Efficient Cluster Head Selection Algorithm Research Articles

ETERS: A comprehensive energy aware trust-based efficient routing scheme for adversarial WSNs

Trust-based secure routing schemes are more effective than cryptographic routing protocols to convey energy-efficient data in WSNs since cryptographic protocols require high computation, more convergence time as well as storage space. The paper presents a well-organized trust estimation-based routing scheme (ETERS) that consists multi-trust (communication trust, energy trust, data trust) approach to alleviate several internal attacks like badmouthing, Sybil, selective forwarding, on–off,​ black hole, and gray-hole attacks for clustered WSN. The proposed multi-trust approach is used to analyze the credibility of sensitive monitored data. A novel and efficient cluster head selection algorithm (ECHSA) is employed to improve the performance of the cluster head (CH) selection process in clustered WSN. ECHSA allows the facility to elect a robust CH after a certain period to perform an equal load balance on all CHs. ETERS utilizes the Beta distribution-based trust function because recovery of trust values under attacks is faster in Beta distribution than Gaussian and Dirichlet distribution. ETERS also incorporates an irregular attenuation factor during the evaluation of communication trust to reflect the effect of various external factors such as natural calamity (earthquake), network congestion, etc., based on the trust values. However, a trust-based attack detection algorithm (TADA) assesses the reliability of SNs to detect internal attacks. TADA employs IDs, locations, and triple trust to detect internal attacks. Additionally, the proposed trust system employs an adjustable dynamic sliding length logical timing window to eradicate the limitations of existing trust models. Furthermore, a trust-based secure routing algorithm is incorporated to dynamically detect misbehavior in terms of packet forwarding for energy-efficient communication among sensor nodes. ETERS is compared with QEBSR, ATRP, ELPC, and SQEER to examine its performance. Experimental results on the MATLAB simulation platform exhibit excellent performance in terms of severity analysis, attack detection, and prevention to protect WSN, energy consumption under (i) normal (ii) attack scenario, latency (in sec.), packet delivery ratio (PDR), and throughput (in %) in the existence of spiteful nodes.

Energy Efficient Clustering Algorithm Based on Particle Swarm Optimization Technique for Wireless Sensor Networks

Maximizing network lifetime in wireless sensor networks is one of the critical issues, particularly for transmitting multimedia data. The wireless sensor network's lifetime is directly linked to energy conservation at each sensor node in the network. Clustering is the most energy-efficient technique for saving energy in sensor networks. The appropriate method for selecting the cluster head is still lagging. The sink node divides the deployment region into the optimal number of sub-regions depending upon its placement in the sensing region. The initial cluster heads are chosen randomly in each region, and this is not an energy-efficient method. The sink node adopts particle swarm optimization technique to select the cluster head in each region efficiently. The chosen cluster head in each region advertises its role to member nodes. Then, the cluster head node is chosen forms the new cluster. PSO optimization technique with the optimization parameters of clustering coefficient, the sensor node's remaining energy, and the distance from the sink and the head of the cluster to the members is adopted to select the cluster head sensor node. The cluster head spends most of its energy aggregating and transferring the data to the sink node. For unloading the cluster head responsibilities, the assistant cluster head and super cluster head are selected for the aggregation and transfer of data, respectively. The proposed energy efficient cluster head selection algorithm has improved the network lifetime by an average of 65 percent better than the existing clustering algorithms.

An Energy Efficient Stable Clustering Approach Using Fuzzy Type-2 Bat Flower Pollinator for Wireless Sensor Networks

Wireless sensor network (WSN) is a cost-effective networking solution for information updating in the coverage radius or in the sensing region. To record a real time event, large number of sensor nodes (SNs) need to be arranged systematically, such that information collection is possible for longer span of time. But, the hurdle faced by WSN is the limited resources of SNs. Hence, there is high demand to design and implement an energy efficient scheme to prolong the operational lifetime of WSN. Clustering based routing is the most suitable approach to support for load balancing, fault tolerance, and reliable communication to prolong performance parameters of WSN. These performance parameters are achieved at the cost of reduced lifetime of cluster head (CH). To overcome such limitations in clustering based hierarchical approach, efficient CH selection algorithm, and optimized routing algorithm are essential to design efficient solution for larger scale networks. In this paper, bat flower pollinator (BFP) using fuzzy type-2 based clustering approach is proposed to enhance the network lifespan. Simulation outcomes show that the proposed algorithm outperforms competitive clustering algorithms in the context of energy consumption, stability period and system lifetime.

DistBlockBuilding: A Distributed Blockchain-Based SDN-IoT Network for Smart Building Management

Security, privacy, and transparency aspects of the Internet of Things (IoT) sensors have recently raised significant concerns among the public and policymakers. The distributed ledger and Blockchain technologies had brought solutions through transparently bridging two or multiple untrusted parties. In this research, a novel architecture for a smart building system, including a control system and automatic approaches, is proposed. An efficient cluster head selection algorithm is proposed to select the desired cluster head with the consideration of low energy consumption and fast head selection. An enhanced combination of IoT forwarding devices and Software-Defined Networking (SDN) technology is further advanced. Furthermore, the proposed “DistBlockBuilding” architecture is implemented for managing a safe and secure data transfer from one surface to another surface. Besides, Blockchain technology is performed for transferring data within the smart building. Finally, the performance of IoT-SDN based secured networks is evaluated.

