Cluster Head Selection Mechanism Research Articles

Adaptive fuzzy-based node communication performance prediction with hybrid heuristic Cluster Head selection framework in WSN using enhanced K-means clustering mechanism

The “Wireless Sensor Networks (WSN)” has gained a lot of interest among research scholars and has been utilized in various advanced applications in distinct fields. Along with the load balancing techniques, the clustering scheme also prolongs the network’s overall lifespan. The “Cluster Head (CH)” performs the task of load balancing between the nodes in the “Clustering algorithm”; hence, the CH selection procedure is regarded as a critical task in the case of the clustering algorithms. Depending on the CH selection and cluster nodes, the rate of energy consumed by these CHs will be reduced in the wireless sensor. CH selection is a promising solution for the transmission of information within various parameters. Thus, CH selection leads to an increase in the duration of the system and a reduction in the energy utilization by the nodes. Therefore, an “optimization-based CH selection” mechanism in WSN is developed in this paper along with an enhanced node communication performance prediction strategy to provide better communication between the “Sensor Nodes (SNs)” with limited energy expenditure. The node’s communication performance is predicted using the Adaptive Fuzzy, in which metrics such as bit rate, latency, throughput, loss, and packet delivery ratio are specified as the input to the network. Here, the parameters within the fuzzy network are tuned with the help of the recommended “Hybrid Position of Heap and African Buffalo Optimization (HP-HABO)”. Then, to perform efficient node clustering, the “Optimal K-Means Clustering (OKMC)” approach is executed and the CHs are formed using the developed HP-HABO. The objective function depends on the constraints like energy, distance, and predicted communication performance attained by forming these CHs. The performance of the developed CH selection mechanism is verified by analyzing the experimental outcome of the proposed technique with different optimization algorithms and previous works concerning the objective constraints.

An energy‐efficient Chebyshev fire hawks optimization algorithm for energy balancing in sensor‐enabled Internet of Things

SummarySensor‐enabled systems have been used successfully in agricultural, healthcare, commercial, and military application domains. Recently, there has been significant interest in the intelligent applications of sensor‐enabled technologies, particularly in the domains of smart grid, Internet of Vehicles (IoV), body area networks, and the Internet of Things (IoT). In recent research, various protocols and algorithm are developed for effective energy‐efficient routing and energy balancing. These existing models have some issues like high energy consumption and minimum network life time. In order to overcome these existing issues, a novel cluster head selection and routing mechanism in a wireless sensor network (WSN) environment is proposed. The clustering process has been formed by an enhanced Taylor kernel fuzzy C‐means algorithm (TKFC‐means). The cluster head in the group of sensor nodes has been identified based on energy and distance calculation. Finally, the routing has been performed by a novel energy‐efficient Chebyshev fire hawks optimization‐based routing protocol to route data to the edge server, which helps to balance the energy effectively. This protocol takes into account various factors, including distance, cost, residual energy, load, temperature, latency, and overall energy. The proposed model can obtain a throughput value of 82 Mbps for the sensor nodes at 500 and an end‐to‐end delay of 3.6 at 500 sensor nodes. The packet delivery ratio and loss ratio attain 96.4% and 2.7%, respectively, with 500 sensor nodes in the proposed approach. The proposed method consumes 0.45 mJ of energy with 500 nodes. From this analysis, the proposed model can obtain better results than the existing compared models.

Secured osprey-based energy efficient routing and congestion control in WSN

An adaptive metaheuristic optimization-based QoS-aware, Energy-balancing, Secure Routing Protocol (AQoS-ESRP) is proposed in this article. The network is modelled as a biconcentric hexagon (BiCon-HexA), and the clusters are formed within the BiCon-HexA network. The BiCon-HexA is divided into six sectors to support effective data aggregation, and then clusters are formed within all sectors. The optimal cluster head (CH) selection mechanism is modelled by an Adaptive Hunter-Prey Optimization (AdapH-PO) algorithm considering QoS parameters. Data aggregation is then done securely with an enhanced encryption approach. Here, upgraded elliptic curve cryptography (UEllip-CC) is used to encode data in CH. This UEllip-CC approach provides security improvements in data transmission. Furthermore, in this study, CHs are combined in the multi-hop routing of data packets to reduce the power consumption problems of wireless sensor networks (WSN). To determine the optimal route for data transmission, an energy-balanced multi-path routing algorithm called improved convolutional osprey network (ICON) is presented. Nevertheless, the data transmission nodes can be overloaded in the data routing phase. Here, the congestion problem can be solved by applying an improved version of the Random Early Detection (RED) congestion control model to discard the data packets more noticeably. The simulation of AQoS-ESRP is done with Matlab, and the performance is evaluated using different metrics. When compared to existing systems, the simulation results clearly indicate a significantly higher throughput and delay. Thus, the AQoS ESRP model is employed to maximize the overall data transfer in the WSN.

