Clustered Wireless Sensor Networks Research Articles

Adaptive Clustering and Trust Aware Routing (ACTAR) for Wireless Sensor Networks in IoT Environments

ABSTRACTThe Internet of Things (IoT) has emerged as a revolutionary technology, connecting a vast number of people to the internet through various devices such as smartphones, laptops, sensors, and more. An essential component of IoT, the wireless sensor network (WSN), enables automation in processes like sensing, node monitoring, and data transmission. However, the full potential of IoT is hindered by cyber threats and unreliable communication. Although several security algorithms exist, they are not suitable for energy‐constrained sensor nodes. To address this issue, this paper proposes an energy‐efficient security mechanism called adaptive clustering and trust aware routing (ACTAR) for IoT‐WSNs. ACTAR operates in three phases: adaptive and hybrid clustering (AHC), multiobjective function‐based cluster head selection (MOCH), and trust aware routing (TAR). First, AHC utilizes a nonuniform clustering mechanism to categorize the network into nearby and distant clusters. Next, the selection of cluster heads is based on four metrics: coverage, communication cost, residual energy, and node proximity. Finally, TAR calculates the trust degree of sensor nodes by evaluating their direct and indirect behavior in terms of communication interactions and energy consumption. The node with the highest trust degree is selected as the next‐hop forwarding node, followed by the route with the highest trust degree. Extensive simulations of ACTAR demonstrate its performance in terms of malicious detection rate, false‐positive rate, residual energy, and packet delivery ratio. Comparative analysis shows that ACTAR outperforms existing methods, proving its superiority.

Distributed Clustering in Wireless Sensor Network with Kernel Based Weighted Fuzzy C-Means Algorithm

Distributed Clustering in Wireless Sensor Network with Kernel Based Weighted Fuzzy C-Means Algorithm

Energy efficient clustering in IoT-based wireless sensor networks using binary whale optimization algorithm and fuzzy inference system

Energy efficient clustering in IoT-based wireless sensor networks using binary whale optimization algorithm and fuzzy inference system

An hybrid machine learning and improved social spider optimization based clustering and routing protocol for wireless sensor network

An hybrid machine learning and improved social spider optimization based clustering and routing protocol for wireless sensor network

An improved energy saving clustering method for IWSN based on Gaussian mutation adaptive artificial fish swarm algorithm

The current industrial wireless sensor network (IWSN) cluster routing methods suffer from energy inefficiency. Designing efficient cluster-based routing protocols is crucial for improving network performance and energy efficiency. Therefore, this paper first designs a new clustering model to achieve efficient cluster head (CH) selection and data transmission performance by comprehensively considering multiple key factors such as CH energy, base station (BS) distance, packet loss rate, and data delay. Based on this clustering model, a novel cluster routing protocol based on Gaussian mutation adaptive artificial fish swarm algorithm (GAAFSA) is proposed. At the same time, a new Gaussian mutation strategy and an adaptive strategy were introduced to effectively promote the protocol to avoid local optima and prevent premature convergence. The GAAFSA based cluster routing protocol was experimentally compared with five popular schemes, namely CMSTR, D2CRP, EEHCHR, ESCVAD and BAFSA. The results showed that the proposed protocol outperformed the other four schemes in terms of network energy consumption, system lifetime, data transmission reliability, and latency. Specifically, GAAFSA has improved network lifespan by at least 15.68%, BS received packets by at least 7.46%, and reduced packet loss rate by at least 15.28%. Therefore, GAAFSA effectively optimizes network performance and extends network lifespan, greatly reducing energy loss within the network and significantly improving network quality of service (QoS).

High-Efficiency Clustering Routing Protocol in AUV-Assisted Underwater Sensor Networks.

Currently, underwater sensor networks are extensively applied for environmental monitoring, disaster prediction, etc. Nevertheless, owing to the complicacy of the underwater environment, the limited energy of underwater sensor nodes, and the high latency of hydroacoustic channels, the energy-efficient operation of underwater sensor networks has become an important challenge. In this paper, a high-efficiency clustering routing protocol in AUV-assisted underwater sensor networks (HECRA) is proposed to address the energy limitations and low data transmission reliability in underwater sensor networks. The protocol optimizes the cluster head selection strategy of the traditional low-energy adaptive clustering hierarchy (LEACH) protocol by introducing the residual energy and node degree in the cluster head selection phase and performs some optimizations in the cluster formation and data transmission phases, including selecting clusters for joining by ordinary nodes based on the residual energy of the cluster head nodes and weight computation based on the depth and residual energy of the cluster head nodes to select the optimal message forwarding nodes. In addition, this paper introduces an autonomous underwater vehicle (AUV) as a dynamic relay node to improve network transmission efficiency. According to the simulation results, compared with the existing LEACH, the energy efficient routing protocol based on layers and unequal clusters in underwater wireless sensor networks (EERBLC) and energy-efficient clustering multi-hop routing protocol in a UWSN (EECMR), the HECRA significantly improves network lifetime, the residual node energy, and the number of successfully transmitted packets, which can effectively prolong network lifetime and ensure efficient data transmission.

