Backup Cluster Head Research Articles

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.

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.

FTOR-Mod PSO: A fault tolerance and an optimal relay node selection algorithm for wireless sensor networks using modified PSO

The wireless sensor network (WSN) is deployed with enormous sensors responsible for data collection. The most critical issue in WSN is higher energy utilization which degrades network lifetime and communication ability. Hence, several methods are applied in WSNs to expand network lifetime. Clustering is a proven technique for reducing energy consumption in WSNs. CH collects data from different sensor nodes (SN) in every cluster and then forwards it to the sink node. On the other hand, fault occurrence in CH collapses entire data communication. Hence, this paper proposes a fault-tolerant cluster-based routing technique to avoid such difficulties. The proposed scheme is fault tolerance and optimal relay node with modified Particle Swarm Optimization FTOR-Mod PSO. Here, fault tolerance is achieved by selecting the backup cluster head (BKCH) for every cluster through the battle royale optimization (BRO) algorithm. Moreover, an aggregator node (AG) is adopted for inter-cluster data transmission, and modified PSO (Mod PSO) identifies CH and AG’s best relay. Results demonstrate that the proposed method reduces delay to 0.15 s by reducing the routing overhead to 2.4%, thereby improving the network lifetime to 92.714%.

Survivability Analysis of Unmanned Aerial Vehicle Network based on Dynamic Weighted Clustering Algorithm with Dual Cluster Heads

The unmanned aerial vehicles (UAVs) network is vulnerable due to the high mobility and energy-constrained characteristics of UAVs. Nonetheless, as a UAV-based communication network, a stable network topology is crucial for efficient communication. To this end, in this paper, we propose a dynamic weighted clustering algorithm with dual cluster heads (DWCA-DCH) in this paper to deploy the UAV network. To trade off communication efficiency and lifetime, the selection of prime and backup cluster heads is designed by synthetically considering communication quality and remaining energy of the UAV. Furthermore, a survivability analysis method based on Markov process (SAM-MP) is constructed to analyze the survivability performance of the proposed UAV network based on DWCA-DCH when the UAV node suffers from energy exhausting or accidents. The simulation results show that the survivability and stability of the UAV cluster ad hoc network based on DWCA-DCH proposed in this paper is improved by about 35% compared with the single cluster head network.

An Improved Lifetime and Energy Consumption with Enhanced Clustering in WSNs

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

A fault tolerant optimal relay node selection algorithm for Wireless Sensor Networks using modified PSO

Wireless Sensor Networks (WSNs) have energy-constraints that restricts to achieve prolonged network lifetime. To optimize energy consumption of sensor nodes, clustering is one of the efficient techniques for minimization of energy conservation in WSNs. This technique sends the collected data towards the SINK based on cluster head (CH) nodes that leads to the saving of energy. WSNs have been faced a crucial issue of fault tolerance and the overall data communication is collapsed due to the failure of cluster head. Various fault-tolerance clustering methods are available for WSNs, but they are not selected the backup nodes properly. The backup nodes’ closeness or location to the other remaining nodes is not considered in these methods. They may increase network overhead with the backup nodes accessibility. A fault-tolerance cluster-based routing method is presented in this paper that aims on providing fault tolerance for relay selection in addition to the data aggregation method for clustered WSNs. The proposed method utilizes backup mechanism & the Particle Swarm Optimization (PSO) to achieve this. Based on the distance from sink, residual energy, and link delay parameters, the CHs are chosen and the network is categorized into the clusters. The Backup CHs are selected by estimating the centrality among the nodes. As a part of intra-cluster communication for reducing the aggregation overhead among CHs, the Aggregator (AG) nodes are deployed in every cluster. So that they act as the bridge between the member nodes and CHs. These AG nodes aggregates the information from member nodes and deliver it to the CHs. The PSO with modified fitness function is used to identify the best relays between AG and member nodes. The proposed mechanism is compared with existing techniques such as EM-LEACH AI-Sodairi and Ouni (2018), QEBSR Rathee et al. (2019), QOS-IHC Singh and Singh (2019), and ML-SEEP Robinson et al. (2019). The simulation results proved that the proposed mechanism reduces overhead by 55% and improve the energy consumption & throughput by 40% & 60% respectively.