Bio-inspired multi-hop clustering algorithm for FANET

Clustering has received extensive attention in recent years as an important approach to address the poor scalability in flying ad hoc networks (FANETs). However, due to the highly mobile nature of FANETs, frequent changes in cluster topology result in increased control overhead and reduced communication efficiency. To tackle these challenges, a novel heuristic clustering algorithm called the physarum-inspired clustering algorithm (PICA) is proposed in this paper. It leverages the intelligence and adaptability of Physarum polycephalum. The PICA adopts a distributed multi-hop clustering approach. During the cluster formation phase, and inspired by the foraging behavior of Physarum polycephalum, the algorithm incorporates link stability, residual energy, and link communication quality as evaluation metrics, thereby ensuring cluster coverage and stability. By selecting the most stable node within an n-hop range as the cluster head, the cluster stability is significantly enhanced. In the cluster maintenance phase, the PICA improves the backup cluster head selection mechanism and introduces damage detection and cluster merging mechanisms to further enhance the robustness and stability of clusters. Experimental results demonstrate that compared to existing EMASS and OSCA algorithms, the proposed PICA reduces the average end-to-end delay by 31.6 % and 25.27 %, improves the average packet delivery ratio by 27.7 % and 19.12 %, and decreases the average inter-cluster switching times by 14.71 % and 14.38 %, respectively. In conclusion, the PICA effectively addresses the issue of poor scalability in FANETs by leveraging the intelligence of multi-headed myxobacteria and adopting a multi-hop clustering approach. As indicated by experimental results, the proposed algorithm demonstrates significant performance improvements in terms of network metrics.

Energy Efficient CH Selection Scheme Based on ABC and Q-Learning Approaches for IoUT Applications

Nowadays, the Internet of Underwater Things (IoUT) provides many marine 5G applications. However, it has some issues with energy efficiency and network lifetime. The network clustering approach is efficient for optimizing energy consumption, especially for underwater acoustic communications. Recently, many algorithms have been developed related to clustering-based underwater communications for energy efficiency. However, these algorithms have drawbacks when considered for heterogeneous IoUT applications. Clustering efficiency in heterogeneous IoUT is influenced by the uniform distribution of cluster heads (CHs). As a result, conventional schemes are inefficient when CHs are arranged in large and dense nodes since they are unable to optimize the right number of CHs. Consequently, the clustering approach cannot improve the IoUT network, and many underwater nodes will rapidly consume their energies and be exhausted because of the large number of clusters. In this paper, we developed an efficient clustering scheme to effectively select the best CHs based on artificial bee colony (ABC) and Q-learning optimization approaches. The proposed scheme enables an effective selection of the CHs based on four factors, the residual energy level, the depth and the distance from the base station, and the signal quality. We first evaluate the most suitable swarm algorithms and their impact on improving the CH selection mechanism. The evaluated algorithms are generic algorithm (GA), particle swarm optimization (PSO), ant colony optimization (ACO), and ABC. Then, the ABC algorithm process is improved by using the Q-learning approach to improve the process of ABC and its fitness function to optimize the CH selection. We observed from the simulation performance result that an improved ABC-QL scheme enables efficient selection of the best CHs to increase the network lifetime and reduce average energy consumption by 40% compared to the conventional ABC.

