Master Cluster Head Research Articles

Energy Efficient Unequal Fault Tolerance Clustering Approach

For achieving Energy-Efficiency in wireless sensor networks (WSNs), different schemes have been proposed which focuses only on reducing the energy consumption. A shortest path determines for the Base Station (BS), but fault tolerance and energy balancing gives equal importance for improving the network lifetime. For saving energy in WSNs, clustering is considered as one of the effective methods for Wireless Sensor Networks. Because of the excessive overload, more energy consumed by cluster heads (CHs) in a cluster based WSN to receive and aggregate the information from member sensor nodes and it leads to failure. For increasing the WSNs’ lifetime, the CHs selection has played a key role in energy consumption for sensor nodes. An Energy Efficient Unequal Fault Tolerant Clustering Approach (EEUFTC) is proposed for reducing the energy utilization through the intelligent methods like Particle Swarm Optimization (PSO). In this approach, an optimal Master Cluster Head (MCH)-Master data Aggregator (MDA), selection method is proposed which uses the fitness values and they evaluate based on the PSO for two optimal nodes in each cluster to act as Master Data Aggregator (MDA), and Master Cluster Head. The data from the cluster members collected by the chosen MCH exclusively and the MDA is used for collected data reception from MCH transmits to the BS. Thus, the MCH overhead reduces. During the heavy communication of data, overhead controls using the scheduling of Energy-Efficient Time Division Multiple Access(EE-TDMA). To describe the proposed method superiority based on various performance metrics, simulation and results are compared to the existing methods.

Advanced Energy Efficient Master/Slave Algorithm in Wireless Sensor Networks

The nodes in WSNs are densely deployed and lots of redundancy exists during the data gathering and sending perceived data straightforwardly to the base station, which leading to consumption of energy in nodes. Existing Clustering algorithms in WSN selects just one group head in the each cluster, where it devours more energy at Cluster head(CH) quickly and which condenses lifetime of the network incredibly. The paper proposes the Advanced and Energy Efficient Master/Slave algorithm to solve this problem. The algorithm reduces the energy consumption of each node by minimizing the direct communication of the nodes with the Base station or CHs by changing the hierarchy in WSN. The moto of the algorithm is to select one master Cluster Head and remaining slave CHs. The algorithm will select Master Cluster Head based on more residual energy, distance, and low packet drop; the remaining become Slave Cluster Heads. The simulation results prove that the Advanced and Energy Efficient Master/Slave algorithm improves throughput and packet delivery ratio(PDR) by decreasing the energy consumption.

A Novel Protein Sequence Alignment-Based Patch Similarity Estimation for Two-Level Data Aggregation in WMSNs

In the digital era, massive growth in Wireless Multimedia Sensor Networks (WMSNs) can be used in various applications such as environmental and industrial monitoring, disaster management. WMSN comprises many low-power camera sensor nodes that may have overlapping field of view for enhancing the accuracy of the monitored environment. Hence, huge volume of redundant data which make it inefficient in terms of network lifetime and energy. It needs to establish a tremendous suitable approach to communicate images over WMSN that handle critical factors such as maximize QoS and minimize energy consumption efficiently. There is a need for energy efficient image processing and transmission over WMSN. So aggregating multi-view images using cluster based distributed routing approaches with QoS constraints have received significant research interest. The Distributed Two-Layer Cluster framework is used in this paper for transmitting aggregated data by local cluster head (LCH) and master cluster head from various clusters to base station in multi-hop basis. This aggregated data are generated from multi-view images captured by correlated camera sensor nodes of local cluster (LC) and master cluster (MC) using first level data aggregation (FLDA) and second level data aggregation (SLDA) algorithms respectively. In FLDA, less complex novel protein sequence alignment based patch matching algorithm is used to reduce inter-view redundancy of multi-view images. Then, this first level aggregated data transmission of LC is reduced using Varying Bit Encoding based on Arithmetic Operations algorithm by combining multi-view images together and achieves its compression ratio of about 2.54. In SLDA, Higher-Order SVD is used to aggregate FLDA data from LCH for reducing MC transmission rate and achieves its compression ratio of about 9.1. The comparison of network performance results with existing cluster based routing algorithms exhibit the improvement of the proposed system in terms of performance metrices such as energy consumption, packet delivery ratio, end-to-end delay and network lifetime. This results show that PSA based TLDA performs better and average energy consumption of processing and transmitting multi-view images per round is considerably minimized to 0.320 J. Thus the proposed system prolongs the network lifetime by 35%, 6% when compared with DFRP and GRAD-CORR based TLDA in terms of number of rounds.