EETSP: Energy‐efficient two‐stage routing protocol for wireless sensor network‐assisted Internet of Things

SummaryWireless sensor network (WSN) is a key technology that can support Industrial Revolution 4.0. The sensor nodes (SNs) are the important component of WSN. Recharge or repair of batteries of SNs is impossible, and thus, energy is the most valuable resource of WSN. Over the years, many methods have been proposed to conserve the precious energy resource of WSN. The most important concern in WSN is how much of the energy of the SNs can be saved, which can contribute to the better network life of WSN. WSN‐assisted IoT output is reduced when a wide region of the network is deployed. Energy‐efficient routing is one of the ways to reduce power consumption in WSNs. Therefore, a novel energy‐efficient two‐stage routing protocol (EETSP) has been proposed to reduce the energy used in WSN and extend the stability period as well as the network lifetime of WSN. Through this protocol, the number of packets communicated to the base station (BS) is significantly improved, and the cluster heads (CHs) and secondary cluster heads (SCHs) reduce the energy utilization in the network. SCH works as a backup CH. EETSP consists of two stages. The selection of CH and SCH is made in the primary stage, based on the input parameters. Intercluster routing and intracluster routing are performed in the second stage. The improvements of EETSP are examined against LEACH and Z‐SEP, and obtained results indicate that the EETSP is better in terms of stability period, throughput and network lifetime.

An energy-aware clustering and two-level routing method in wireless sensor networks

Wireless sensor networks (WSN) are consisted of several sensor nodes scattered in an area to gather data from their ambient environment and send it to base station (BS). The energy of nodes in WSNs is limited. One of the most significant issues in WSNs is reducing the energy consumption of nodes, which leads to increased network lifetime. One method to reduce energy consumption in WSNs is energy-efficient routing. In energy-efficient routing, gathered data is sent to the sink in a way to save the energy of nodes. This paper proposed a cluster-based two-level routing method. In the proposed method, we seek to improve packet delivery rate and reduce energy consumption through clustering, selecting backup cluster head (BCH), layering cluster heads (CH), and dividing each cluster into four sections. The method is consisted of two phases. In the first phase, CHs and BCHs are selected, and nodes are clustered based on their residual energy, distance to BS, and centrality. To perform intra-cluster routing, each cluster is divided into four sections so that nodes directly deliver their data to CH or through the most proper node in their sections. To perform inter-cluster routing, CHs are layered based on their distance to BS. Since CHs are layered, the source CH selects the next hop from CHs in the upper layer based on their residual energy and distance to BS. The proposed method has been simulated by NS-2 software and compared with CFPT (Yarinezhad and Hashemi in J Syst Softw 155:145–161, 2019), FBCFP (Thangaramya et al. in Comput Netw 151:211–223, 2019) and DFCR (Azharuddin et al. in Comput Electr Eng, 41:177–190, 2015) methods. The results reveal that the proposed method leads to reduced end-to-end delay, number of total hops, energy consumption as well as increased packet delivery rate and network lifetime.

Quantitative survivability analysis using probability model checking: A study of cluster-based vehicle networks with dual cluster heads

As an important part of future 5G wireless networks, a vehicular network demands safety, reliability and connectivity. In this context, networking survivability is usually considered an important metric to evaluate network performance. In this paper, we propose a survivability model for vehicle communication networking based on dual cluster heads, wherein a backup cluster head (CH) will be activated if the primary CH fails, thereby effectively enhancing the network lifetime. Additionally, we introduce a software rejuvenation strategy for the prime CH to further improve the survivability of the entire network. Using the Probabilistic Symbolic Model Checker (PRISM), we verify and discuss the proposed survivability model via numerical simulations. The results show that network survivability can be effectively improved by introducing an additional CH and further enhanced by adopting the software rejuvenation technique.

An Energy-Efficient Compressive Sensing-Based Clustering Routing Protocol for WSNs

A novel algorithm which combined the merits of the clustering strategy and the compressive sensing-based (CS-based) scheme was proposed in this paper. The lemmas for the relationship between any two adjacent layers, the optimal size of clusters, the optimal distribution of the cluster head (CH), and the corresponding proofs were presented first. In addition, to alleviate the “hot spot problem” and reduce the energy consumption resulted from the rotation of the role of CHs, a third role of backup CH (BCH) as well as the corresponding mechanism to rotate the roles between the CH and BCH were proposed. Subsequently, the energy-efficient compressive sensing-based clustering routing (EECSR) protocol was presented in detail. Finally, extensive simulation experiments were conducted to evaluate its energy performance. Comparisons with the existing clustering algorithms and the CS-based algorithm verified the effect of EECSR on improving energy efficiency and extending the lifespan of wireless sensor networks.

Fault Tolerance and Energy Efficient Clustering Algorithm in Wireless Sensor Networks: FTEC

Wireless sensor networks are employed to monitor physical areas in different places. Fault tolerance and energy efficiency are main challenges of the networks. WSNs are designed to transmit sensed data to the base station when a part of the network is faulty. In this paper, an algorithm is presented to withstand the challenges. In the suggested algorithm, HEED clustering algorithm and sleep/wake-up method are used for the members of cluster to improve energy consumption. To detect and recover the faults of cluster heads and cluster member nodes, a node is selected as backup cluster head. Also, weighted median is employed to detect and recover the faults of cluster nodes. To recover an fault, the faulty node is isolated and replaced by a neighboring node going from sleep mode to wake-up mode. On comparing the proposed algorithm with HEED and DFD algorithms, the results of simulation reveal that energy consumption is improved and the rate of received correct data, detection accuracy and survival nodes are increase and false alarm rate are decrees.