CL‐HPWSR: Cross‐layer‐based energy efficient cluster head selection using hybrid particle swarm wild horse optimizer and stable routing in IoT‐enabled smart farming applications

AbstractIn the recent days, the Internet of Things (IoT) has assumed a significant role in automized farming applications. The farmers can receive frequent information corresponding to the soil moisture, water used, humidity, temperature, moisture, etc., by utilizing actuators and sensor nodes. Various clustering techniques used in wireless sensor network (WSN) applications have achieved energy‐efficient results. However, IoT‐enabled smart farming (SF) applications require scalable networks and long‐distance communication. The existing clustering techniques cannot provide SF solutions, and work related to communication overhead, end‐to‐end delay, latency etc., has not been taken up exhaustively. Therefore, in this article, a cross‐layer‐based hybrid particle Swarm wild horse optimizer (PSWHO) and stable routing technique (CL‐HPWSR) is designed to minimize energy consumption, communication delay, latency etc. in SF applications. Initially, the network is designed to be Bi‐concentric Hexagons. The cross‐layer‐based optimal cluster head (CH) selection mechanism is designed using the hybrid optimization algorithm‐named PSWHO to mitigate excess energy consumption problems in WSN. While clustering, cross‐layer parameters from different layers such as physical, medium access control, and network are used to compute the probability of each sensor node. Moreover, the deep learning‐based routing algorithm extended deep golden eagle neural network (EDGENN) is introduced to explore the optimal route for transmitting the data. The performance of CL‐HPWSR is implemented in Matlab by focusing energy‐efficiency, computational‐efficiency and QoS‐efficiency factors. Finally, in comparison with state‐of‐the‐art‐based SF approaches such as CL‐IoT, FEEC‐IIR, BiHCLR, PSO‐ECHS, and PSO‐C, the CL‐HPWSR has performed better in terms of energy‐efficiency, QoS‐efficiency and computational‐efficiency.

EECHS-ARO: Energy-efficient cluster head selection mechanism for livestock industry using artificial rabbits optimization and wireless sensor networks

<abstract> <p>In the livestock industry, wireless sensor networks (WSNs) play a significant role in monitoring many fauna health statuses and behaviors. Energy preservation in WSNs is considered one of the critical, complicated tasks since the sensors are coupled to constrained resources. Therefore, the clustering approach has proved its efficacy in preserving energy in WSNs. In recent studies, various clustering approaches have been introduced that use optimization techniques to improve the network lifespan by decreasing energy depletion. Yet, they take longer to converge and choose the optimal cluster heads in the network. In addition, the energy is exhausted quickly in the network. This paper introduces a novel optimization technique, i.e., an artificial rabbits optimization algorithm-based energy efficient cluster formation (EECHS-ARO) approach in a WSN, to extend the network lifetime by minimizing the energy consumption rate. The EECHS-ARO technique balances the search process in terms of enriched exploration and exploitation while selecting the optimal cluster heads. The experimentation was carried out on a MATLAB 2021a platform with varying sensor nodes. The obtained results of EECHS-ARO are contrasted with other existing approaches via teaching–learning based optimization algorithm (TLBO), ant lion optimizer (ALO) and quasi oppositional butterfly optimization algorithm (QOBOA). The proposed EECHS-ARO enriches the network lifespan by ~15% and improves the packet delivery ratio by ~5%.</p> </abstract>

RANCE: A Randomly Centralized and On-Demand Clustering Protocol for Mobile Ad Hoc Networks

LEACH-like clustering protocols focus mainly on the low-power, low-rate, and low-wakeup network applications, and work in a multiround clustering strategy that causes frequent handovers of cluster heads (CHs), thus less support for real-time services that require stable cluster topologies. Besides, these protocols are faced with respective drawbacks, such as suboptimality of selected heads, costly node-base station (BS) energy overheads, lack of runtime cluster maintenance, etc. This article proposes RANCE, a randomly centralized and on-demand clustering protocol, aiming at prolonging nodes’ clustered time to support internodes collaboration while being energy efficient in mobile ad hoc networks. First, RANCE designs a randomly centralized CH selection mechanism in which every node in the local wireless network is eligible to initiate the centralized CH selection, so that the self-organizing characteristics of mobile ad hoc nodes can be utilized for head selection optimization. Second, taking into account the wireless volatility caused by changes of topology, obstacles, signal strength, etc., the fine-grained cluster relationships maintenance is provided by means of multilevel aliveness and adaptive bidirectional heartbeat packets. Third, RANCE works in an event-driven and on-demand manner instead of a time-triggered manner in LEACH-like protocols, to reduce the impact on continuous services caused by frequent CH handovers among all nodes. Simulation results show that RANCE provides longer clustered time (over 99% of nodes’ lifetime in networks more than 100 nodes) and good clustering scalability with high consistency at minimum energy cost, and exhibits good potentials in mobile wireless environments that are infrastructureless/poor for continuous missions.

