Clustered Wireless Sensor Networks Research Articles

A Novel Method for Energy Efficient Clustering in Wireless Sensor Networks

A Novel Method for Energy Efficient Clustering in Wireless Sensor Networks

Double firefly based efficient clustering for large-scale wireless sensor networks

Double firefly based efficient clustering for large-scale wireless sensor networks

A Review on Energy and Latency Efficient Routing Protocol Design for Wireless Sensor Networks

Wireless sensor networks (WSNs) have been utilized by various industries, such as disaster management, the chemical and heavy industries, marine research, and space exploration. One of the primary challenges faced by wireless sensor networks is their limited network lifetime. Several techniques have been devised to extend the duration of network operation. This proposed method introduces a threshold-based algorithm to minimize the amount of data that needs to be transferred. In this approach, the selection of cluster heads and their sizes is not fixed. Instead, they are dynamically determined at the beginning of each iteration. The design of routing algorithms for wireless sensor networks faces three main challenges: lowering the rate at which the average node energy declines, minimizing the number of dead nodes, and eventually prolonging the network's lifetime. This paper provides a thorough examination of efficient routing protocols for wireless sensor networks with the goal of improving network longevity. Keywords: Efficient Routing, WSN Clustering, Network Latency, Network Lifetime.

Research on WSN clustering algorithm of firefly algorithm to optimize k-means

Aiming at the problem that uneven clustering of wireless sensor networks leads to large energy consumption differences between clusters and short network lifetime, this paper proposes a clustering routing algorithm of WSN based on the Firefly algorithm to optimize k-means (FAKMC). This paper mainly studies the clustering stage of the network, adopts the k-means algorithm for node partition, and uses the improved FA to optimize the sensitive problem of the initial clustering centre of k-means. Simulation results show that, compared to LEACH and k-means, the clustering result proposed in this paper is more uniform and can extend the network life cycle.

Energy-Recognition Clustering Technique Based on Reinforcement Learning In WSN

WSN (Wireless Sensor Network) technology has recently gained a lot of attention. This began with the deployment of small WSNs and moved to the deployment of big and IoT WSNs, all with an emphasis on energy conservation. Wireless sensor networks can benefit from network clustering to increase their energy efficiency. The practise of dividing nodes into clusters before picking multiple cluster heads is known as network clustering (CHs). Clustering in wireless sensor networks is known to save energy and extend the network's lifetime (WSNs). Energy efficiency is a hot topic in existing wireless sensor networks, although it's not generally discussed. In this research, we offer a reinforcement learning (RL) based energy-aware clustering approach, whereby peripheral cluster nodes monitor environmental factors like energy use and choose an optimal cluster leader (CH). Connect the CH (BS) to the base station. In the simulation (PDR), performance factors such as network lifetime, energy tax, network stability period, and packet delivery rate are all taken into account. The simulation results show that the proposed QL-ReLeC performs around 11% better than the reference protocol in terms of PDR and 11% better in terms of energy tax.

Kalman filtering and sine arithmetic optimization algorithm (KSAOA) for wireless sensor network clustering

Hierarchical routing is a crucial approach for enhancing the energy efficiency of Wireless Sensor Networks (WSN). The selection of sensor clusters poses a Non-deterministic Polynomial (NP) problem, typically addressed using meta-heuristic algorithms. However, existing research has not adequately considered the observation error of node energy and the limited convergence performance of their meta-heuristic algorithms. To address these challenges, we proposes a novel approach called KSAOA (Kalman filtering and Sine Arithmetic Optimization Algorithm) for WSN clustering, aimed at prolonging the service lifetime and enhancing the network balance of WSN. The proposed model combines Kalman filtering for estimating node energy and the Sine Arithmetic Optimization Algorithm (SAOA) for clustering nodes. SAOA builds upon the Arithmetic Optimization algorithm (AOA) by introducing a sine factor to extend the exploration boundary and a levy flight strategy to improve local exploration. Additionally, greedy algorithms are employed in the data transfer phase to enhance operational efficiency. The experimental results demonstrate the effectiveness of the proposed method in tabular and graphical form. The results are analyzed through comparative experiments to show that the proposed strategy is effective in improving the balance of network lifetime and network energy consumption.

