Energy-Efficient Clustering System Model and Reconfiguration Schemes for Wireless Sensor Networks

One of the challenging aspects for wireless sensor network (WSN) clustering is to estimate the optimal cluster number. Most WSN clustering techniques have used an a priori value to determine the cluster number. A few techniques, available at the literature, attempted to find the optimal cluster number by energy minimization. However, due to the assumption of lossless communication, these initiatives do not always converge to the minimum energy as wireless channels are error-prone. As wireless communication is always vulnerable to the signal interference, environmental noise, and so on, it suffers from data loss. To capture the characteristics of the communication phenomenon in a real context and have better reliability, we need to embed the link quality into the cluster number determination process. To address this issue, in this paper together with the energy, we incorporate link quality metric into the theoretical underpinning to achieve an optimal cluster number analytically and finally justified the result by simulation.

Clustering has been proposed by researches to group a number of nodes to form a cluster that managed by cluster head. The main goal of clustering protocols is to minimize the energy consumption and enhance the network lifetime of wireless sensor networks. We propose DEBC protocol, which is distributed energy balanced clustering for wireless sensor networks. DEBC forms clusters, selects cluster heads, selects cluster senders and determines appropriate routings in order to reduce overall energy consumption and enhance the network lifetime. DEBC compared to well known protocol with two metrics network lifetime and energy dissipation. The simulation results show that DEBC minimizes energy consumption of sensor nodes and enhance the network lifetime.

Among important issues in the current energy-efficient routing protocols for wireless sensor networks (WSNs) based on clustering approaches are how to determine the optimal number of clusters, how to generate clusters and how select cluster-heads to improve WSNs performance. These approaches aim to reduce energy consumption of the WSN nodes and to extend the network lifetime. This paper reviews and compares the performance of three clustering techniques for determining number of clusters: rule of thumb, elbow, and silhouette. Each of these techniques involves a distributed K-means approach to generate clusters. Moreover, we compare these three clustering methods with low energy adaptive clustering hierarchy (LEACH), Imp LEACH and MODLEACH in terms of network lifetime, energy consumption and the number of packets sent to the base station (BS). The results obtained indicate that rule of thumb method provides better performance compared to other clustering techniques in terms of energy consumption and network lifetime.

Prolonged network lifetime, scalability and efficient load balancing are essential for optimal performance of a wireless sensor network. Clustering provides an effective way of extending the lifetime of a sensor network. Clustering is the process that divides sensor networks into smaller localized group (called clusters) of members with a cluster head. Clustering protocols need to elect optimal number of clusters in hierarchically structured wireless sensor networks. Any clustering scheme that elects clusters uniformly (irrespective of the distance from Base Station) incurs excessive energy usage on clusters proximal and distant to Base Station. In single hop networks a gradual increment in the energy depletion rate is observed as the distance from the cluster head increases[17]. This work focuses on the analysis of wasteful energy consumption within a uniform cluster head election model (EPEM) and provides an analytical solution to reduce the overall consumption of energy usage amongst the clusters elected in a wireless sensor network. A circular model of sensor network is considered, where the sensor nodes are deployed around a centrally located Base Station. The sensor network is divided into several concentric rings centred at the Base Station. A model, Unequal Probability Election Model (UEPEM), which elects cluster heads non-uniformly is proposed. The probability of cluster head election depends on the distance from the Base Station. UEPEM reduces the overall energy usage by about 21% over EPEM. The performance of UEPEM improves as the number of rings is increased.

Clustering in wireless sensor networks has been one of the most effective mechanisms for addressing the issues of network lifetime, scalability and bandwidth reusability. In this paper, application of a relatively new meta-heuristic called as quantum inspired genetic algorithm has been proposed for dealing with the problem of multi-hop energy balanced unequal clustering and prolonging the network lifetime. Quantum inspired genetic algorithm conflates the concepts of evolutionary computation with the principles of quantum computing. The quantum computing principles impart huge parallel processing capabilities to the genetic algorithm due to which these algorithms provide better quality solutions in lesser amount of time. Quantum inspired algorithms are capable of providing competitive solutions even with a very small population size which results in reduced computational cost as population size is a factor in complexity calculation for evolutionary and swarm algorithms. The effectiveness of proposed approach has been evaluated by comparing it with traditional genetic algorithm based clustering for wireless sensor networks. Simulation results clearly indicate that quantum inspired genetic algorithm is capable of performing clustering in more efficient way and increasing the network lifetime as compared to traditional genetic algorithm.