Energy aware multiobjective levy flight artificial rabbits optimization based clustering and routing for mobile wireless sensor networks

Energy aware multiobjective levy flight artificial rabbits optimization based clustering and routing for mobile wireless sensor networks

Energy Efficient Secured PSO Optimized Clustering and Data Aggregation Routing Protocol for Wireless Sensor Networks

In Wireless Sensor Networks (WSNs), sensor nodes are placed to sense and collect data. Due to the energy constraint nature of WSN, optimising the energy during the data dissemination is a major concern. To solve this problem, data aggregation may be used to bring down the redundant transmission of packets in WSN. In most of the previously available techniques, security is also a major concern during data aggregation and routing process with optimized energy. Many data aggregation-based routing systems are subject to security attacks during the data transfer from sensors to source to Clustered Heads (CHs) and then to sink with data aggregation process. Moreover, the existing data aggregation-based routing protocols suffer from data redundancy with less accuracy in aggregated data. For handling such issues order to overcome these issues, an Energy Efficient Secured Clustered Particle Swarm Optimization (PSO) oriented Data Aggregation Routing Protocol (EESCPSO-DARP) that can provide efficient authentication during data aggregation-based routing is introduced in this paper. Moreover, the proposed protocol enhances the rate of the data transmission by efficient prevention of false data injection and other attacks through node authentication and data encryption. This proposed protocol minimizes the energy usage by minimizing the retransmissions by eliminating the possible redundant transmissions of data during data aggregation-based routing. Moreover, the introduced protocol minimizes both communication and computational overhead through optimal clustering and routing with PSO and provides and efficient routing system. This proposed EESCPSO-DARP protocol has been developed by using the NS3 simulator. The results of this protocol showed improved security, higher packet delivery ratio and enhanced network throughput with reduced energy and delay.

Enhanced Energy Efficient Clustering and Routing Algorithm in Wireless Sensor Network

Enhanced Energy Efficient Clustering and Routing Algorithm in Wireless Sensor Network

Multimedia Cognitive Wireless Sensor Network Cluster Routing based on Intelligent Robot Edge Computing and Collection

Multimedia Cognitive Wireless Sensor Network Cluster Routing based on Intelligent Robot Edge Computing and Collection

Threshold-driven K-means sector clustering algorithm for wireless sensor networks

The clustering algorithm is an effective method for developing energy efficiency routing protocol for wireless sensor networks (WSNs). In clustered WSNs, cluster heads must handle high traffic, thus consuming more energy. Therefore, forming balanced clusters and selecting optimal cluster heads are significant challenges. The paper proposes a sector clustering algorithm based on K-means called KMSC. KMSC improves efficiency and balances the cluster size by employing symmetric dividing sectors in conjunction with K-means. For the selection of cluster heads (CHs), KMSC uses the residual energy and distance to calculate the weight of the node, then selects the node with the highest weight as CH. A hybrid single-hop and multi-hop communication is utilized to reduce long-distance transmissions. Furthermore, the impact of the number of sectors, the threshold for clustering, and the network size on the performance of KMSC has been explored. The simulation results show that KMSC outperforms EECPK-means, K-means, TSC, LSC, and SEECP in terms of FND, HND, and LND.

Retraction Note: Enhancement of network lifetime using fuzzy clustering and multidirectional routing for wireless sensor networks

Retraction Note: Enhancement of network lifetime using fuzzy clustering and multidirectional routing for wireless sensor networks

Optimizing Rule Weights to Improve FRBS Clustering in Wireless Sensor Networks.

Wireless sensor networks (WSNs) are usually composed of tens or hundreds of nodes powered by batteries that need efficient resource management to achieve the WSN's goals. One of the techniques used to manage WSN resources is clustering, where nodes are grouped into clusters around a cluster head (CH), which must be chosen carefully. In this article, a new centralized clustering algorithm is presented based on a Type-1 fuzzy logic controller that infers the probability of each node becoming a CH. The main novelty presented is that the fuzzy logic controller employs three different knowledge bases (KBs) during the lifetime of the WSN. The first KB is used from the beginning to the instant when the first node depletes its battery, the second KB is then applied from that moment to the instant when half of the nodes are dead, and the last KB is loaded from that point until the last node runs out of power. These three KBs are obtained from the original KB designed by the authors after an optimization process. It is based on a particle swarm optimization algorithm that maximizes the lifetime of the WSN in the three periods by adjusting each rule in the KBs through the assignment of a weight value ranging from 0 to 1. This optimization process is used to obtain better results in complex systems where the number of variables or rules could make them unaffordable. The results of the presented optimized approach significantly improved upon those from other authors with similar methods. Finally, the paper presents an analysis of why some rule weights change more than others, in order to design more suitable controllers in the future.