SECURE and energy-efficient routing protocol based on micro-segmentation and batch authentication

An effective technique in designing routing algorithms for Wireless Sensor Networks (WSNs) is named clustering nodes, which augments the network lifespan. More tasks are performed by the Cluster Heads (CHs) of the clustered WSNs, thereby consuming more energy. Hence, a secure energy-efficient Routing Protocol (RP) centered on micro-segmentation and batch authentication is proposed here. The proposed method begins by initializing the WSN nodes. After that, a random key is engendered for every node centered on their Node ID. The nodes undergo a Micro-segmentation process; in addition, they are securely grouped utilizing the Supremum Distance based K-Prototype (SD-KP) algorithm. Now, a fog layer with a number of fog nodes is initialized; also, every single micro-segmented group is assigned with an optimal fog node. Then, by using the Quasi Deterministic Sequence-Black Widow Optimization (QDS-BWO) algorithm, the optimal fog nodes are selected. By using the Root Squared- Diffie Hellman (R2-DH) technique, a key agreement is created for each group centered on their Node IDs. By using the Bitwise Cyclic Shift –BLAKE 512 (BCS-BLAKE-512) algorithm, the created keys are converted into hashcode. Now, for the purpose of performing batch authentication, the hashcode of each group is sent to the trusted authority. Subsequent to verification, data sensing occurs. Lastly, by using the Elliptic Curve Cryptography (ECC) algorithm, the data is securely encrypted and it is transmitted via the routes selected utilizing the Learned Gradient Weight Initialization-based Geography and Energy Aware Routing (LGWI-GEAR) algorithm. The proposed framework's efficacy is proved by the experimental outcomes.

Efficient energy consumption and fault tolerant method for clustering and reliable routing in wireless sensor network

Efficient energy consumption and fault tolerant method for clustering and reliable routing in wireless sensor network

An energy efficient fusing data gathering protocol in wireless sensor networks

The Internet of Things (IoT) is rapidly developing as a promising technology for today’s digital world. Wireless sensor networks (WSN) are a crucial component of IoT, and one of the major problems faced is energy constraints. The clustering protocol is an effective energy saving solution. In this paper, we propose an energy efficient fusing data gathering protocol via distributed second level clustering and deep learning-based data fusion (EEFDG) for WSN. EEFDG divides the WSN into several second-level clusters by a novel distributed cluster algorithm. The transmission distance and the number of neighbors are jointly considered to select an energy-efficient parent node in each second-level cluster. A new cluster head weight function is proposed for intra-cluster parent node rotation. To minimize redundant data transmission, a multivariate convolutional neural network and long short-term memory network (MVCLNet) are designed for data fusion. MVCLNet is deployed hierarchically into the second-level clustered structure to extract features of the multi-sensor data. The EEFDG is an effective solution to the energy limitation problem of WSN. The proposed second-level clustering and data fusion algorithm based on MVCLNet typically decreases communication distances and the amount of data, thereby minimizing communication overhead and energy consumption. Simulation results show that EEFDG significantly prolongs the network lifetime in terms of the first node dead, compared to existing protocols including MR-LEACH, BPDA, CNNDA, FDEAM, DCNN, and LPLL-LEACH, achieving improvements of 181.3%, 42.2%, 48.2%, 19.6%, 62.0%, and 30.2%, respectively. The EEFDG enables highly accurate data fusion and has superior performance in terms of remaining energy, network lifetime, and data collection.

Improved energy efficiency using adaptive ant colony distributed intelligent based clustering in wireless sensor networks

Optimization algorithms have come a long way in the last several decades, with the goal of reducing energy consumption and minimizing interference with primary users during data transmission over shorter distances. The adaptive ant colony distributed intelligent based clustering algorithm (AACDIC) is a key component of the cognitive radio (CR) system because of its superior performance in spectrum sensing among a group of multi-users in terms of reduced sensing errors, power conservation, and faster convergence times. This study presents the AACDIC method, which improves energy efficiency by determining the ideal cluster count using connectedness and distributed cluster-based sensing. In this study, we take into account the reality of a system with an unpredictable number of both primary users and secondary users. As a result, the proposed AACDIC method outperforms pre-existing optimization algorithms by increasing the rate at which solutions converge via the utilisation of multi-user clustered communication. Experiments show that compared to other algorithms, the AACDIC method significantly reduces node power usage by 9.646 percent. The average power of Secondary Users nodes is reduced by 24.23 percent compared to earlier versions. The AACDIC algorithm is particularly strong at reducing the Signal-to-Noise Ratio to levels as low as 2 dB, which significantly increases the likelihood of detection. When comparing AACDIC to other primary detection optimization strategies, it is clear that it has the lowest false positive rate. The proposed AACDIC algorithm optimizes network capacity performance, as shown by the results of simulations, due to its ability to solve multimodal optimization challenges. Our analysis reveals that variations in SNR significantly affect the probability of successful detection, shedding light on the intricate interplay between signal strength, noise levels, and the overall reliability of sensor data. This insight contributes to a more comprehensive understanding of the proposed scheme's performance in realistic deployment scenarios, where environmental conditions may vary dynamically. The experimental results demonstrate the effectiveness of the proposed algorithm in mitigating the identified drawback and highlight the importance of SNR considerations in optimizing detection reliability in energy-constrained WSNs.