A Modified Energy-Efficient Clustering with Splitting and Merging for Wireless Sensor Networks using Cluster-Head Handover Mechanism

Energy efficiency is one of the most important challenges for Wireless Sensor Networks (WSNs). This is due to the fact that sensor nodes have limited energy capacity. Therefore, the energy of sensor nodes has to be efficiently managed to provide longer lifetime for the network. To reduce energy consumption in WSNs, a modified Energy Efficient Clustering with Splitting and Merging (EECSM) for WSNs using Cluster-Head Handover Mechanism was implemented in this research. The modified model used information of the residual energy of sensor nodes to select backup Cluster Heads (CHs) while maintaining a suitable CH handover threshold to minimize energy consumption in the network. The backup CHs take over the responsibilities of the CHs once the handover threshold is reached. The modified model was validated in terms of network lifetime and residual energy ratio with EECSM using MATLAB R2013a. Average improvements of 7.5% and 50.7% were achieved for the network lifetime and residual energy ratio respectively which indicates a significant reduction in energy consumption of the network nodes. Keywords— Clustering, Energy-Efficiency, Handover, Lifetime, Wireless Sensor Network

A novel fault detection and recovery technique for cluster‐based underwater wireless sensor networks

SummaryThe performance of underwater wireless sensor network gets affected by the working of a cluster in the network. The cluster head (CH) or cluster member (CM) fails because of energy depletion or hardware errors that increase delay and message overhead of the network. To recover the affected cluster, a technique is required to identify the failed CH or CM. We propose a fault detection and recovery technique (FDRT) for a cluster‐based network in this paper. Primarily, while selecting the CH, a backup cluster head (BCH) is selected using fuzzy logic technique based on parameters such as node density, residual energy, load, distance to sink, and link quality. Then, failure of CH, BCH, and CM is detected. If fault is detected at CH, then the BCH will start performing the task of failed CH. Simultaneously, when BCH failed, any other CM will be elected as BCH. If any of the CM appears to be nonperforming, then CH will detect the communication failure and request BCH to transfer the data from the failed CM to CH. The comparison of proposed FDRT is performed with existing FDRTs EDETA, RCH, and SDMCGC on the basis of packet drop, end‐to‐end delay, energy consumption, and delivery ratio of data packets. By simulation results, it is shown that FDRT for cluster‐based underwater wireless sensor network results in quicker detection of failures and recovery of the network along with the reduction in energy consumption, thereby increasing the lifespan of the network.

A Unified Framework of Clustering Approach in Vehicular Ad Hoc Networks

Effective clustering algorithms are indispensable in order to solve the scalability problem in vehicular ad hoc networks. Although current existing clustering algorithms show increased cluster stability under some certain traffic scenarios, it is still hard to address which clustering metric performs the best. In this paper, we propose a unified framework of clustering approach (UFC), composed of three important parts: 1) neighbor sampling; 2) backoff-based cluster head selection; and 3) backup cluster head based cluster maintenance. Three mobility-based clustering metrics, including vehicle relative position, relative velocity, and link lifetime, are considered in our approach under different traffic scenarios. Furthermore, a detailed analysis of UFC with parameters optimization is presented. Extensive comparison results among UFC, lowest-ID, and VMaSC algorithms demonstrate that our clustering approach performs high cluster stability, especially under high dynamic traffic scenarios.

A delay and energy aware coordination mechanism for WSAN

SummaryIn this paper, a delay and energy aware coordination mechanism (DEACM) has been devised for wireless sensor–actor networks. In DEACM, a two‐level hierarchical K‐hop clustering mechanism is used to organize the sensors and actors for communication. In the first level, sensors form a K‐hop cluster using actors as cluster heads, and sink is made as the cluster head in the second level to form a cluster among actors. Sensor nodes, which are 1‐hop away from the actors, also called as relay nodes are elected as backup cluster head (BCH) based on the residual energy and node degree. BCH collects the data from sensors when an actor is away to perform actions in the affected area. The scheme is evaluated through exhaustive simulation in NS2 along with other existing schemes. Different parameters like average event waiting time, event reliability, and average energy dissipation are compared, varying the number of sensors, actors, and data transfer rate. In general, it is observed that the proposed DEACM outperforms other existing schemes. Copyright © 2016 John Wiley & Sons, Ltd.

