Event-Oriented Focal Weight-Based Clustering for Environmental Wireless Sensor Networks

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.

Energy efficiency is a critical factor for operation of a wireless sensor network. In general, wireless sensor networks perform clustering and routing using localized neighbor information and simple rules only. In this paper, we propose an energy efficient clustering and routing method for wireless sensor networks by exploiting energy state information of neighbor sensor nodes for selection of cluster heads. We compared the performance of the proposed method with that of a well known clustering and routing protocol. Our computational experiments demonstrate that the lifetime and distribution of energy consumptions of the proposed method are better than those of the compared method.

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.

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 (WSN) has been attracting growing interests for developing a new generation of large-scale embedded computing systems. However, the communication paradigms in wireless sensor networks differ from the ones associated to traditional wireless networks, triggering the need for new communication protocols and energy-consuming model. The NDSA (A Novel Data-sending Algorithm) Based on Cluster for wireless sensor networks aims at the design of a scalable data-sending model for supporting large-scale embedded computing applications with critical requirements. In this paper, we assume intra-cluster data taking on Gaussian distribution. According to the desired accuracy given by system, NDSA can automatically adjust the number of data-sending nodes. Experimental results show that NDSA can lengthen the life of network significantly better than other similar algorithms.

Wireless sensor networks is a key technology for new ways of interaction between computers and the physical environment. However, the energy constrained and limited computing resources of the sensor nodes present major challenges in gathering data. Since sensor nodes are densely deployed, redundant data may occur. While cluster-based data gathering is efficient at energy and bandwidth, it's difficult to cluster network efficiently. In this work, a new distributed clustering algorithm for ubiquitous sensor network is presented. Clustering is based on the distance between nodes and the number in a cluster for wireless sensor networks. Simulation results show that the proposed algorithm balances the energy dissipation over the whole network thus increase the amount of data delivery to the sink

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.

A wireless sensor network with a large number of small sensor nodes can be used as an effective tool for gathering data in various situations. The paradigm of data aggregation enables wireless sensor networks to accurately estimate the different parameters of an environment. The aim of this paper is to introduce a new fault tolerant protocol striving to reduce overall power consumption, prolong network lifetime, and support mobile nodes in a wireless sensor network. Our protocol, FTPASC (fault tolerant power aware protocol with static clustering), partitions the network into static clusters and distributes the energy load among high-power sensor nodes. Mobility management is another attractive feature of this protocol that guarantees reliable communications between mobile and sensor nodes. Simulation results show that FTPASC outperforms conventional protocols in terms of network lifetime and power consumption minimization

Based on the principles of wireless mesh network (WMN), clustering of wireless sensor network (WSN) was reconstructed by combination of WMN and WSN into a topology named wireless mesh sensor network (WMSN). In terms of Voronoi diagram and Delaunay triangulations of computational geometry, central degree theory of complex network and heuristic algorithm based on mean weight of communication energy, a optimal deployment algorithm named WHD algorithm which can figure out large scale WMSN was designed. The study results show that the combination of WSN and WMN can achieve mesh wide hybrid network topology; WHD algorithm can be an effective solution to optimal deployment of large-scale WMSN. The experimental results show that WHD algorithm not only ensures optimal deployment of large-scale WMSN but also has the energy saved features, and then extends the network life-cycle efficiently.

Wireless Sensor Networks (WSNs) have extensively been worked upon due to their ability to monitor various physical phenomenon. Time synchronization is the most fundamental and most important challenge faced by WSNs. Time Synchronization in WSN can be achieved either by gaining consensus amongst all the nodes or by a reference node which broadcasts a single unit of time to the entire network. The paper describes a novel technique to achieve fully distributed cluster clock synchronization, which synchronizes all the nodes in the network to a common consensus value. The algorithm employs clustering and confidence weighted running average (CWA) method for offset and skew compensations. The efficiency of protocol is validated with simulation results presented in the paper.

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.

Clustering in wireless sensor networks (WSNs) attracted academia and industry alike due to the wide range of their applications. For the limited resources of WSNs, many algorithms have been proposed for efficient clustering. One of the most famous clustering algorithms is hybrid energy efficient distributed (HEED) protocol. At the same time, security is another challenge due to the critical information transferred. In this paper, we propose a modified HEED, entitled S2HEED, in which a fuzzy logic is used to enhance the cluster head selection criteria for efficient distribution in the field. In addition, we propose a security framework that can be used along with HEED. Based on extensive experiments, our proposals in this paper show that fuzzy logic approach prolongs the network lifetime and the proposed framework defend the network from different attacks like Sinkhole, Sybil attack, Hello flooding attack and selective forwarding in addition to the replay attack.

Clustering in wireless sensor networks is one of the crucial methods for increasing of network lifetime. There are many algorithms for clustering. One of the cluster based algorithm in wireless sensor networks is LEACH algorithm. In this paper we proposed a new clustering method for increasing of network lifetime. In proposed method Clustering is done symmetrically and the best node with respect to remained energy and distance of other nodes in comparing with each that selected as a cluster head. Although in this protocol we didn’t use GPS but we could find geographical position nodes so easily. However, failures in higher level of hierarchy e.g. cluster-head cause more damage to the system because they also limit accessibility to the nodes that are under their supervision. In this paper we propose an efficient mechanism to recover sensors from a failed cluster. In this performance of the proposed algorithm via computer simulation was evaluated and compared with other clustering algorithms. The simulation results show the high performance of the proposed clustering algorithm.

By taking the data-receiving energy consumption into account, we propose an extended analytical model to derive the optimal parameters ? the number of clusters for wireless sensor networks. Our analyses show that the previous existing model underestimates the optimal number of clusters within a sensor network. The simulation results verify our analyses and demonstrate the modified model is more accurate in deriving the optimal number of clusters to maximize the network lifetime of the wireless sensor networks. Using our extended model, we also develop the energy-threshold-driven based clustering reconfiguration schemes with the optimal number of clusters to further improve the wireless sensor-network lifetime. The simulation experiments indicate that our clustering reconfiguration schemes outperform the existing ones in terms of sensor-network lifetime while imposing low overhead due to clustering reconfigurations.