Clustering is an appealing paradigm exploited to improve the lifetime and scalability of wireless sensor networks (WSNs). Considering the NP-completeness of the clustering problem, numerous meta-heuristic algorithms are provided in the literature for the clustering of WSNs. Teaching–learning-based optimization (TLBO) is an optimization algorithm employed to tackle continuous optimization problems. In this paper, a novel discrete version of the TLBO algorithm is being presented that employs the swap and mutation operators to deal with discrete solutions. Subsequently, the new-fangled algorithm was utilized to design a hierarchical energy-aware clustering scheme for the WSNs to minimize the energy usage of the sensor nodes. In addition, an energy-aware local search algorithm was provided to enhance the network lifetime by taking factors such as energy and distance into account. Extensive simulations are conducted to indicate the effectiveness of this scheme in reducing the power usage of the sensor nodes and improving the WSN lifetime.

Energy consumption is the main issue that researchers have dealt with to design any routing application. To design comprehensive reliable wireless sensors networks (WSNs), it is essential to consider node failure and energy constraint as inevitable phenomena. In this paper, a new method for optimal clustering is proposed, that accurately localizes sensors while minimizing their power consumption. It also splits the task of Cluster head between sensor nodes to send data to the base station (BS). The proposed scheme is based on K-means method to selected Cluster-Head. The selection of CH starts with the closest nodes to the centroid point. In the second step, we selected nodes with high power compared to its neighbors, after repeating this phenomenon until the energy of all the nodes is drained. This method can equilibrate the role of Cluster-Head between all the network sensors. Simulation results showed that our clustering mechanism seemed to effectively improve the network lifetime over Low Energy Adaptive Clustering Hierarchy (LEACH) and Energy efficient clustering algorithm for maximizing the lifetime of wireless sensor networks.

In past few years the potential use of wireless sensor network has increased tremendously in a wide range of applications and it has become a hot research area. WSN has two types of routing protocols: flat routing protocols and clustering routing protocols. Low Energy Adaptive Clustering Hierarchy (LEACH) is first major improvements in clustering routing protocols in WSN. This paper presents two modified architectures of LEACH: LEACH-Balanced (LEACH-B) and Two-Level Hierarchy LEACH (TL-LEACH). The main objective of these protocols is to improve the energy efficiency of the sensor nodes and to prolong the lifetime of the wireless sensor network. The performance of LEACH-B and TL-LEACH are compared in terms of Throughput, Packet Delivery Ratio (PDR), Residual Energy and Life-Time of the network.

This paper considers wireless sensor (hyper) networks by single-valued neutrosophic (hyper)graphs. It tries to extend the notion of single-valued neutrosophic graphs to single-valued neutrosophic hypergraphs and it is derived single-valued neutrosophic graphs from single-valued neutrosophic hypergraphs via positive equivalence relation. We use single-valued neutrosophic hypergraphs and positive equivalence relation to create the sensor clusters and access to cluster heads. Finally, the concept of (extended) derivable single-valued neutrosophic graph is considered as the energy clustering of wireless sensor networks and is applied this concept as a tool in wireless sensor (hyper) networks.

Wireless sensor networks (WSNs) are formed using a cluster of sensor nodes (SNs), deployed randomly to perform sensing operations in an area under observation. Due to the unavailability of an external power source, the energy efficiency considered as one of the critical issues in WSNs. Selection of a sensor node (SN) from a wireless sensor network (WSN) cluster to serve as an aggregator or cluster head (CH), considered as an efficient method to increase the lifetime of wireless sensor network (WSN). In this paper, we have proposed an energy efficient CH selection scheme for WSN, to enhance the lifetime and average residual energy of a single WSN cluster. The proposed strategy nominates a group SNs as CHs, based on their channel condition with the base station (BS) and their residual energy. The proposed algorithm is helpful in solving the problem of unbalanced energy consumption in WSNs. Furthermore, the mechanism of using mobile sink during the hand-off stage helps to overcome the delay in data transmission. Moreover, the incorporation of energy harvesting significantly increases the lifetime of WSN. In comparison to a state-of-art technique available in the literature, our scheme shows a 33% increase in lifetime and presents a steady decrease in residual energy for the same rounds of data transmission.

