysis. Therefore, machine learning (ML) techniques are needed for WSNs to facilitate data transmission. Other current solutions have drawbacks as well, such as being less reliable, more susceptible to environmental changes, converging more slowly, and having shorter network lifetimes. This study addressed problems with wireless sensor networks and devised an efficient clustering and routing algorithm based on machine learning. Results from simulations demonstrate that the proposed system beats previous state-of-the-art models on a variety of metrics, including accuracy, specificity, and sensitivity (0.93, 0.93, and 0.92 respectively).

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

[This retracts the article DOI: 10.1155/2022/7940895.].

Energy Saving Multi-Relay Technique for Wireless Sensor Networks Based on Hw/Sw MPSoC System

Currently, we are witnessing a wide range of Wireless Sensor Networks (WSNs) in various fields of application. In most cases, these networks contain numerous battery-powered sensor nodes. Therefore, the lifetime of WSNs is intrinsically dependent. For this purpose, different studies have been conducted to reduce the power consumption. We propose a new technique for reducing the power consumption of clustered WSNs. We considered clusters in which sensors transmitted data to a Cluster Head (CH) by cooperative communication based on the Bit Error Probability (BEP) criterion for the multi-relay technique. Therefore, the proposed technique uses a multi-relay to reduce the transmission power to communicate between the source and destination which respects the predefined reliability level. The proof of the described technique was performed using a multiprocessor architecture. Second, we used an efficient software/hardware (Sw/Hw) architecture that utilized accelerator modules. The experimental results demonstrate significant gains in terms of energy consumption and workload compared to the SW implementation.

Investigation of Energy Efficient Clustering Algorithms in WSNs: A Review

In recent years, Wireless Sensor Networks (WSNs) are attracting more attention in many fields as they are extensively used in a wide range of applications, such as environment monitoring, the Internet of Things, industrial operation control, electric distribution, and the oil industry. One of the major concerns in these networks is the limited energy sources. Clustering and routing algorithms represent one of the critical issues that directly contribute to power consumption in WSNs. Therefore, optimization techniques and routing protocols for such networks have to be studied and developed. This paper focuses on the most recent studies and algorithms that handle energy-efficiency clustering and routing in WSNs. In addition, the prime issues in these networks are discussed and summarized using comparison tables, including the main features, limitations, and the kind of simulation toolbox. Energy efficiency is compared between some techniques and showed that according to clustering mode “Distributed” and CH distribution “Uniform”, HEED and EECS are best, while in the non-uniform clustering, both DDAR and THC are efficient. According to clustering mode “Centralized” and CH distribution “Uniform”, the LEACH-C protocol is more effective.

Optimal Cluster Head Selection in WSN with Convolutional Neural Network-Based Energy Level Prediction

Currently, analysts in a variety of nations have developed various WSN clustering protocols. The major characteristic is the Low Energy Adaptive Clustering Hierarchy (LEACH), which attained the objective of energy balance by sporadically varying the Cluster Heads (CHs) in the region. Nevertheless, because it implements an arbitrary number system, the appropriateness of CH is complete with suspicions. In this paper, an optimal cluster head selection (CHS) model is developed regarding secure and energy-aware routing in the Wireless Sensor Network (WSN). Here, optimal CH is preferred based on distance, energy, security (risk probability), delay, trust evaluation (direct and indirect trust), and Received Signal Strength Indicator (RSSI). Here, the energy level is predicted using an improved Deep Convolutional Neural Network (DCNN). To choose the finest CH in WSN, Bald Eagle Assisted SSA (BEA-SSA) is employed in this work. Finally, the results authenticate the effectiveness of BEA-SSA linked to trust, RSSI, security, etc. The Packet Delivery Ratio (PDR) for 100 nodes is 0.98 at 500 rounds, which is high when compared to Grey Wolf Optimization (GWO), Multi-Objective Fractional Particle Lion Algorithm (MOFPL), Sparrow Search Algorithm (SSA), Bald Eagle Search optimization (BES), Rider Optimization (ROA), Hunger Games Search (HGS), Shark Smell Optimization (SSO), Rider-Cat Swarm Optimization (RCSO), and Firefly Cyclic Randomization (FCR) methods.