Secure data aggregation scheme based on node self-adaptive monitoring for wireless sensor networks

Malicious nodes in wireless sensor networks will cause a great deviation in aggregation data. Such malicious nodes assigned with the aggregation task can forge and send a mass of false data. As a result, the network may consume a mass of resources and users may make wrong decisions. Concerning this problem, a secure data aggregation scheme based on node self-adaptive monitoring is proposed in this paper. According to the scheme, a master cluster head node and a vice cluster head node are selected, wherein, the vice cluster head node verifies the aggregation data produced by the master cluster head, the intra-cluster monitoring nodes monitor the behaviour of the master and vice cluster head nodes in a self-adaptive manner. Besides, the confidence interval theory is introduced to evaluate and calculate the credibility of nodes and highly credible node data are selected for data aggregation. Both theoretical analysis and experimental results show that our scheme can not only effectively recognise and shield malicious nodes, enhance accuracy of data aggregation but also prevent malicious master cluster head nodes from forging and sending a lot of false data.

Secure data aggregation scheme based on node self-adaptive monitoring for wireless sensor networks

Malicious nodes in wireless sensor networks will cause a great deviation in aggregation data. Such malicious nodes assigned with the aggregation task can forge and send a mass of false data. As a result, the network may consume a mass of resources and users may make wrong decisions. Concerning this problem, a secure data aggregation scheme based on node self-adaptive monitoring is proposed in this paper. According to the scheme, a master cluster head node and a vice cluster head node are selected, wherein, the vice cluster head node verifies the aggregation data produced by the master cluster head, the intra-cluster monitoring nodes monitor the behaviour of the master and vice cluster head nodes in a self-adaptive manner. Besides, the confidence interval theory is introduced to evaluate and calculate the credibility of nodes and highly credible node data are selected for data aggregation. Both theoretical analysis and experimental results show that our scheme can not only effectively recognise and shield malicious nodes, enhance accuracy of data aggregation but also prevent malicious master cluster head nodes from forging and sending a lot of false data.

Energy-Efficient Heterogeneous Optimization Routing Protocol for Wireless Sensor Network

A wide range of applications include in Wireless Sensor Networks (WSNs), and it is being used extensively in data collection specifically to process the mission-critical tasks. The implementation of routing protocols of energy-efficient (EE) is one of the significant challenging jobs of Sensor Networks (MC-SSN) and Mission Critical Sensors. In hierarchical routing protocols, higher EE can reach when compared to the flat routing protocols. The network’s scheduling process doesn’t support enhanced balanced Energy-efficient network-integrated super-heterogeneous (E-BEENISH), which discusses earlier. An Energy. Energy efficient Time scheduling based particle swarm optimization unequal fault tolerance clustering protocol (EE-TDMA-PSO-UFC) is proposed in this paper. Based on the distance parameter, an efficient cluster head (CH) is selected in this protocol. Owing to the unexpected failure of MCH (Master Cluster Head), an additional “CH” is chosen that is termed as Surrogate cluster head (SCH) for the restoration of network’s connectivity in the protocol of PSO-UFC. Based on TDMA (Time Division Multiple Access) protocols, the consumption of Energy. Energy is reduced with the allocation of timeslots during transmission of data. Using the technique of EE-TDMA-PSOUFC, the network’s lifespan improves than CEEC and E-BEENISH protocols according to the assessment of simulation results.