Recent trend to steer Wireless Sensor Networks towards a broad outdoor environment allows to monitor harsh areas and prevent a disaster situation. To guarantee an extensive work of the system and smooth cooperation among involved devices a new clustering approach needs to be found. In this paper, we present a new algorithm for an Event-oriented Focal Weight-based clustering for Wireless Sensor Network which will be able to monitor various parameters (physical, biological, and chemical) of the outdoor environment and signal the current situation further to the network. Shown results demonstrate the applicability of the algorithm for the large amount of nodes with various power levels. The use of a dual-band transceiver and advanced processing elements allow to accommodate new features in the nodes and guarantee a high performance with the low power consumption which is crucial for the outdoor system with no human attendance.

Clustering techniques in wireless sensor networks (WSNs) compare to random selection techniques is less costly due to the saving of time in journeys, reduction in number of transmissions and receptions at each node, identification, contacts, etc., which are valuable for increasing the overall network life, scalability of WSNs. Clustering sensor nodes is an effective and efficient technique for achieving the requirement. The maximizing lifetime of network by minimizing energy consumption poses a challenge in design of protocols. Therefore, proper organization of clustering and orientation of nodes within the cluster becomes one of the important issues to extend the lifetime of the whole sensor network through Cluster Head (CH). We investigate the problem of energy consumption in CH rotation in WSNs. In this paper, CH selection algorithm has been proposed from an unequal cluster. The total energy and expected number of packet retransmissions in delivering a packet from the sensor node to other nodes have been mathematically derived. In this paper, we applied the approach for producing energy-aware unequal clusters with optimal selection of CH and discussed several aspects of the network mathematically and statistically. The simulation results demonstrate that our approach of re-clustering in terms of energy consumption and lifetime parameters.

In recent years there are many extensive applications of wireless sensor networks such as environment monitoring, surveillance, enemy tracking, etc. Since the available energy of sensor nodes are limited and hard to renew, energy supervision is critical for nodes in wireless sensor networks. In this paper, we propose a dynamic multi hop routing technique using residual energy based clustering algorithm to prolong node as well as network lifetime. Here, clusters are constructed using certain suitable parameters such as remaining energy of the nodes, its centrality, energy efficiency and cluster heads selection frequency Furthermore, after collecting all data within a cluster, the cluster heads gradually enhance their transmission range till finding a high residual energy based adjacent cluster heads to forward the data to the base station. In this way the proposed method generates an energy efficient routing path from each sensor to base station to send the data. Simulation results show that our approach effectively conserves energy for cluster heads as well as cluster members and prolong their life time effectively. This proposed method also reduces number of clusters and thus improve the nodes life time significantly.

with the analysis of wireless sensor network clustering route mechanism, Distributed Energy-surplus and Centralism Distance clustering protocol (DECDC) has been proposed for dynamic optimization problem of wireless sensor network routing technology cluster head choice and clustering and inter-cluster route, in witch a node, according to its residual energy and the position and distance from sink node, independently makes its decision compete for becoming a cluster head. Moreover this protocol proposes a communication method, which based on the minimum way tree, causing cluster head to carry on according to the minimum gathering by tree. Experiment results have shown that compared with LEACH, in the same case, can lead to the increase of sensor network lifetime by 800% respectively,the energy consumption is more balance. So the reliability of sensing information in DECDC is higher than the other protocol.

Wireless sensor networks are normally utilized for monitoring and controlling of specific environments. They are made out of a large number of low-cost sensor nodes which are densely separated in distributed environments. The information collected by sensors should be transmitted to a base station. Recent developments indicate that the interest in applications of wireless sensor networks has increased and expanded on a large scale. One of the most important issues in this type of networks is limited energy. Clustering is a suitable method for increasing network lifetime that protects the limited sensor resources by energy saving. Meta-heuristic algorithms have been widely used for clustering of wireless sensor networks. In most complicated problems, it is required to evaluate a multitude of possible modes, to determine an accurate answer. Meta-heuristic algorithms can provide an answer in acceptable time constraints. They play an effective role in solving such problems by optimizing the energy consumption of the networks that has a significant impact on the network lifetime. In this paper, the application of clustering algorithms in wireless sensor networks has been investigated. The selected clustering methods are based on the meta-heuristic algorithms.