An improved optimal energy aware data availability approach for secure clustering and routing in wireless sensor networks

AbstractMaintaining high residual energy is one of the major challenges of wireless sensor networks (WSNs), where nodes are moving at a random speed. In addition, data availability is also a major concern that must be maintained constantly during the packet transmission phase. In this research work, a Residual‐Energy‐Based Data Availability Approach (REDAA) for WSNs is developed to increase the network lifetime by focusing on the selection of stable routing paths and cluster heads. During the first phase of the work, the network model, and assumptions for route creations are adopted to effectively initialize the data transmission phase. During the second phase, a cluster was formed, and two categories of cluster heads were created based on quadrature low energy adaptive clustering hierarchy (Q‐LEACH) and multi‐hop low energy adaptive clustering (MH‐LEACH) algorithms to formalize the route and to make data available whenever requested. During the third phase, energy conservation routes are initialized, and data gathering is improved through slot‐based code division multiple access schemes. Simulation results demonstrate that the proposed REDAA approach can achieve an improvement in terms of throughput and energy consumption by 37% and 70% respectively when compared to MH‐LEACH approach, whereas in comparison to Q‐LEACH approach, these improvements are found to be 30% and 73% respectively.

MCEN: Maximum cooperative equilibrium WSN based on greedy prediction to reduce opposite transmission

In wireless sensor network (WSN) routing model design, energy efficiency is a key to WSN performance. Existing energy efficiency routing approaches transmit fused data packets through cluster head (CH) to save energy. However, some of these approaches do not make full use of WSNs’ energy because some sensor node transmitting paths are opposite to their transmission destination in these WSNs. Therefore, this paper proposes an approach named maximum cooperative equilibrium WSN (MCEN) to improve WSN energy utilization based on greedy policies and nodes energy consumption prediction. MCEN reconstructs the cluster structure to help nodes minimize their transmitting paths on which signals travel in the opposite direction to their destination. The energy-consumption prediction strategy in MCEN is to find the best cooperative partner for a node by predicting the energy consumption of the cooperative data transmission between the node and base station. CHs are voted to form clusters based on the maximum energy consumption prediction results. The approach can be embedded in other WSN clustering approaches, thereby further extending the overall lifetime of those WSNs. The simulation results show that the proposed method can greatly extend the lifetime of the entire network by more than 50%.

Switched Beam Array Antenna Optimized for Microwave Powering of 3-D Distributed Nodes in Clustered Wireless Sensor Network

The IoT applications like smart warehouse, smart industry, and smart vertical farming will require microwave power transmission (MPT) to the densely deployed low-power sensor nodes in 3-D space. To address this issue, a switched beam array antenna (SBA) is synthesized as an MPT system to power the proposed 3-D clustered wireless sensor network (WSN). The 3-D space is divided into nine clusters; each illuminated individually with RF power using a dedicated patch antenna subarray. The subarrays can be switched ON or OFF based on the energy requirement of the WSN cluster nodes resulting in efficient power transmission. Moreover, the low complexity of switched beam design reduces the cost and processing overhead as compared to the exiting adaptive beamforming systems in a dense WSN scenario. The subarray size and excitation coefficients are optimized with a constraint over the harvested dc power ( <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$P_{dc}$ </tex-math></inline-formula> ) to enhance the 3-D coverage. In addition, the optimal location of the cluster head (CH) is evaluated to minimize the average power consumption of WSN cluster. The subarrays are designed and fabricated with the evaluated excitation coefficients, and <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$P_{dc}$ </tex-math></inline-formula> measurements are carried out validating analytical results.

