Cluster Head Selection Algorithm Research Articles

Cluster head selection method of multiple UAVs under COVID-19 situation

As COVID-19 continues to spread, people are unable to move freely when their residence region is temporarily lockdown, supplies cannot normally enter into such zones, leading to the shortage of supplies in these areas. Thus to ensure the delivery of supplies while reducing contact, the unmanned aerial vehicle (UAV) deliveries have become a common way. In order to efficiently use UAV resources and reduce energy loss in data transmission while performing the tasks, clustering is often used for achieving the above objectives, where the selected cluster heads centrally plan tasks so that reduce the communication times. However, problems such as unreasonable clustering, high energy consumption of cluster heads, and high mortality of cluster heads, directly lead the low cooperation efficiency and short life cycle of UAVs. Considering the nodes often died earlier through the k-means algorithm and ant colony algorithm, and highly dependent on the base station, these factors affect the working cycle and coordination efficiency of the UAVs. Facing the issues above, the cluster head selection algorithm of UAV based on game (CHSA) is proposed, where the mixed game model is adopted to select cluster heads for each region after regional division, and selecting the representative node to perform the cluster head selection algorithm, which help to reduce the energy consumption of each round of communication between nodes. Moreover, the key properties of the CHSA algorithm are proved, and the comparison experiment are conducted to prove the CHSA algorithm can effectively reduce energy consumption and prolong the network life cycle.

Improve Performances of Wireless Sensor Networks for Data Transfer Based on Fuzzy Clustering and Huffman Compression

In today’s world, the main challenge is to save use energy optimally. The IoT devices generate a large amount of data for wide applications. Considering the application perspective in the IoT market, in one instance of the IoT technology, that is a wireless sensor network, factors like energy, storage capacity, computation power, and limitations of communication bandwidth resources are the reason for using data fusion. Data fusion and aggregation in wireless sensor networks (WSNs) such that minimum energy is consumed are an essential issue. In most clustering models, data aggregation is carried out by the cluster-head (CH). In the proposed algorithm, data aggregation in the cluster-head is carried out using the lossless cascode Huffman compression algorithm. Due to the correlation among data of nodes, the data sensed by each node is compared with the data of the cluster-head node; after removing redundancy, the coded data is transmitted to the main node. The CH node is selected by an algorithm based on fuzzy logic according to the residual energy of the node and the distance of the node from the sink node. Various fuzzy type-I systems of Mamdani and Takagi-Sugeno and type-II systems are used. In this paper, the CH selection algorithms are evaluated using three scenarios in terms of the number of live nodes, received packets and CHs, proper distribution rate, and other parameters of the LEACH protocol, network lifetime, and network energy. In the following, to demonstrate the performance of this new algorithm, simulations are performed in MATLAB based on the proposed method. The results show that the proposed compression algorithm in environments with high data correlation improves the compression rate by 8% compared to the conventional Huffman compression, while in environments with low data correlation, these two algorithms perform almost the same. This compression helps reduce the energy consumption of the network.

Hybrid Bacteria Foraging Algorithm With PSO and DE Algorithm for Optimal Cluster Head Selection in Wireless Sensor Networks

Network lifetime and energy constraint are the main issues for the application of wireless sensor networks. Sensor nodes spend more energy in the communication process and affect the network lifetime. Clustering is the technique for choosing the optimal cluster head from the clusters. LEACH-C is the clustering protocol in WSN. BFA is applied in the LEACH-C protocol to form the optimal clusters. This optimization obtains more steps in the tumbling process and reaches the global optimum solution very slowly. This method directly affects the network lifetime. The above limitations are overcome by introducing the hybrid approach of bacteria foraging algorithm by integrating the PSO, and DE is applied in LEACH-C algorithm for finding the optimal cluster head. The best foraging solution is utilized in the chemotactic behavior of the bacterium by using PSO and DE algorithms. The proposed methodology increases by 66% and 77% of the alive nodes when compared to FA and BFPSO.