Two-level data aggregation for WMSNs employing a novel VBEAO and HOSVD

Recent growth in camera/video technology, alongside the development of visual data aggregation and transmission algorithms, has facilitated a new kind of WSN called the Wireless Multimedia Sensor Network (WMSN). In traditional WSNs, environmental parameters such as temperature, humidity and pressure are analyzed with scalar sensor nodes, which compress redundant data using simple data aggregation functions to reduce transmission energy before sending the data to the receiver. However, this mechanism may not be suitable for image processing and image transmission in WMSNs. This is because camera sensor nodes handle vast quanta of redundant data, particularly if the Field of View (FoV) of camera sensor nodes overlaps with each other to increase monitoring accuracy. In a traditional WSN cluster-based protocol, much of the process relies on a cluster head (CH), which may run out of energy to become a dead node, eventually leading to network failure. To handle this scenario, a Distributed Two-Layer Cluster (DTLC) framework is proposed to minimize the energy consumption of individual nodes in WMSN by sharing the processes performed by a CH. This framework also extends the overall network lifetime by distributing the computational load among camera sensor nodes.A Two-Level Image Aggregation (TLIA) algorithm is proposed to remove the inter-view correlation in the DTLC-based node deployment. The local cluster head (LCH) performs first-level image aggregation (FLIA), which eliminates the correlated information of multi-view images from local cluster members (LCM) by combining multi-view images together, using a novel Varying Bit Encoding based on Arithmetic Operations (VBEAO) with bit reduction. The FLIA results in a compression ratio of 2.4. The master cluster head (MCH) performs second-level image aggregation (SLIA) to further eliminate redundant data from LCHs using a Higher-Order SVD (HOSVD), followed by logarithmic quantization on both the decomposed tensor core and factor matrices. SLIA results in the next level of compression, with a ratio of about 8.9. A comparison of the performance with existing approaches demonstrates the superiority of WMSNs, with improvements in terms of network lifetime and energy consumption.

MCA-V2I: A Multi-hop Clustering Approach over Vehicle-to-Internet communication for improving VANETs performances

The Internet of Vehicles is a new Intelligent Transportation System paradigm and a promising solution to improve conventional Vehicular Ad-hoc NETworks (VANETs) performances. It has received a great deal of attention in recent years, from many researchers. For this reason, several control mechanisms have been proposed for these networks to confront their challenges, such as dynamic topology and the scalability problem due to the high mobility of vehicles and the high number of connected vehicles, respectively. As an important mechanism used in a VANET, clustering has significantly improved the performance in numerous applications. In this regard, the present work proposes a new Multi-hop Clustering Approach over Vehicle-to-Internet called MCA-V2I to improve VANETs’ performance. MCA-V2I is based on the reasonable assumption that a vehicle can connect to the Internet via a special infrastructure called a Road Side Unit Gateway. Once connected to the Internet, each vehicle can obtain and share the necessary information about its Multi-hop neighbors to perform the clustering process. This latter is performed using a Breadth-first search (BFS) algorithm for traversing a graph based on a Mobility Rate that is calculated according to mobility metrics. MCA-V2I strengthens clusters’ stability through the selection of a Slave Cluster Head in addition to the Master Cluster Head. We evaluate the performances of the proposed scheme using network simulator NS-2 and the VanetMobiSim integrated environment.

Particle Swarm Optimization-Based Unequal and Fault Tolerant Clustering Protocol for Wireless Sensor Networks

Clustering is one of most efficient energy saving techniques for maximizing network lifetime in wireless sensor networks. In the multi-hop approach, cluster heads (CHs) close to the base station deplete their energy very quickly due to high inter-cluster relay traffic load, causing the hot spot problem. Thus, a clustering protocol is required to be energy efficient as well as fault tolerant. This paper presents a particle swarm optimization (PSO)-based unequal and fault tolerant clustering protocol referred as PSO-UFC. The proposed protocol addresses imbalanced clustering and fault tolerance issues in the existing energy-balanced unequal clustering (EBUC) protocol for the long-run operation of the network. To solve the imbalanced clustering problem, the PSO-UFC protocol utilizes unequal clustering mechanism to balance intra-cluster and inter-cluster energy consumption between the Master CHs (MCHs). Also, in PSO-UFC protocol the network connectivity is restored by electing an extra CH called Surrogate CH due to sudden failure of MCH. The obtained simulation results demonstrate that the PSO-UFC protocol prolongs the network lifetime against EBUC, PSO-C, and LEACH-C protocols.