An energy efficient stable clustering approach using fuzzy extended grey wolf optimization algorithm for WSNs

Wireless sensor network (WSN) is a cost-effective networking solution for information updating in the coverage radius or in the sensing region. To record a real-time event, a large number of sensor nodes (SNs) need to be arranged systematically, such that information collection is possible for a longer span of time. But, the hurdle faced by WSN is the limited resources of SNs. Hence, there is a high demand to design and implement an energy-efficient scheme to prolong the performance parameters of WSN. Clustering-based routing is the most suitable approach to support for load balancing, fault tolerance, and reliable communication to prolong performance parameters of WSN. These performance parameters are achieved at the cost of reduced lifetime of cluster head (CH). To overcome such limitations in clustering based hierarchical approach, efficient CH selection algorithm, and optimized routing algorithm are essential to design efficient solution for larger scale networks. In this paper, fuzzy extended grey wolf optimization algorithm based threshold-sensitive energy-efficient clustering protocol is proposed to prolong the stability period of the network. Analysis and simulation results show that the proposed algorithm significantly outperforms competitive clustering algorithms in the context of energy consumption, stability period and system lifetime.

An energy-efficient stable clustering approach using fuzzy-enhanced flower pollination algorithm for WSNs

Due to advancement in the technology and need for machine-to-machine connectivity, wireless sensor network (WSN) overplays the role compared to other wireless networks. In this context, different applications based on WSNs need to be executed efficiently in terms of energy and communication. To achieve this, there is a need to collaborate among various devices at various levels. This can be achieved by the grouping of these devices, that is, through the clustering. Clustering-based routing is the most suitable approach to support for load balancing, fault tolerance and reliable communication to prolong performance parameters of WSN. These performance parameters are achieved at the cost of reduced lifetime of cluster head (CH). To overcome such limitations in clustering-based hierarchical approach, efficient CH selection algorithm and optimized routing algorithm are essential to design efficient solution for larger scale networks. In this paper, fuzzy-enhanced flower pollination algorithm-based threshold-sensitive energy-efficient clustering protocol is proposed to prolong the stability period of the network. Analysis and simulation results show that the proposed algorithm significantly outperforms competitive clustering algorithms in the context of energy consumption, stability period and system lifetime.

Energy Efficient Data Collection Algorithm for Mobile Wireless Sensor Network

Wireless sensor network (WSN) with mobile sink serves a lot of industrial and agricultural monitoring applications. The data collection with WSN has been popularly known in many applications such as smart environments, health monitoring, habitat monitoring, surveillance and tracking systems. The mobile sink moves to the corresponding cluster and gathers the data and moves to the next cluster head (CH) for the same. The data loss and waiting time of the data in the CH results from energy consumption and work repetition. This article provides a novel energy efficient data collection scheme. The data collection is done by the sink in a Polling based M/G/1 server model. The cluster member (CM) sends the data in N threshold model to the CH. The CH gathers the data from the CM and reports to sink once it arrives nearby. The energy efficient cluster head selection algorithm is evaluated with the proposed model and the waiting time is analyzed. The data arrival from the CM to the CH is considered as Poisson in nature. The sink mobility with respect to polling system is analyzed. The sink data collection is done through dynamic polling mechanism based on the CH arrival rate. The proposed algorithm outperforms the modified low energy adaptive clustering hierarchy and energy-aware multi-hop routing protocol (M-GEAR) protocols in terms of lifetime, waiting time and throughput. The proposed algorithm provides high resistance to energy hole and HOTSPOT problem.

Architectural analysis for lifetime maximization and energy efficiency in hybridized WSN model

It is well known that WSN is one of the leading techniques in granting pervasive computing for various applications regarding health sector and communication sector. However, the raising of issues in WSN is still a burden cause because of certain renowned terms like energy consumption and network lifetime extension. Clustering is a major contribution in any network and moreover Cluster Head selection is also a vital role since it is additively responsible in sending data to the base station, which means that Cluster Head directly makes its communication with base station. Day by day, the researches in cluster head selection get increased, but the requirements are not yet fulfilled. This paper proposes a energy efficient cluster head selection algorithm for maximizing the WSN lifetime. This paper develops a hybrid optimization process termed Group Search Ant Lion with Levy Flight (GAL-LF) for selecting the Cluster head in WSN. The proposed model is compared to the conventional models such as Genetic Algorithm (GA), Particle Swarm Optimization (PSO), Artificial Bee Colony (ABC), Group Search Optimization (GSO), Ant Lion Optimization (ALO) and Cuckoo Search (CS). The outcome of the simulation result shows the superiority of the proposed model by prolonging the lifetime of the network.