Spectral Efficiency Optimization of Uplink Millimeter Wave MIMO-NOMA Systems

In this paper, we considered uplink communication, focusing on the improvement of spectral efficiency (SE) for millimeter wave (mmWave) multiple-input multiple-output non-orthogonal multiple access (MIMO-NOMA) systems. Firstly, we proposed an adaptive cluster head selection algorithm. Then, a channel-aligned analog beamforming scheme was designed based on the selected cluster heads. After that, the user grouping algorithm was designed based on the user-equivalent channel correlation. Subsequently, the power allocation problem was transformed from a nonconvex problem to a convex one using the quadratic transformation (QT) method considering all relevant constraints. Finally, the optimal user power allocation and digital beamforming design was obtained by iteratively optimizing the power and digital beamforming. Simulation results show that our proposed scheme can achieve a higher SE than existing methods.

LCIPA: Lightweight clustering protocol for industry 4.0 enabled precision agriculture

Smart agriculture using the Internet of Things (IoT) is an Industry 4.0 technology used in rural and urban environments. Industry 4.0 is a new class of the Industrial Revolution that focuses heavily on automation, interconnection, real-time data, and artificial intelligence (AI). With the growing demand for agricultural automation to increase farm output while meeting minimal criteria, smart farming has emerged as one of the fundamental applications of Industry 4.0. We propose an AI-based smart farming protocol since AI approaches are essential for the performance improvement of Industry 4.0 standards. We design Lightweight Clustering Protocol for Industry 4.0 Enabled Precision Agriculture (LCIPA) protocol via clustering and routing algorithms. Smart farming such as Industry 4.0, tackles long-distance communications, energy efficiency, computing efficiency, and assured QoS performance. In the clustering phase, we compute the direct and indirect sensor node parameters to produce the integrated fitness function value. The optimal selection of Cluster Head (CH) and reliable data forwarders is obtained using this novel fitness function. We establish the objective function for clustering and routing to reduce energy consumption and communication overhead while maximizing network performance using an integrated fitness function. The proposed fitness function is used to develop the Bacterial Foraging Algorithm (FBA) for optimum CH selection. A lightweight route discovery approach is based on periodic fitness ratings for inter-cluster and intra-cluster data transfer without specific processing. The simulation results reveal the efficiency of the LCIPA protocol compared to underlying methods.

Adaptive clustering routing protocol for underwater sensor networks

In underwater sensor networks, the limited energy of sensor nodes and the difficulty of replacing the power supply means that one faulty node will severely affect the lifetime of the entire network. The design of energy-saving and efficient routing protocols is the key to prolonging the network lifetime. This paper describes an adaptive clustering routing protocol for underwater sensor networks based on multi-agent reinforcement learning. The proposed protocol models the network as a multi-agent system, and allows the nodes to select the global optimal route collaboratively through reinforcement learning. To reduce the probability of hotspots generation, an adaptive cluster head selection algorithm that does not incur any additional communication overhead and does not require consensus from surrounding nodes is proposed, which enables nodes to autonomously decide whether they can act as cluster heads using routing and environment information. Additionally, a biased reward function is designed to feedback the effect of the adaptive cluster head selection algorithm on the routing performance and to encourage the nodes to select the cluster heads as relays. Simulation results show that the proposed adaptive clustering routing protocol achieves higher routing efficiency, lower energy consumption, and longer network lifetime than existing approaches.

A secure trust-based protocol for hierarchical routing in wireless sensor network

<p><span>Wireless sensor networks (WSNs) became the backbone of the internet of things (IoT). IoT applications are vital and demand specific quality of service (QoS) requirements. In addition, security has become a primary concern to provide secure communication between wireless nodes, with additional challenges related to the node’s computational resources. Particular, the design of secure and resource efficient routing protocol is a critical issue in the current deployment of WSNs. Therefore, this paper proposes a novel secure-trust aware routing protocol (ST2A) that provides secure and reliable routing. The proposed protocol establishes communication routes based on calculated trust value in joint with a novel cluster head selection algorithm in the hierarchical routing process. The proposed trust-aware routing algorithm improves the routing security in WSN and optimizes many performance metrics related to WSNs unique characteristics. The results of simulation validate the feasibility of the proposed algorithm for enhancing the network lifetime up to 18% and data delivery by 17% as compared with some state-of-the-art routing algorithms.</span></p>