Artificial neural network-based clustering in Wireless sensor Networks to balance energy consumption

Artificial neural network-based clustering in Wireless sensor Networks to balance energy consumption

Optimizing Clustering in Wireless Sensor Networks: A Synergistic Approach Using Reinforcement Learning (RL) and Particle Swarm Optimization (PSO)

Optimizing Clustering in Wireless Sensor Networks: A Synergistic Approach Using Reinforcement Learning (RL) and Particle Swarm Optimization (PSO)

Congestion aware clustered WSN based on an improved ant colony algorithm

Conventional works carried out in Wireless Sensor Networks (WSN) mostly focussed on energy oriented services and very less significant measures given to delay oriented and congestion aware services. Hence the proposed mechanism specially focuses on network structural design by placing rendezvous location for each cluster as well as route segmentation for controlling the congestion occurrence and unwanted delay. Here Congestion Aware Clustering with Improved Ant Colony Algorithm (CAC_IACA) is proposed. This mechanism involves two steps (i) identifying the best route by following the Ant Colony Optimization (ACO) algorithm and (ii) data segmentation using rendezvous mobile nodes. The Rendezvous nodes are present in each cluster to reduce the congestion rate on receiver side during data transmission. This proposed methodology mainly concentrates on reducing coverage cost for 3D environmental monitoring. Simulation results are analysed and the efficiency of the proposed scheme proves 26.54 % better than the conventional method.

TEDDEEC: Threshold enhanced developed distributed energy-efficient clustering for heterogeneous wireless sensor networks

Wireless Sensor Networks (WSNs) comprise of vast number of arbitrarily conveyed energy required sensor nodes. Sensor nodes have capacity to sense and send detected information to Base Station (BS). Detecting and in addition transmitting information towards BS require more energy. In WSNs, sparing energy and developing network lifetime are awesome difficulties. Clustering is a key method used to enhance energy utilization in WSNs. Among two types of network; Homogeneous and Heterogeneous, the later has demonstrated to be significantly more essential in upgrading the network lifetime and making the network a great deal more energy adjusted with fitting probabilistic cluster head choice. In this paper, a novel clustering based routing protocol called Threshold Enhanced Developed Distributed Energy Efficient Clustering scheme (TEDDEEC) for heterogeneous WSNs is proposed. This method depends on more efficient selection of Cluster Heads (CH) for the rounds. Simulation is carried out for different proportion of normal and advanced nodes. Simulation results demonstrate that this proposed scheme accomplishes longer lifetime, stability period and more effective messages to BS than Enhanced Developed Distributed Energy Efficient Clustering scheme (EDDEEC) in heterogeneous situations.

Energy-aware proof-of-authority: Blockchain consensus for clustered wireless sensor network

This study addresses integrating blockchain technology into lightweight devices, specifically clustered Wireless Sensor Networks (WSNs). Integrating blockchain into the WSNs solves the problems of heterogeneity, data integrity, and data confidentiality. However, no blockchain integration considers network lifetime in WSNs. This research focuses on developing a permissioned blockchain system that incorporates a consensus mechanism known as Proof-of-Authority (PoA) within clustered WSNs with two main features. The first feature is to enhance the network lifetime by introducing a rotational selection of block proposers using an Energy-Aware PoA (EA-PoA) weighting mechanism. Known as the Multi-Level Blockchain Model (MLBM), the subsequent feature is to create a hierarchical network model within a blockchain network. The MLBM network comprises both local and master blockchains. Each cluster inside a WSN possesses its local blockchain network. In the MLBM, the local blockchain creates a block on the main blockchain by proposing the headers of every 10 blocks to improve data integrity. Each local blockchain has its leader, which can increase block production. The results show that the proposed solution can overcome traditional PoA performance and is suitable for clustered WSNs. In terms of lifetime, the EA-PoA selection method can extend the network lifetime by up to 10%. In addition, the MLBM can increase block production by up to twice each additional cluster compared to a single blockchain network used in traditional PoA.

A Novel Method for Energy Efficient Clustering in Wireless Sensor Networks

Wireless Sensor Networks (WSNs) play a crucial role in various applications, including environmental monitoring, industrial automation, and healthcare. However, optimizing WSNs for efficient resource utilization, energy conservation, and reliable data transmission remains a challenging task due to the dynamic nature of the network environment and resource-constrained sensor nodes. In this study, we propose a Hybrid Firefly Genetic Algorithm (HFGA) for optimizing WSN performance. The HFGA combines the strengths of the firefly algorithm's global search capabilities and the genetic algorithm's local search and optimization efficiency. By integrating these two evolutionary algorithms, the HFGA aims to achieve superior performance in terms of energy efficiency, network coverage, and convergence speed. We evaluate the effectiveness of the proposed HFGA through extensive simulation experiments in various WSN scenarios. The results demonstrate that the HFGA outperforms traditional optimization approaches and baseline algorithms in optimizing WSN performance metrics. Furthermore, we discuss the practical implications and future research directions for deploying the HFGA in real-world WSN applications. Overall, this study contributes to advancing WSN optimization techniques and enhancing the reliability and efficiency of WSN deployments. Keywords: Clustering, Genetic algorithm, Firefly algorithm, FAG algorithm.

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

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