Survey on Clustering Techniques for Wireless Sensor Networks and Cluster Head Selection Model

Low-powered wirelessly connected devices create a system that is self-contained and called Wireless sensor network (WSN), this system has a number of practical applications in the modern world. Nodes in WSN are spread out spatially and have wireless communication with other nodes in the network, these nodes are primarily dedicated to sensing the vicinity and recording the physical properties of the area, including collect observations pertaining to humidity, noise, load, temperature Sensors and other factors at a central station. From there, the end user analyzes the information. After distribution, a cluster is formed between nodes, called a cluster. One of the nodes is selected as the cluster's head, which is used to migrate the data across the network. Clustering is essential in addressing many issues in WSN, particularly in regards to their lifetime, energy savings, and interoperability. Many approaches exist for clustering WSN, the difference between them is in the choice of CH, the number of clusters, the structure of clusters, the policy for data aggregation, the efficiency of communication between clusters, and the choice of communication between internal and external clusters. This article provides a comprehensive comparison of various methods of clustering and their advantages and disadvantages in WSNs. It also describes the principles of CH selection and how it works within each category. This helps the end user to choose the most suitable one protocol for their needs, and also will assist researchers in addressing the problems revealed in the process of clustering WSN.

Energy efficient clustering for dense wireless sensor network by applying Graph Neural Networks with coverage metrics

Wireless sensor networks (WSNs) have become increasingly important in recent years due to their ability to monitor and collect data in various environments. However, WSNs often face limitations in terms of their energy resources, making energy efficiency a critical concern when designing WSNs. Clustering and multihop routing are effective methods for maximizing the lifetime of WSNs. The Graph Neural Network (GNN) is an emerging architecture in neural networks that has recently gained attention for solving problems in various domains, including wireless networks. In this paper, we propose a method based on GNN to create static clusters of equal size. This approach aims to balance energy consumption among the nodes, which is an important sub-goal in designing an energy-efficient WSN clustering protocol. Additionally, we introduce a distributed cluster head selection scheme that operates independently within each cluster. Another crucial aspect of energy efficiency in WSNs is multi-hop routing, which helps avoid long-distance communication that consumes significant amounts of energy. Our proposed centralized routing protocol establishes exclusive routes to the base station for each cluster. This ensures even energy consumption for relay nodes and prevents the hotspot problem. To evaluate our proposed protocol, we conducted simulations comparing it with several state-of-the-art counterparts. The empirical results demonstrate that our approach outperforms other protocols in terms of lifetime and coverage metrics.

Clustering protocols for energy efficiency analysis in WSNS and the IOT

Throughout the development of energy efficient routing protocol for wireless sensor network clustering technique has been widely adopted approach. The Selection of cluster heads is also very important for the energy efficiency of the network. In the past, researchers have proposed multiple routing protocols; however, problems are still alive and need to be resolved because of the diversity of WSN applications. This article presents an energy-efficient routing protocol using the advanced approaches of Artificial Intelligence, the most promising field of computer science currently providing the best solutions. The proposed model uses the Deep Q-network to select the cluster head. Moreover, collected data at the cluster head is generalized as low, moderate, and high values using the fuzzy logic technique. After that, the Predictive coding theory algorithm is used for the data compression, and the lossy compression technique is applied to the data. Its compressed form also gives complete information of the data in small size and is delivered to the base station. Again, the transmitted data is reconstructed into its actual format. In the end, to justify the performance of the newly designed routing protocol, simulations are performed using the Matlab tool, and its results are evaluated in quality of service matrices and compared with well-known routing protocols.

Effective data transmission through energy-efficient clustering and Fuzzy-Based IDS routing approach in WSNs

Wireless sensor networks (WSN) gather information and sense information samples in a certain region and communicate these readings to a base station (BS). Energy efficiency is considered a major design issue in the WSNs, and can be addressed using clustering and routing techniques. Information is sent from the source to the BS via routing procedures. However, these routing protocols must ensure that packets are delivered securely, guar- anteeing that neither adversaries nor unauthentic individuals have access to the sent information. Secure data transfer is intended to protect the data from illegal access, damage, or disruption. Thus, in the proposed model, secure data transmission is developed in an energy-effective manner. A low-energy adaptive clustering hierarchy (LEACH) is developed to efficiently transfer the data. For the intrusion detection systems (IDS), Fuzzy logic and artificial neural networks (ANNs) are proposed. Initially, the nodes were randomly placed in the network and initialized to gather information. To ensure fair energy dissipation between the nodes, LEACH randomly chooses cluster heads (CHs) and allocates this role to the various nodes based on a round-robin management mechanism. The intrusion-detection procedure was then utilized to determine whether intruders were present in the network. Within the WSN, a Fuzzy interference rule was utilized to distinguish the malicious nodes from legal nodes. Subsequently, an ANN was employed to distinguish the harmful nodes from suspicious nodes. The effectiveness of the proposed approach was validated using metrics that attained 97% accuracy, 97% specificity, and 97% sensitivity of 95%. Thus, it was proved that the LEACH and Fuzzy-based IDS approaches are the best choices for securing data transmission in an energy-efficient manner.