Characteristics analysis and suppression strategy of energy hole in wireless sensor networks

The energy hole is one of the problems that wireless sensor networks cannot completely avoid. The existing energy hole suppression strategies lack the analysis of the development trend of energy holes and effective measures. In order to solve these problems, this paper deeply studies the formation process of energy hole, constructs an energy topography model that can reflect the different development states of energy hole. It proposes a series of basic definitions related to energy hole, including node energy surface, energy trough, quasi-energy hole, energy hole, independent energy hole, fusion energy hole, hole edge domain, and the dispersion of dead node. Then, an Energy Hole Suppression Routing Algorithm (EHSRA) for wireless sensor networks is proposed, which includes the cluster head election algorithm for suppressing energy hole, the transmission ordering strategy of the nodes in the cluster, and the data transmission strategy to realize the energy consumption balance of the network and prolong the network lifetime. Simulation experiments show that, compared with EEEHR, EA-BECHA, and DECEM algorithms, the network lifetime of the proposed algorithm is extended by 127.66%, 59.7%, and 48.61% respectively, the total energy consumption of the network is reduced by 48.52%, 21.55%, and 16.9%, respectively. At the same time, the occurrence time of dead node is delayed, and the dispersion of dead node is also higher than other algorithms. It effectively suppresses the occurrence time and expansion speed of energy hole to a certain extent.

Cluster Head Selection Algorithms for Enhanced Energy Efficiency in Wireless Sensor Networks: A Systematic Literature Review

The extension of the sensor node's life span is an essential requirement in a Wireless Sensor Network. Cluster head selection algorithms undertake the task of cluster head election and rotation among nodes, and this has significant effects on the network's energy consumption. The objective of this paper is to analyze existing cluster head selection algorithms and the parameters they implement to enhance energy efficiency. To achieve this objective, systematic literature review methodology was used. Relevant papers were extracted from major academic databases Elsevier, Springer, Wiley, IEEE, ACM Digital Library, Citeseer Library, and preprints posted on arXiv. The results show that there are many existing Cluster Head Selection Algorithms and they are categorized as deterministic, adaptive and hybrid. These algorithms use different parameters to elect Cluster Heads. In future the researchers should derive more parameters that can be used to elect cluster heads to improve on energy consumption.

A Reliable Merging Link Scheme Using Weighted Markov Chain Model in Vehicular Ad Hoc Networks.

The vehicular ad hoc network (VANET) is a potential technology for intelligent transportation systems (ITS) that aims to improve safety by allowing vehicles to communicate quickly and reliably. The rates of merging collision and hidden terminal problems, as well as the problems of picking the best match cluster head (CH) in a merged cluster, may emerge when two or more clusters are merged in the design of a clustering and cluster management scheme. In this paper, we propose an enhanced cluster-based multi-access channel protocol (ECMA) for high-throughput and effective access channel transmissions while minimizing access delay and preventing collisions during cluster merging. We devised an aperiodic and acceptable merge cluster head selection (MCHS) algorithm for selecting the optimal merge cluster head (MCH) in centralized clusters where all nodes are one-hop nodes during the merging window. We also applied a weighted Markov chain mathematical model to improve accuracy while lowering ECMA channel data access transmission delay during the cluster merger window. We presented extensive simulation data to demonstrate the superiority of the suggested approach over existing state-of-the-arts. The implementation of a MCHS algorithm and a weight chain Markov model reveal that ECMA is distinct and more efficient by 64.20–69.49% in terms of average network throughput, end-to-end delay, and access transmission probability.