Blockchain Based Authentication and Cluster Head Selection Using DDR-LEACH in Internet of Sensor Things.

This paper proposes a blockchain-based node authentication model for the Internet of sensor things (IoST). The nodes in the network are authenticated based on their credentials to make the network free from malicious nodes. In IoST, sensor nodes gather the information from the environment and send it to the cluster heads (CHs) for additional processing. CHs aggregate the sensed information. Therefore, their energy rapidly depletes due to extra workload. To solve this issue, we proposed distance, degree, and residual energy-based low-energy adaptive clustering hierarchy (DDR-LEACH) protocol. DDR-LEACH is used to replace CHs with the ordinary nodes based on maximum residual energy, degree, and minimum distance from BS. Furthermore, storing a huge amount of data in the blockchain is very costly. To tackle this issue, an external data storage, named as interplanetary file system (IPFS), is used. Furthermore, for ensuring data security in IPFS, AES 128-bit is used, which performs better than the existing encryption schemes. Moreover, a huge computational cost is required using a proof of work consensus mechanism to validate transactions. To solve this issue, proof of authority (PoA) consensus mechanism is used in the proposed model. The simulation results are carried out, which show the efficiency and effectiveness of the proposed system model. The DDR-LEACH is compared with LEACH and the simulation results show that DDR-LEACH outperforms LEACH in terms of energy consumption, throughput, and improvement in network lifetime with CH selection mechanism. Moreover, transaction cost is computed, which is reduced by PoA during data storage on IPFS and service provisioning. Furthermore, the time is calculated in the comparison of AES 128-bit scheme with existing scheme. The formal security analysis is performed to check the effectiveness of smart contract against attacks. Additionally, two different attacks, MITM and Sybil, are induced in our system to show our system model’s resilience against cyber attacks.

An Improved Vanet Clustering and Channel Quality using Movc and Weighted Probability

VANET offers a vast range of application which will make travel experience safe. The concept behind VANET is quite simple which incorporates wireless communication and capable of sharing data; the vehicles are turned for offering network to provide services which can be used in the home or office networks. VANET network nodes are move in-network with higher velocity in the network. This higher mobility of vehicles causes increased resource and energy utilization which causes failure of nodes. Resource and energy consumption is higher for inefficient constructed clustering for VANET. Also, vehicle mobility causes several obstacles in data transmission this causes degraded channel quality. data transmission this causes degraded channel quality. In this paper concentrated on improving channel quality and energy efficiency in the network. Initially, performed efficient clustering through middle-order based cluster head (CH) election (MOVC) mechanism. Through effective CH election resource and energy utilization is limited. In the next stage, channel quality is improved by the proposed weighted probability approach for effective data transmission between vehicles in the VANET network. The weighted probability approach estimates the path for data transmission to destination vehicle. Through the selection of CHs using the middle-order approach, the network exhibits an effective maintenance phase here, the weighted probability is applied for the estimation of the path for effective data transmission in the VANET. The comparative analysis of the proposed approach exhibits significant performance rather than conventional technique. The estimated residual energy of the proposed approach is almost 20% higher than ACO, PSO, and KH. The packet delivery rate is observed around 80% and delay is minimal as of 2.5sec.

Markov Modulated Bernoulli Prediction Process‐based Cluster Head Selection Mechanism for improving resilience in wireless sensor networks

SummaryThe clustering process in wireless sensor networks is considered to be predominant for facilitating minimized energy consumptions through the selection of potential cluster heads that are efficient in energy and trust. The significant cluster head selection is deterministic in topology control with the view to manage the sensor member nodes of the cluster with the view to enhance energy efficiency in the network. In this paper, a Markov Modulated Bernoulli Prediction Process‐based Cluster Head Selection Mechanism (MMBPP‐CHSM) is proposed by modeling the success probability of sensor nodes' state into a Bernoulli process that dynamically evolves over time based on Markov chain for extending the lifetime of the network. This proposed MMBPP‐CHSM scheme is vital in quantifying the reliability and availability of the sensor nodes in a dynamically evolving wireless sensor network through the estimation of trust and energy in order to facilitate effective cluster head selection. The simulation results of the proposed MMBPP‐CHSM scheme confirmed a superior enhancement in percentage of alive nodes and death nodes in the network to a maximum level of 28% and 23% on par with the baseline cluster head selection approaches used for investigation.