Study on the Survival of Wireless Sensor Networks Based on Hierarchical Topology Control Method

Wireless sensor network (WSN) is composed of a large number of sensor nodes, it can real-time monitor and collect various information of monitored objects and passed to the user for analysis and use. This paper studies and designs a hierarchical WSN network management architecture and communication model, focus on the wireless sensor network topology control mechanism and based on the analysis of the existing hierarchical topology control algorithms. On the introduction of node load, the judgment method and the backup cluster head, design a load balancing topology control algorithm, the cluster management WSN, effectively balance the node energy consumption, reducing the number of network clusters, and the cluster head failure. Design of WSN network management protocol has a simple data type, operation type and the format of the message level HWSNMP, the improved MER protocol code to PDU code, the coded data length ratio after using the BER code length is reduced by 10%. Based on the hierarchical structure as the research object and energy-saving topology, a comprehensive analysis of the LEACH algorithm in layered structures (physical model, algorithm), and compares the advantages and disadvantages and the existing typical algorithms based on LEACH, the cluster algorithm based on node energy balance, balance the node energy consumption to meet the clusters in the theory history

A Novel Cluster Based Survivable Routing Protocol for Wireless Sensor Network

Sensor nodes easily suffer from failure, attack or capture because of the limited energy, storage, communication ability, complex and severe network environment when WSN is applied to emergency or battlefield environment. Thus, the basic scout mission is influenced. In this paper, a survivability route protocol named SRPC in cluster-based WSN is put forward. The protocol uses key negotiation and identity authentication mechanism to resist the attacks of malicious nodes; when the main cluster head is destroyed, monitoring data will be transmitted to the base station by the backup cluster head chain. The simulation results show that, SRPC protocol can not only resist the attacks of the enemy malicious nodes based on energy balance, but also assure reliable delivery of the packets after the cluster head is attacked or destroyed. As a result, the survivable ability of WSN in battlefield is improved.

The research and design of network routing protocol for field observation instruments

Field stations in cold and arid regions are all located in harsh natural environment that resulting in difficult to obtain information and severely restricting the study of Geosciences. To build up networks of field observation instruments in cold and arid regions, and promote the overall level of automation and real-time of field observation data transfer and data quality, a new routing protocol named clustering protocol of sub-area and backup cluster-head (LEACH-ABC) for observation instruments networking was proposed-based low energy adaptive clustering hierarchy (LEACH) protocol in this paper. The protocol mainly improved the various aspects of LEACH protocol such as clustering approach, cluster head (CH) selection, communication after clustering. We use the Matlab software for simulation environment to analyse the lifecycle, network throughput and energy consumption of LEACH and LEACH-ABC routing algorithm. Experiments results show that LEACH-ABC algorithm can effectively improve energy efficiency and extend life cycle of observation instruments network.

Fault Tolerant Event Detection in Distributed WSN via Pivotal Messaging

To mitigate the problem of loosing data, which can be very crucial to prevent environmental disasters, we proposed a fault tolerant event detection algorithm in a distributed environment. We considered the data loss because of node failure in sensor network due to some technical fault or energy issues. This algorithm works by backing up the data of cluster heads at some secondary nodes. If the secondary node that is backup cluster head notices the failure of primary or vital clusterhead, it will inform this to all non cluster-head nodes through pivotal messages. Subsequently, backup cluster-head will start working as vital cluster-head with new backup cluster-head. We have taken all parameters with the promising optimal use of energy. We have compared our results with the existing Dynamic Static Clustering Protocol (DSC) and fault toleran Dynamic Clustering Protocol (FT-DSC). We have compared results on the basis of various parameters like energy consumption over time, number of data packet transmission and network life time based on network remaining energy.

A Strategy for Fault Recovery of Wireless Sensor Network Based on v-SVC

Wireless Sensor Network (WSN) is applied widely and large-scale, which is usually used to collect physical parameters in wicked environment. It’s difficult for human to intervene when a WSN has faults. That is to say, fault diagnosis, isolation and recovery which exist automatically in WSN itself can solve this problem. In clustering algorithm, a Cluster Head (CH) is necessary for the whole network. When the CH can’t work, due to the energy, spatial barrier, strength of signal and so on, we have to replace the CH efficiently; when there is no suitable backup CH for one cluster, the cluster should be re-clustering which needs an effective strategy for the nodes joining other clusters. This paper proposed a strategy for the WSN in that situation. Based on the Support Vector Machine (SVM), we suggested a decision-function. In this function, by using support vector we can balance multiple causes of a fault except energy. This light calculation can make fault management of WSN more efficient, practical and easy-deployed to improve the lifetime and robustness of the whole network. The simulation experiment showed that, based on the v-SVC, the CH replacing and re-clustering algorithm are more efficient than the traditional solutions in way of optimizing the training sets and kernel function.