Manta Ray Foraging Optimization (MRFO)-Based Energy-Efficient Cluster Head Selection Algorithm for Wireless Sensor Networks

Wireless sensor network (WSN) has become a very popular technology with a wide range of applications. It consists of several spatially distributed sensors that work collaboratively to monitor a given region of interest (ROI). The limited energy available for each sensor node is a crucial restriction that affects the overall performance of the network. Therefore, energy efficiency is a major concern in WSNs. Over the years, many techniques have been developed and used to reduce energy consumption in WSNs. Clustering is one of the most effective energy-saving techniques that significantly can improve the efficiency of WSNs in terms of the network lifetime, energy consumption, and the number of received packets. In this paper, an energy-efficient algorithm for cluster head (CH) selection based on a newly formulated fitness function and using the manta ray foraging optimization (MRFO) is proposed. The objective function for the proposed formulation takes into account different network parameters such as the average distance between the CH and the sensors in its cluster, the distance between CHs and the base station (BS), residual energy, and CH balancing. The proposed algorithm is tested by running many simulations under a variety of conditions. The simulation results showed that the proposed algorithm has a better performance than that of some other algorithms reported in the literature in terms of energy consumption, networks lifetime, and the number of received packets.

FOC-MOP: Fuzzy Optimal Clustering Based Multi-Objective Parameter Route Selection for Energy Efficiency

Wireless sensor networks are emerging and involved in a wide range of applications. Improving energy efficiency is a significant task in WSNs. Energy consumption is a crucial problem that affects the network lifetime due to energy limitations in sensor nodes. So, it's required to introduce more energy-efficient protocols for WSNs. Different types of energy-efficient solutions exist for WSNs. The energy consumption is optimized and controlled if clustering is an efficient data collection method. The cluster heads aggregate the data from the member nodes and transmit the aggregated data to BS (base station) either in an uncompressed or compressed manner after collecting all data by the cluster-heads (CHs). Due to the heavy inter-cluster relay and aggregation Overhead, earlier death happens to the CHs located very close to the sink. There is a need for an efficient and reliable CH selection algorithms to select reliable CHs to prevent the earlier death of CHs and frequent reselection of CHs. Therefore, in this work, a fuzzy optimal CH selection algorithm is proposed to select the optimal CHs to improve the network's lifetime. The proposed algorithm uses the following multi-objective parameters: cluster load, communication cost, neighbour density, inter and intra-cluster distance, and node energy for CH selection. The modified Emperor Penguin Optimization (EPO) is used in the proposed work to determine an optimal route for transmitting the data from CH to sink. The modified EPO algorithm helps to improve the multi-hop routing efficiency, reduces the cost of long-distance transmission, and decreases energy utilization during data transmission. The modified EPO considers the parameters like link quality factor, relative distance factor, and residual energy factor for relay selection. The proposed technique was compared against earlier methods like Z-DSS, CCR, RETOPSIS, and LEACH-FC. The various parameters like packet delivery ratio, lifetime, energy consumption, end-to-end delay, and Throughput are studied. The evaluation results prove that the proposed mechanism achieves 40% energy efficiency with 60% lesser Overhead and improves the Throughput rate by 50% than the compared methods.

Controlling energy aware clustering and multihop routing protocol for IoT assisted wireless sensor networks

SummaryBecause of recent breakthroughs in information technology, the Internet of Things (IoT) is becoming increasingly popular in a variety of application areas. Wireless sensor networks (WSN) are a critical component of IoT systems, and they consist of a collection of affordable and compact sensors that are utilized for data collecting. WSNs are used in a variety of IoT applications, such as surveillance, detection, and tracking systems, to sense the surroundings and transmit the information to the user's device. Smart gadgets, on the other hand, are limited in terms of resources, such as electricity, bandwidth, memory, and computation. A fundamental issue in the IoT‐based WSN is to achieve energy efficiency while also extending the network's lifetime, which is one of the limits that must be overcome. As a result, energy‐efficient clustering and routing algorithms are frequently employed in the IoT system. As a result of this inspiration, the authors of this research describe an Energy Aware Clustering and Multihop Routing Protocol with mobile sink (EACMRP‐MS) technique for IoT supported WSN. The EACMRP‐MS technique's purpose is to efficiently reduce the energy consumption of IoT sensor nodes, consequently increasing the network efficiency of the IoT system. The suggested EACMRP‐MS technique initially relies on the Tunicate Swarm Algorithm (TSA) for cluster head (CH) selection and cluster assembly, as well as the TSA. Furthermore, the type‐II fuzzy logic (T2FL) technique is used for the optimal selection of multi‐hop routes, with multiple input parameters being used to achieve this. Finally, a mobile sink with route adjustment scheme is presented to further increase the energy efficiency of the IoT system. This scheme allows for the adjustment of routes based on the trajectory of the mobile sink, which further improves the energy efficiency of the system. Using a detailed experimental analysis and simulation findings, it was discovered that the EACMRP‐MS technique outperformed the most recent state of the art methods in terms of a variety of evaluation metrics, indicating that it is a promising alternative.