Hybrid energy-efficient APTEEN protocol based on ant colony algorithm in wireless sensor network

Due to the limited energy of the sensor nodes, the unreasonable clustering routing algorithm will cause node premature death and low utilization of energy efficiency in wireless sensor network (WSN). In Adaptive Threshold-sensitive Energy Efficient Network (APTEEN), the assignments of the cluster head (CH) are much heavier than other nodes. The CH unbalanced energy dissipation between nodes that make them die prematurely. Ant colony algorithm can avoid this problem, so this paper presents a double cluster heads Adaptive Threshold-sensitive Energy Efficient Network based on ant colony (ADCAPTEEN). ADCAPTEEN optimizes the cluster head election method compared with APTEEN. It suggests that one master cluster head (MCH) and one vice cluster head (VCH) will be selected in each cluster. The double cluster heads (DCH) can co-work on data collection, fusion, transition, etc. To make routes more stable and energy efficient, this paper proposes a Multiple Adaptive Threshold-sensitive Energy Efficient Network based on Ant-colony (AMAPTEEN). It is the optimization of ADCAPTEEN. And CH selects intermediate node (IM_node) multiple times with ant colony algorithm per round in each cluster, and this way forms multiple route transmission data. Simulation in OPNET proves that compared with APTEEN, ADCAPTEEN reduces energy dissipation, improves node survival rate, and extends network life cycle. AMAPTEEN delays the time of node death, balances energy consumption, and extends network lifetime further operating in the same settings compared with ADCAPTEEN. The proposed two algorithms have good scalability, and they are suitable for large-scale network.

A fuzzy three-level clustering method for lifetime improvement of wireless sensor networks

One of the most important issues in wireless sensor networks (WSNs) is to reduce energy consumption and increase the network lifetime. Proper election of cluster head is one of the approaches to reduce energy consumption in the network. Among existing methods, low-energy adaptive cluster hierarchy (LEACH) is the most prevalent routing algorithm, in which the cluster head is elected based on a given threshold. In LEACH algorithm, only the cluster heads are allowed to send information to the base station (BS). In this paper, a novel routing protocol based on super cluster head election using fuzzy logic in three levels (SCHFTL) is proposed, in which a super cluster head is elected among the cluster heads. The super cluster head election is performed based on a fuzzy description in three levels using Mamdani inference engine. Effectiveness of the proposed SCHFTL routing protocol is verified through MATLAB simulations in terms of death, time of the first node and network lifetime compared with LEACH, cluster head election mechanism using fuzzy logic (CHEF) and fuzzy-based master cluster head election leach (F-MCHEL) protocols.

An Effective Approach for Minimizing Energy Consumption in WSN

Wireless Sensor Networks finds it difficult to transfer equal amount of data through all nodes, and in minimizing the level of energy consumption during transmission of data. This paper is an improvement to LEACH protocol; suggested to increase the overall lifespan of the network. Formation of clusters is based upon distance of nodes from base station, number of neighbor nodes and distance among neighbor nodes. The cluster heads are selected on the basis of nodes holding maximum energy. We have proposed the concept of selecting the master cluster head from the elected cluster heads based on the strength of energy that CH nodes have and the distance from the Base station. Further, using this concept, the fused data from master CH are forwarded to the sink node. Proposed work reduces the frequency and length of data exchanged between the CHs and the Base station. Simulation results revealed that the proposed method reduces the consumption of energy by 46.85 percent and increases the lifespan of the network efficiently. The parameters for comparison are First Node Dead and Half Node Alive.

