Survey of Energy Efficient Clustering Mechanisms in Wireless Sensor Networks

Advancements in wireless communications and Micro-Electro-Mechanical structures have enabled the improvement of wireless sensor networks (WSN), which in flip have fostered the emergence of a plethora of programs in diverse fields together with agriculture, healthcare supervision, and transportation systems. However, because of the strength dilemma of battery-powered sensors, these packages nonetheless face a major energy issue that save you their giant adoption. In this thesis, we contributed to conquer this challenge via several contributions. In this paper we've got surveyed the various techniques to power efficient strategies in wi-fi sensor networks

Improved energy aware cluster based data routing scheme for WSN

Wireless sensor network (WSN) consists of several tiny devices that are dispersed randomly for gathering network field. Clustering mechanism divides the WSN into different sub-regions called clusters. Individual cluster is consisting of cluster head (CH) and member nodes. The main research challenges behind clustering mechanism are to optimize network overheads with efficient data delivery. Sensor nodes are operated by batteries and practically it is not feasible to replace them during sensing the environment so energy should be effectively utilized among sensors for improving overall network performance. This research paper presents an improved energy aware cluster based data routing (i-ECBR) scheme, by dividing the network regions into uniform sized square partitions and localized CH election mechanism. In addition, consistent end-to-end data routing is performed for improving data dissemination. Simulation results illustrate that our proposed scheme outperforms than existing work in terms of different performance metrics

Improved Energy Aware Cluster based Data Routing Scheme for WSN

Wireless sensor network (WSN) consists of several tiny devices that are dispersed randomly for gathering network field. Clustering mechanism divides the WSN into different sub-regions called clusters. Individual cluster is consisting of cluster head (CH) and member nodes. The main research challenges behind clustering mechanism are to optimize network overheads with efficient data delivery. Sensor nodes are operated by batteries and practically it is not feasible to replace them during sensing the environment so energy should be effectively utilized among sensors for improving overall network performance. This research paper presents an improved energy aware cluster based data routing (i-ECBR) scheme, by dividing the network regions into uniform sized square partitions and localized CH election mechanism. In addition, consistent end-to-end data routing is performed for improving data dissemination. Simulation results illustrate that our proposed scheme outperforms than existing work in terms of different performance metrics

Grid Based Cluster Head Selection Mechanism for Wireless sensor network

Wireless sensor network (WSN) consists of hundred to thousands sensor nodes to gathered the information from physical environment. Different clustering based algorithms have been proposed to improve network lifetime and energy efficiency. Practically it is not feasible to recharge the battery of sensor nodes when they are sensing the data. In such situation energy is crucial resource and it should be improved for life span of WSN. Cluster head (CH) has an important role in hierarchical energy efficient routing protocols because it receives data from nodes and sends towards base station (BS) or sink node. This paper presents a grid based cluster head selection (GBCHS) mechanism by dividing the network field into MXN uniform size partitions that aims to minimize the energy dissipation of sensor nodes and enhancing network lifetime. Simulation experiments have been performed in network simulator (NS2) that show our proposed GBCHS approach outperformed than standard clustering hierarchy LEACH protocol

Fitness function X-means for prolonging wireless sensor networks lifetime

X-means and k-means are clustering algorithms proposed as a solution for prolonging wireless sensor networks (WSN) lifetime. In general, X-means overcomes k-means limitations such as predetermined number of clusters. The main concept of X-means is to create a network with basic clusters called parents and then generate (j) number of children clusters by parents splitting. X-means did not provide any criteria for splitting parent’s clusters, nor does it provide a method to determine the acceptable number of children. This article proposes fitness function X-means (FFX-means) as an enhancement of X-means; FFX-means has a new method that determines if the parent clusters are worth splitting or not based on predefined network criteria, and later on it determines the number of children. Furthermore, FFX-means proposes a new cluster-heads selection method, where the cluster-head is selected based on the remaining energy of the node and the intra-cluster distance. The simulation results show that FFX-means extend network lifetime by 11.5% over X-means and 75.34% over k-means. Furthermore, the results show that FFX-means balance the node’s energy consumption, and nearly all nodes depleted their energy within an acceptable range of simulation rounds.

Energy Efficient UAV Flight Path Model for Cluster Head Selection in Next‐Generation Wireless Sensor Networks

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Energy Efficient Hierarchical Clustering Approaches in Wireless Sensor Networks: A Survey

Wireless sensor networks (WSN) are one of the significant technologies due to their diverse applications such as health care monitoring, smart phones, military, disaster management, and other surveillance systems. Sensor nodes are usually deployed in large number that work independently in unattended harsh environments. Due to constraint resources, typically the scarce battery power, these wireless nodes are grouped into clusters for energy efficient communication. In clustering hierarchical schemes have achieved great interest for minimizing energy consumption. Hierarchical schemes are generally categorized as cluster-based and grid-based approaches. In cluster-based approaches, nodes are grouped into clusters, where a resourceful sensor node is nominated as a cluster head (CH) while in grid-based approach the network is divided into confined virtual grids usually performed by the base station. This paper highlights and discusses the design challenges for cluster-based schemes, the important cluster formation parameters, and classification of hierarchical clustering protocols. Moreover, existing cluster-based and grid-based techniques are evaluated by considering certain parameters to help users in selecting appropriate technique. Furthermore, a detailed summary of these protocols is presented with their advantages, disadvantages, and applicability in particular cases