Energy Efficient Communication Protocols for Wireless Sensor Networks

The popularity of Wireless Sensor Networks have increased tremendously due to the vast potential of the sensor networks to connect the physical world with the virtual world. Since these devices rely on battery power and may be placed in hostile environments replacing them becomes a tedious task. Thus, improving the energy of these networks becomes important.The thesis provides methods for clustering and cluster head selection to WSN to improve energy efficiency. It presents a comparison between the different methods on the basis of the network lifetime . It proposes a modified approach for cluster head selection with good performance and reduced computational complexity .In addition it also proposes BFO as an algorithm for clustering of WSN which would result improved performance with faster convergence

EFFICIENT ROUTING PROTOCOL ALGORITHM FOR WIRELESS SENSOR NETWORKS

Recently, different applications of wireless sensor networks (WSNs) in the industry fields using different data transfer protocols has been developed. As the energy of sensor nodes is limited, prolonging network lifetime in WSNs considered a significant occurrence. To develop network permanence, researchers had considered energy consuming in routing protocols of WSNs by using modified Low Energy Adaptive Clustering Hierarchy. This article presents a developed effective transfer protocols for autonomic WSNs. An efficient routing scheme for wireless sensor network regarded as significant components of electronic devices is proposed. An optimal election probability of a node to be cluster head has being presented. In addition, this article uses a Voronoi diagram, which decomposes the nodes into zone around each node. This diagram used in management architecture for WSNs

EEIT2-F: energy-efficient aware IT2-fuzzy based clustering protocol in wireless sensor networks

Improving the network lifetime is still a vital challenge because most wireless sensor networks (WSNs) run in an unreached environment, and offer almost impossible human access and tracking. Clustering is one of the most effective methods for ensuring that the relevant device process takes place to improve network scalability, decrease energy consumption and maintain an extended network lifetime. Many research have been developed on the numerous effective clustering algorithms to address this problem.  Such algorithms almost dominate on the cluster head (CH) selection and cluster formation; using the intelligent type1 fuzzy-logic (T1-FL) scheme. In this paper, we suggest an interval type2 FL (IT2-FL) methodology that assumes uncertain levels of a decision to be more efficient than the T1-FL model. It is the so-called energy-efficient interval type2 fuzzy (EEIT2-F) low energy adaptive clustering hierarchical (LEACH) protocol. The IT2-FL system depends on three inputs of the residual energy of each node, the node distance from the base station (sink node), and the centrality of each node. Accordingly, the simulation results show that the suggested clustering protocol outperforms the other existing proposals in terms of energy consumption and network lifetime

A Brief Survey on Cluster based Energy Efficient Routing Protocols in IoT based Wireless Sensor Networks

The wireless sensor network (WSN) consists of a large number of randomly distributed nodes capable of detecting environmental data, converting it into a suitable format, and transmitting it to the base station. The most essential issue in WSNs is energy consumption, which is mostly dependent on the energy-efficient clustering and data transfer phases. We compared a variety of algorithms for clustering that balance the number of clusters. The cluster head selection protocol is arbitrary and incorporates energy-conscious considerations. In this survey, we compared different types of energy-efficient clustering-based protocols to determine which one is effective for lowering energy consumption, latency and extending the lifetime of wireless sensor networks (WSN) under various scenarios

Survey on Various Aspects of Clustering in Wireless Sensor Networks Employing Classical, Optimization, and Machine Learning Techniques

A wide range of academic scholars, engineers, scientific and technology communities are interested in energy utilization of Wireless Sensor Networks (WSNs). Their extensive research is going on in areas like scalability, coverage, energy efficiency, data communication, connection, load balancing, security, reliability and network lifespan. Individual researchers are searching for affordable methods to enhance the solutions to existing problems that show unique techniques, protocols, concepts, and algorithms in the wanted domain. Review studies typically offer complete, simple access or a solution to these problems. Taking into account this motivating factor and the effect of clustering on the decline of energy, this article focuses on clustering techniques using various wireless sensor networks aspects. The important contribution of this paper is to give a succinct overview of clustering

Energy Consumption Minimization in WSN using BFO

The popularity of Wireless Sensor Networks (WSN) have increased rapidly and tremendously due to the vast potential of the sensor networks to connect the physical world with the virtual world. Since sensor devices rely on battery power and node energy and may be placed in hostile environments, so replacing them becomes a difficult task. Thus, improving the energy of these networks i.e. network lifetime becomes important. The thesis provides methods for clustering and cluster head selection to WSN to improve energy efficiency using fuzzy logic controller. It presents a comparison between the different methods on the basis of the network lifetime. It compares existing ABC optimization method with BFO algorithm for different size of networks and different scenario. It provides cluster head selection method with good performance and reduced computational complexity. In addition it also proposes BFO as an algorithm for clustering of WSN which would result in improved performance with faster convergence