Simulation research of nodes target tracking algorithm based on clustering in wireless sensor network

Since sensor nodes are constrained in cost and volume, they have limited ability in data storage and processing. Therefore, the routing algorithm needs lower time and space complexity for energy conservation. This article design a dynamic cluster head selection mechanism based on back-off time of residual energy of nodes, aiming at energy conservation and low complexity for the algorithm. At the phase of cluster head selection, we reduce the back-off time of nodes with more energy, so it can win the competition in cluster head selection and control the number of cluster heads, by controlling the radius of cluster lead broadcasting. Then the cluster member nodes are chosen according to the hop range to cluster head, whose weights are computed by the distance to cluster head and the residual energy. At the premise of saving self-energy, it can choose different cluster head and save the energy of cluster head to achieve global energy balance and prolong the network lifetime. The simulations verify that the improved algorithm can make reasonable distribution location of cluster head. The membership has close number and the reasonable topology ensure the global consumption of network.

An energy-efficient artificial bee colony-based clustering in the internet of things

Wireless communication on the Internet of Things (IoT) requires context-aware data transmission protocols. Developing an energy-efficient clustering mechanism is the primary challenge in data transmission over IoT. The existing approaches struggle with the short lifetime of IoT, imbalance load distribution, and high transmission delay. This paper proposes a novel cluster-head selection and clustering mechanism on IoT. It is composed of two main phases. The first phase selects the near-optimal cluster-heads using Artificial Bee Colony (ABC) algorithm. Performance criteria include the residual energy of the devices, the number of neighbors, Euclidean distance between devices and the sink, and Euclidean distance between each device and its neighbors. The principal objective of the second phase is to group devices into some clusters based on Euclidean distance between each cluster-head and its members, and the data volume generated by clusters. Simulation results verify that our mechanism improves energy consumption, lifetime, and transmission delay.

RaCH: A New Radial Cluster Head Selection Algorithm for Wireless Sensor Networks

This paper presents a radial cluster heads selection mechanism for homogenous wireless sensor networks. It aims to ensure a good load balancing and enhance the network lifetime. A performance comparison between LEACH, Mod_LEACH, Multi-Chain-PEGASIS, M-GEAR protocols and our proposal in term of network lifetime, stability/instability periods and the evolution of the number of cluster heads is carried out in this work. Obtained results show that developing an efficient cluster heads selection algorithm enhances considerably the network lifetime and the QoS in the network. Simulations results under NS3 simulator showed that our proposal can extend the network life more than 300% compared to the famous LEACH protocol and ensure a network lifetime of about 5000 rounds while it does not exceed 2500 rounds using M-GEAR.

Study on the algorithm for smart community sensor network routing with adaptive optimization via cluster head election

AbstractTo reduce the uneven energy consumption for the data transmission and extend network life of intelligent community sensor network, an adaptive routing optimized algorithm for intelligent community sensor networks with cluster head election is proposed. In this algorithm, a three‐dimensional clustering method adapted to the structure of intelligent community sensor network is proposed. The three‐dimensional clustering method uses the cluster head election mechanism based on minimizing the total transmission loss to optimize the energy of the intelligent community sensor network. Second, an adaptive ant colony propagation method is proposed to solve the problem of intercluster data propagation after clustering. With the best path finding algorithm of ant colony algorithm, energy balance routing with lower energy loss and lower packet error rate is proposed. Finally, the simulation results show that the algorithm has better performance in reducing energy consumption and delay, improving transmission efficiency and node survival time.