MKFF: mid-point K-means based clustering in wireless sensor network for forest fire prediction

MKFF: mid-point K-means based clustering in wireless sensor network for forest fire prediction

Performance enhancement of efficient clustering and routing protocol for wireless sensor networks using improved elephant herd optimization algorithm

Performance enhancement of efficient clustering and routing protocol for wireless sensor networks using improved elephant herd optimization algorithm

Data Traffic Based Shape Independent Adaptive Unequal Clustering for Heterogeneous Wireless Sensor Networks

Data Traffic Based Shape Independent Adaptive Unequal Clustering for Heterogeneous Wireless Sensor Networks

Improved beluga whale optimization algorithm based cluster routing in wireless sensor networks.

Cluster routing is a critical routing approach in wireless sensor networks (WSNs). However, the uneven distribution of selected cluster head nodes and impractical data transmission paths can result in uneven depletion of network energy. For this purpose, we introduce a new routing strategy for clustered wireless sensor networks that utilizes an improved beluga whale optimization algorithm, called tCBWO-DPR. In the selection process of cluster heads, we introduce a new excitation function to evaluate and select more suitable candidate cluster heads by establishing the correlation between the energy of node and the positional relationship of nodes. In addition, the beluga whale optimization (BWO) algorithm has been improved by incorporating the cosine factor and t-distribution to enhance its local and global search capabilities, as well as to improve its convergence speed and ability. For the data transmission path, we use Prim's algorithm to construct a spanning tree and introduce DPR for determining the optimal route between cluster heads based on the correlation distances of cluster heads. This effectively shortens the data transmission path and enhances network stability. Simulation results show that the improved beluga whale optimization based algorithm can effectively improve the survival cycle and reduce the average energy consumption of the network.

DEVELOPMENT OF WIRELESS SENSOR NETWORKS EFFICIENT CLUSTERIZATION METHOD

Wireless sensor networks are a critical element of the Internet of Things and are widely used in various fields. Effective clustering is key to energy efficiency and network stability in wireless sensor networks. Optimize the density of clusters, their number and size to increase the stability and service life of the network – It is one of the tasks of clustering wireless sensor networks. Another problem – effectively organize the network topology in order to balance the load and extend the network lifetime. Clustering has proven to be an effective approach for organizing a network into a cohesive hierarchy. However, there are several key issues that affect the practical application of clustering methods in sensor network applications. These issues include the rationale for developing different clustering approaches, classification of proposed approaches based on their goals and design principles, and problems associated with clustering. K-means algorithm is one of the unsupervised machine learning methods for clustering tasks and is widely used in wireless sensor networks. This article proposes a modified K-means clustering algorithm that eliminates the shortcomings of the basic K-means algorithm. The modified algorithm increases network lifetime, reduces power consumption and improves network stability.

Enhanced ACO in clustering algorithm for QoS in WSN enabled IoT

Various sensor nodes whose deployment is done in a random way are included in Wireless sensor networks (WSN) within a region for gathering the information out of the respective atmospheres as well as forwards it towards the Base Station (BS). Constrained sources of energy are allotted to the WSN nodes. The consumption of energy of nodes must be reduced for obtaining the enhanced lifetime of the network. Clustering is referred as a technique that reduced the consumption of energy in WSNs. By transmitting the gathered data to the sink through the Cluster Head (CH) nodes located within the clustered networks, the energy of the node can be saved. In WSNs, fault tolerance is a significant issue to consider. The entire data communication system can be rendered inoperable by the failure of just one cluster head. Within the scope of this research, a cluster-based routing method with fault tolerance is provided. The purpose of this study is to present an innovative technique for increasing tolerance of failure and data accumulation in clustered WSN by making use of backup CHs and improving the relay node selection mechanism by modifying ACO. The selection of the backup cluster head is what allows for fault tolerance in this case (BKCH). The method can be broken down into two distinct stages. In the first step, the network is categorized into clusters; in the second step, CHs and BCHs are chosen from the pool of candidates. Communication within the cluster takes place between the member nodes and the CH nodes. Aggregator nodes (AG) are utilised for the purpose of inter-cluster communication, and the modified ACO is utilised to determine which node serves as the most effective relay between CH and AG. When compared with other energy-conserving protocols, this technique uses less energy while increasing fault resilience and PDR.