Energy consumption in cluster communication using mcsbch approach in WSN

Internet of Things (IoT) proposed a new digital computing paradigm enabling interaction between devices and machines. It deliberately creates connectivity between the internet, electronics, and other forms of hardware. A novel modern cluster supervisor-based cluster head selection algorithm (MCSBCH) is proposed for the Wireless Sensor Network (WSN). The proposed cluster supervisor mechanism is responsible for controlling and monitoring the network effectively. In this approach, the cluster supervisor is the heart of the network, and the whole mechanism work under its supervision. The Cluster supervisor (CS) monitors the node’s energy level and allocates CH (cluster head) node. Each node’s energy level is considered for electing the CH. Obviously when the cluster head energy level gets drained, then it allocates the next higher energy node as cluster head. The assigned CH is the next node with the highest energy level known as the backup node. This cluster supervisor (CS) is supported by the cluster head (CH) and other backup nodes in the network. The proposed MCSBCH is boosted with an enhanced clustering routing protocol. An experimental result is based on the aspect of a lifetime, energy consumption, and throughput, to test the proposed mechanism performance.

IMPERIALIST COMPETITIVE ALGORITHM FOR INCREASING THE LIFETIME OF WIRELESS SENSOR NETWORK

Recent years have seen the rapid growth in the applications of wireless sensor network (WSN) which is due to the advances of sensor nodes with low cost and tiny size. Despite the various potential applications of WSN, one of the key tasks in sensor network design is to make sure that the network is functional as long as possible. This paper presents an energy-efficient cluster head selection algorithm for the clustering of heterogeneous WSN, inspired by Imperialist Competitive Algorithm (ICA). In order to reduce the network energy consumption and subsequently increases the sensor network lifetime, the clustering problem is transformed into an optimization problem and the specific cost function is used to select the cluster heads in a way that the energy utilization of the network is optimized. Extensive simulation works are done based on MATLAB to test the algorithm in various network scenarios, with different network sizes and number of nodes. Simulation results have shown that the proposed algorithm is able to extend the network lifetime compared to its comparative by up to 154 percent in terms of first node death. Furthermore, choosing the optimum set of cluster heads at every round has proved that our proposed algorithm not only could reduce the network energy consumption, but also improves the total data delivery at the base station up to 59 percent compared to the well-known algorithm.

An Improved Weighted and Location-Based Clustering Scheme for Flying Ad Hoc Networks.

Flying ad hoc networks (FANETs) have been gradually deployed in diverse application scenarios, ranging from civilian to military. However, the high-speed mobility of unmanned aerial vehicles (UAVs) and dynamically changing topology has led to critical challenges for the stability of communications in FANETs. To overcome the technical challenges, an Improved Weighted and Location-based Clustering (IWLC) scheme is proposed for FANET performance enhancement, under the constraints of network resources. Specifically, a location-based K-means++ clustering algorithm is first developed to set up the initial UAV clusters. Subsequently, a weighted summation-based cluster head selection algorithm is proposed. In the algorithm, the remaining energy ratio, adaptive node degree, relative mobility, and average distance are adopted as the selection criteria, considering the influence of different physical factors. Moreover, an efficient cluster maintenance algorithm is proposed to keep updating the UAV clusters. The simulation results indicate that the proposed IWLC scheme significantly enhances the performance of the packet delivery ratio, network lifetime, cluster head changing ratio, and energy consumption, compared to the benchmark clustering methods in the literature.