An Energy-Efficient Mobility-Based Cluster Head Selection for Lifetime Enhancement of Wireless Sensor Networks

Wireless based sensor networks contain sensors for environment monitoring but have restricted resources. Many clustering protocols are designed to prolong network lifetime but have problems of inadequate cluster head selection criteria, fixed clustering, and static rounds which consume more energy. It is needed to develop an adaptive clustering strategy for better CH selection and load balancing. In this article, we introduced an energy-efficient mobility based cluster head selection mechanism to overcome these limitations. CH selection is based on dedicated parameters that have a huge impact on the sensor energy consumption. The weightage of each node is calculated on the base of the node's mobility level, residual energy, distance to sink, and density of neighbors. Inter-cluster communication uses single-hop/multi-hop. MATLAB is used to perform simulations. Results show that the proposed approach EEMCS performs better as compared to the existing algorithms CRPD, LEACH, and MODLEACH in terms of load balancing, network stability, energy depletion, and throughput. Energy utilization in the case of EEMCS is much less and the network lifetime is greater than other existing protocols.

Cooperative Relay Selection for Load Balancing With Mobility in Hierarchical WSNs: A Multi-Armed Bandit Approach

Energy efficiency is the major concern in hierarchical wireless sensor networks(WSNs), where the major energy consumption originates from radios for communication. Due to notable energy expenditure of long-range transmission for cluster members and data aggregation for Cluster Head (CH), saving and balancing energy consumption is a tricky challenge in WSNs. In this paper, we design a CH selection mechanism with a mobile sink (MS) while proposing relay selection algorithms with multi-user multi-armed bandit (UM-MAB) to solve the problem of energy efficiency. According to the definition of node density and residual energy, we propose a conception referred to as a Virtual Head (VH) for MS to collect data in terms of energy efficiency. Moreover, we naturally change the relay selection problem into permutation problem through employing the two-hop transmission in cooperative power line communication, which deals with long-distance transmission. As far as the relay selection problem is concerned, we propose the machine learning algorithm, namely MU-MAB, to solve it through the reward associated with an increment for energy consumption. Furthermore, we employ the stable matching theory based on marginal utility for the allocation of the final one-to-one optimal combinations to achieve energy efficiency. In order to evaluate MU-MAB, the regret is taken advantage to demonstrate the performance by using upper confidence bound (UCB) index. In the end, simulation results illustrate the efficacy and effectiveness of our proposed solutions for saving and balancing energy consumption.

Analytic Network Process-Based Cluster Head Selection Mechanism for Extending the Network Lifetime

Analytic Network Process-Based Cluster Head Selection Mechanism for Extending the Network Lifetime

Stochastic QoE-aware optimization of multisource multimedia content delivery for mobile cloud

The increasing popularity of mobile video streaming in wireless networks has stimulated growing demands for efficient video streaming services. However, due to the time-varying throughput and user mobility, it is still difficult to provide high quality video services for mobile users. Our proposed optimization method considers key factors such as video quality, bitrate level, and quality variations to enhance quality of experience over wireless networks. The mobile network and device parameters are estimated in order to deliver the best quality video for the mobile user. We develop a rate adaptation algorithm using Lyapunov optimization for multi-source multimedia content delivery to minimize the video rate switches and provide higher video quality. The multi-source manager algorithm is developed to select the best stream based on the path quality for each path. The node joining and cluster head election mechanism update the node information. As the proposed approach selects the optimal path, it also achieves fairness and stability among clients. The quality of experience feature metrics like bitrate level, rebuffering events, and bitrate switch frequency are employed to assess video quality. We also employ objective video quality assessment methods like VQM, MS-SSIM, and SSIMplus for video quality measurement closer to human visual assessment. Numerical results show the effectiveness of the proposed method as compared to the existing state-of-the-art methods in providing quality of experience and bandwidth utilization.

A Split Network based Routing Approach in Wireless Sensor Network to Enhance Network Stability

Background & Objective: The operating efficiency of a sensor network totally relies upon the energy that is consumed by the nodes to perform various tasks like data transmission etc. Thus, it becomes mandatory to consume the energy in an intelligent way so that the network can run for a long period. This paper proposed an energy efficient Cluster Head (CH) selection mechanism by considering the distance to Base Station (BS), distance to node and energy as major factors. The concept of volunteer node is also introduced with an objective to reduce the energy consumption of the CH to transmit data from source to BS. The role of the volunteer node is to transmit the data successfully from source to destination or BS. Conclusion: The results are observed with respect to the Alive nodes, dead nodes and energy consumption of the network. The outcome of the proposed work proves that it outperforms the traditional mechanisms.