An Energy Distance Aware Clustering Protocol with Dual Cluster Heads Using Niching Particle Swarm Optimization for Wireless Sensor Networks

Energy efficient utilization is an important criteria and factor that affects the design of wireless sensor networks (WSNs). In this literature, we propose an energy distance aware clustering protocol with Dual Cluster Heads using Niching Particle Swarm Optimization (DCH-NPSO). The protocol selects two cluster heads in each cluster, the Master Cluster Head (MCH) and the Slave Cluster Head (SCH), and the selection needs to consider the network state information carefully and deliberately. Simulation results show that the protocol we proposed can balance the energy dissipation and extend the network lifetime effectively.

Clustering Routing Algorithm in Wireless Sensor Networks Based on artificial Bee Colony and Assistant Cluster Heads

The solution presented in this article is a new clustering routing algorithm based on artificial bee colony and assistant cluster-head. This algorithm is a selection process of optimizing master cluster-head and assistant cluster-head by introducing assistant cluster-head in the cluster and artificial bee colony algorithm. Simulation experiment shows that the clustering routing algorithm based on artificial bee colony and assistant cluster-head can effectively improve energy utilization ratio, balance network load and extend network lifetime. Keyword: wireless sensor networks; route; artificial bee colony; assistant cluster-head; lifetime

Improvement In LEACH Protocol By Electing Master Cluster Heads To Enhance The Network Lifetime In WSN

In wireless sensor networks, sensor nodes play the most prominent role. These sensor nodes are mainly un-chargeable, so it raises an issue regarding lifetime of the network. Mainly sensor nodes collect data and transmit it to the Base Station. So, most of the energy is consumed in the communication process between sensor nodes and the Base Station. In this paper, we present an improvement on LEACH protocol to enhance the network lifetime. Our goal is to reduce the transmissions between cluster heads and the sink node. We will choose optimum number of Master Cluster Heads from variation cluster heads present in the network. The simulation results show that our proposed algorithm enhances the network lifetime as compare to the LEACH protocol.

Clustering and power management for virtual MIMO communications in wireless sensor networks

Multi-input multi-output (MIMO) is a well-established technique for increasing the link throughput, extending the transmission range, and/or reducing energy consumption. In the context of wireless sensor networks (WSNs), even if each node is equipped with a single antenna, it is possible to group several nodes to form a virtual antenna array, which can act as the transmitting or receiving end of a virtual MIMO (VMIMO) link. In this paper, we propose energy-efficient clustering and power management schemes for virtual MIMO operation in a multi-hop WSN. Our schemes are integrated into a comprehensive protocol, called cooperative MIMO (CMIMO), which involves clustering the WSN into several clusters, each managed by up to two cluster heads (CHs); a master CH (MCH) and a slave CH (SCH). The MCH and SCH collect data from their cluster members during the intra-cluster communications phase and communicate these data to neighboring MCHs/SCHs via an inter-cluster VMIMO link. CMIMO achieves energy efficiency by proper selection of the MCHs and SCHs, adaptation of the antenna elements and powers in the inter-cluster communications phase, and using a cross-layer MIMO-aware route selection algorithm for multi-hop operation. We formally establish the conditions on the transmission powers of CHs and non-CHs that ensure the connectivity of the inter-cluster topology. Simulations are used to study the performance of CMIMO. The simulation results indicate that our proposed protocol achieves significant reduction in energy consumption and longer network life time, compared with non-adaptive clustered WSNs.

Clustering Algorithm for Heterogeneous Sensor Network Based on Energy Matrix

Aiming at the energy heterogeneous wireless sensor network, this paper proposes This paper proposes the remaining energy prediction model based on energy matrix and a novel clustering algorithm .The Kalman filtering algorithm was introduced into the model, in which a node , according to energy prediction mechanism , independently makes it s decision to compete for becoming a master cluster head in order to improve energy efficiency. Simulation results show that the algorithm can balance the network energy consumption, improve the utilization efficiency of node energy and prolong the network lifetime.