Taylor-spotted hyena optimization algorithm for reliable and energy-efficient cluster head selection based secure data routing and failure tolerance in WSN

Taylor-spotted hyena optimization algorithm for reliable and energy-efficient cluster head selection based secure data routing and failure tolerance in WSN

Blockchain-SDN-Based Energy-Aware and Distributed Secure Architecture for IoT in Smart Cities

Insecure and portable devices in the smart city’s Internet of Things (IoT) network are increasing at an incredible rate. Various distributed and centralized platforms against cyber attacks have been implemented in recent years, but these platforms are inefficient due to their constrained levels of storage, high energy consumption, the central point of failure, underutilized resources, high latency, etc. In addition, the current architecture confronts the problems of scalability, flexibility, complexity, monitoring, managing and collecting of IoT data, and defend against cyber threats. To address these issues, the authors present a distributed and decentralized blockchain-software-defined networking (SDN)-based energy-aware architecture for IoT in smart cities. Thus, SDN is continuously observing, controlling, and managing IoT devices activities and detects possible attacks in the network; blockchain provides adequate security and privacy against cyber attacks, and reduces the central point of failure issues; network function virtualization (NFV) is used to saving energy, load balancing, as well as increasing the lifetime of the entire network. Also, we introduce a cluster head selection (CHS) algorithm to reduce the energy consumption in the presented model. Finally, we analyze the performance using various parameters (e.g., throughput, response time, gas consumption, and communication overhead) and demonstrate the result that provides higher throughput, lower response time, and lower gas consumption than existing works for smart cities.

Towards Design of an Efficient Sensing Data Acquisition Scheme for UAVs-Assisted Wireless Sensor Networks

This paper investigates sensing data acquisition issues from large-scale hazardous environments using UAVs-assisted WSNs. Most of the existing schemes suffer from low scalability, high latency, low throughput, and low service time of the deployed network. To overcome these issues, we considered a clustered WSN architecture in which multiple UAVs are dispatched with assigned path knowledge for sensing data acquisition from each cluster heads (CHs) of the network. This paper first presents a non-cooperative Game Theory (GT)-based CHs selection algorithm and load balanced cluster formation scheme. Next, to provide timely delivery of sensing information using UAVs, hybrid meta-heuristic based optimal path planning algorithm is proposed by combing the best features of Dolphin Echolocation and Crow Search meta-heuristic techniques. In this research work, a novel objective function is formulated for both load-balanced CHs selection and for optimal the path planning problem. Results analyses demonstrate that the proposed scheme significantly performs better than the state-of-art schemes.

Energy-Saving Clustering Routing Protocol for Wireless Sensor Networks Using Fuzzy Inference

One of the most important researches in wireless sensor networks is energy saving. The clustering algorithm can efficiently save energy. However, most of the existing clustering algorithms use a fixed cluster head election algorithm and perform each round of clustering, so that unsuitable nodes are continuously elected as cluster heads, leading to rapid exhaustion of node energy and short network life. In order to solve these problems, this paper proposes an Energy-saving Fuzzy Clustering Routing algorithm (EFCR) for wireless sensor networks,which designs two types of clustering, Clustering Type1 and Clustering Type2, and alternately executes them in different rounds according to the threshold. Clustering Type1 considers the parameters that affect the energy consumption of the node: the distance from the node to the base station, the number of neighbor nodes of the node, and the remaining energy of the node as fuzzy inputs, and calculates the fitness of the node as a cluster head through fuzzy inference to avoid the occurrence of unsuitable nodes being successively elected as cluster heads. Clustering Type2 performs data transmission by trusting the last round of cluster heads, which reduces the number of control data packets in each round of clustering and reduces the overall energy consumption of the network. This paper conducted simulations in four different scenarios, compared other clustering algorithms of the same type, and analyzed the remaining energy of the network, the number of surviving nodes, and the life of the network. The results show that EFCR algorithm is better than others.