EE-MRP: Energy-Efficient Multistage Routing Protocol for Wireless Sensor Networks

Wireless sensor networks (WSNs) have captivated substantial attention from both industrial and academic research in the last few years. The major factor behind the research efforts in that field is their vast range of applications which include surveillance systems, military operations, health care, environment event monitoring, and human safety. However, sensor nodes are low potential and energy constrained devices; therefore, energy-efficient routing protocol is the foremost concern. In this paper, an energy-efficient routing protocol for wireless sensor networks is proposed. Our protocol consists of a routing algorithm for the transmission of data, cluster head selection algorithm, and a scheme for the formation of clusters. On the basis of energy analysis of the existing routing protocols, a multistage data transmission mechanism is proposed. An efficient cluster head selection algorithm is adopted and unnecessary frequency of reclustering is exterminated. Static clustering is used for efficient selection of cluster heads. The performance and energy efficiency of our proposed routing protocol are assessed by the comparison of the existing routing protocols on a simulation platform. On the basis of simulation results, it is observed that our proposed routing protocol (EE-MRP) has performed well in terms of overall network lifetime, throughput, and energy efficiency.

A particle swarm optimization based energy efficient cluster head selection algorithm for wireless sensor networks

Clustering has been proven to be one of the most efficient techniques for saving energy of wireless sensor networks (WSNs). However, in a hierarchical cluster based WSN, cluster heads (CHs) consume more energy due to extra overload for receiving and aggregating the data from their member sensor nodes and transmitting the aggregated data to the base station. Therefore, the proper selection of CHs plays vital role to conserve the energy of sensor nodes for prolonging the lifetime of WSNs. In this paper, we propose an energy efficient cluster head selection algorithm which is based on particle swarm optimization (PSO) called PSO-ECHS. The algorithm is developed with an efficient scheme of particle encoding and fitness function. For the energy efficiency of the proposed PSO approach, we consider various parameters such as intra-cluster distance, sink distance and residual energy of sensor nodes. We also present cluster formation in which non-cluster head sensor nodes join their CHs based on derived weight function. The algorithm is tested extensively on various scenarios of WSNs, varying number of sensor nodes and the CHs. The results are compared with some existing algorithms to demonstrate the superiority of the proposed algorithm.

An Energy-Efficient Cluster-Head Selection Algorithm for Wireless Sensor Networks

Wireless sensor netwo rk has a wide range of applications in vario us fields, extending rapidly its area due to the development of technologies both in sensor and IT. However, technology of efficient battery energy utilization cannot catch up with the rapidly developing technology in releva nt fields. Therefore, energy efficiency becomes the critical factor in wireless sensor networks that requires more attention. Clustering technology is emerging as a promising method to solve efficient energy utilization problem and various cluster-head selection techniques are now under research. This paper suggests an energy efficient cluster-head selection algorithm considering the optimal number of cluster-heads and transmission energy. Our proposed scheme selects cluster-head by computing communi cation cost in probability by adopting distance between base station and n odes to assure an o ptimal number of clusters for a given wireless sensor network. With rapidly increasing amount of information produced by the Internet users along with the constant information technology development, clustering methods are gaining more importance. Clustering technology classifies a given data into significant subgroups, minimizes energy consumption, and maximizes the life span of a network by using limited resources efficiently in various fields such as data analysis, visualization, compression, and data processing. The important goal of clustering technology research is t o extend the l ife span o f a network by distributing energy consumption fairly among all nodes. Among many clustering approaches to extend the life span of a network there are two typical methods, now under research, a cluster-head selection and optimization of the number of cluster-heads or clusters. The typical head selection methods include LEACH-C (Low-Energy Adaptive Clustering Hierarchy-Centralized) (1), EACHS (Energy Adaptive Cluster-head Selection) (2), HEED (Hybrid Energy Efficient Distributed Clustering) (3). But, LEACH- C requires a big initial cost for hardware and EACHS bears a trans mission cost to acknowledge a quantity of energy, while HEED has no guarantee on the number of cluster-heads. This research suggests the energy efficient cluster-head selection technique (EECHS), an efficient header selection algorithm that takes into account the optimal number of cluster-heads and the transmission energy. E ECHS adopts distance in formation between a n ode and base station (BS) as communication cost to ensure the optimal number of clusters. A proposed cluster-head selection method operates by computing communication cost as internal information probability to save overall communication or transmission energy. The rest of this paper consists of following chapters. Chapter 2 d escribes related cluster header selection techniques and examines HEED, the basis of selection method for EECHS, in more detail. Chapter 3 explains the cluster-head selection technique of EECHS, Chapter 4 identifies performance of EECHS whose technique suggested through analysis on the test result using a simulator, and Chapter 5 deduces a conclusion.

Efficient Cluster Head Selection Algorithm for MANET

In mobile ad hoc network (MANET) cluster head selection is considered a gigantic challenge. In wireless sensor network LEACH protocol can be used to select cluster head on the bases of energy, but it is still a dispute in mobil ad hoc networks and especially when nodes are itinerant. In this paper we proposed an efficient cluster head selection algorithm (ECHSA), for selection of the cluster head efficiently in Mobile ad hoc networks. We evaluate our proposed algorithm through simulation in OMNet++ as well as on test bed; we experience the result according to our assumption. For further evaluation we also compare our proposed protocol with several other protocols like LEACH-C and consequences show perfection.