Energy‐aware redundancy‐aware clustering in wireless sensor networks using Spined Loach Searching Optimization

SummaryWireless sensor networks play dominant role in data communication in many engineering applications and faces lot of challenges in its implementation. Energy conservation is also significantly required in these systems as the sensor nodes are battery operated. Hence, the energy efficient‐based technique needs to be implemented in data transmission. The most widely used methods are clustering‐based data transmission and energy efficient routing detection. In clustering, the proper selection of cluster head (CH) node is important to ensure energy balancing. The CH node should have more residual energy as it does many transmissions in the network. Moreover, the location of CH node should be nearer to all member nodes in the cluster. The clustering process must also perform the job of avoiding unnecessary transmissions. By considering these factors, the CH node can be optimally selected using Spined Loach Searching Optimization algorithm. The food searching behavior and environmental suitability for spiny loach fish living are deliberated to develop this bio‐inspired algorithm. In addition, the redundant transmissions inside the clusters can be reduced by employing a new technique called Least Difference Threshold Based Similarity Grouping. This work improves energy efficiency comparing with the conventional low energy adaptive clustering hierarchy protocol, and it is evaluated by setting the network in simulation environment. The network lifetime is increased; energy consumption, delay of transmission and the message cost are reduced.

Cooperative Networking Strategy of UAV Cluster for Large-Scale WSNs

To solve the shortcomings of wireless sensor networks (WSNs) in data collection in a wide range, long distance, and other complex environments, such as high networking cost, short network life cycle, and difficult data collection, a data collection strategy based on the unmanned aerial vehicle (UAV) cluster collaborative networking is proposed. First, the node structure and deployment application of WSNs are analyzed, and the architecture of UAV collaborative data collection is designed. Second, the related models of communication energy consumption between WSNs’ network and UAV cluster, UAV cluster task allocation, and path planning are established successively. Then, a simple but efficient task planning strategy considering UAV forward and return distances is proposed. Finally, for the problem of model solving, the solution strategy of the heuristic swarm intelligence algorithm is proposed. The simulation results verify that the strategy reduces the overall network energy consumption by 42.16% and the total range of UAV by 9.7% and realizes the relative minimization of WSNs energy consumption and UAV flight cost on the premise of ensuring the full coverage of WSNs’ nodes.

A classy energy efficient spider monkey optimization based clustering and data aggregation models for wireless sensor network

SUMMARYEstablishment of energy efficient and reliable data routing in wireless sensor network (WSN) is one of the most critical and challenging task in the recent days. Also, the overall performance and lifetime of WSN is highly depends on the energy level of sensor nodes, hence it is most essential to save the energy of network. For this purpose, the different types of clustering and data aggregation mechanisms are developed in the conventional works, which are focusing on improving both the energy conservation and lifetime of network. Yet, it facing the challenges of increased computational complexity, inefficient routing of data, high controlling overhead, and reduced reliability. Thus, the proposed work objects to develop a novel energy efficient mechanism by integrating the functionalities of advanced clustering, path selection, and data aggregation methodologies. Here, the spider monkey optimization based energy efficient routing protocol is developed for optimally selecting the cluster head (CH) based on certain parameters of energy, distance, and weight value. In this framework, the data transmission is performed between the source to destination nodes through the relay nodes and CHs, which helps to minimize the energy consumption of network. Then, the Classy Bellman‐Ford algorithm is deployed for identifying the best paths having shortest distance with the sink nodes. Consequently, an anticipated data aggregation mechanism is utilized for ensuring the security and reliability of data transmission in WSN. For evaluation assessment, various performance metrics have been utilized to validate the results of proposed methodology, and also the obtained values are compared with some other recent state‐of‐the‐art models for proving the betterment of proposed mechanism.

Secure Data Collection in Clustered Wireless Sensor Networks using Fuzzy based scheme to detect Malicious Data Collector

Secure Data Collection in Clustered Wireless Sensor Networks using Fuzzy based scheme to detect Malicious Data Collector