Energy efficient rough fuzzy set based clustering and cluster head selection for WSN
01 Oct 2016-Vol. 2016, pp 439-444
TL;DR: Matlab simulation results indicate RF-LEACH performs better than LEACH, Fuzzy LEACH and FCM LEACH in terms of extending network lifetime and throughput in a load balanced way and is shown to be statistically significant.
Abstract: Wireless Sensor Network (WSN) consists of battery powered sensor nodes communicating to each other and to the base station (BS) in multi-hop wireless manner. The primary task of WSN is to gather field data and route it to the BS for analysis. They are generally deployed in harsh localities where battery replacement is not possible advocating the need for energy efficient data gathering to elongate network lifetime. In cluster based hierarchical routing protocols (HRP's) the energy intensive task of being a cluster head are performed by a subset of deployed nodes while others are engaged in local communication. The roles of the nodes are interchanged in different rounds for load balancing. In this paper we propose an energy efficient load balanced data gathering protocol coined as RF-LEACH where partitioning is done using rough fuzzy c means (RFCM) and cluster head selection is based on fuzzy logic. Matlab simulation results indicate RF-LEACH performs better than LEACH, Fuzzy LEACH and FCM LEACH in terms of extending network lifetime and throughput in a load balanced way. The results are shown to be statistically significant.
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TL;DR: A multi-layer cluster-based Energy Efficient (MLCEE) protocol for UWSNs to address the issue of hotspot and energy consumption and the simulation results have revealed that MLCEE achieves superior performance than the other techniques with regard to the network lifetime, energy consumption, and data transmission amount.
Abstract: Underwater Wireless Sensor Networks (UWSNs) have emerged as a remarkable interest for scholars worldwide in terms of various applications such as monitoring offshore oil and gas reservoirs, pollution, oceans for defense, and other applications such as tsunami. Terrestrial Wireless Sensor Networks (TWSN) and UWSNs share many characteristics apart from having different communication medium and working environment as UWSNs face the challenges of low-bandwidth, long latency, and high bit error rate. These have caused for UWSNs many problems such as low reliability, packet retransmission, and high consumption of energy. To alleviate the aforementioned issues, many techniques have been proposed. However, most of them merely consider the issue of hotspot which occurs due to the unbalanced transmission of load on sensor nodes near the surface sink. In this article, we propose a multi-layer cluster-based Energy Efficient (MLCEE) protocol for UWSNs to address the issue of hotspot and energy consumption. There are different stages in MLCEE, first of which is the division of the whole network in layers, the second is clustering of the nodes at same layers. In the last stage of transmission, the cluster head (CH) selects the next hop among the CHs based on greater fitness value, small Hopid and small layer number. To mitigate the issue of hotspot, the first layer remains un-clustered and any node in the first layer transfers data to the sink directly while cluster heads (CHs) are selected based on Bayesian Probability and residual energy. The simulation results of the proposed technique, done using MATLAB, have revealed that MLCEE achieves superior performance than the other techniques with regard to the network lifetime, energy consumption, and data transmission amount.
16 citations
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TL;DR: A localization-free routing protocol, named energy efficient routing protocol based on layers and unequal clusters (EERBLC) is proposed, which can effectively balance the energy consumption, prolong the network lifetime, and increase the amount of data transmission compared with DBR and EEDBR protocols.
Abstract: Underwater Wireless Sensor Networks (UWSNs) have drawn tremendous attentions from all fields because of their wide application. Underwater wireless sensor networks are similar to terrestrial Wireless Sensor Networks (WSNs), however, due to different working environment and communication medium, UWSNs have many unique characteristics such as high bit error rate, long end-to-end delay and low bandwidth. These characteristics of UWSNs lead to many problems such as retransmission, high energy consumption and low reliability. To solve these problems, many routing protocols for UWSNs are proposed. In this paper, a localization-free routing protocol, named energy efficient routing protocol based on layers and unequal clusters (EERBLC) is proposed. EERBLC protocol consists of three phases: layer and unequal cluster formation, transmission routing, maintenance and update of clusters. In the first phase, the monitoring area under the water is divided into layers, the nodes in the same layer are clustered. For balancing energy of the whole network and avoiding the “hotspot” problem, a novel unequal clustering method based on layers for UWSNs is proposed, in which a new calculation method of unequal cluster size is presented. Meanwhile, a new cluster head selection mechanism based on energy balance and degree is given. In the transmission phase, EERBLC protocol proposes a novel next forwarder selection method based on the forwarding ratio and the residual energy. In the third phase, Intra and inter cluster updating method is presented. The simulation results show that the EERBLC can effectively balance the energy consumption, prolong the network lifetime, and increase the amount of data transmission compared with DBR and EEDBR protocols.
8 citations
Journal Article•
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TL;DR: The results prove that the proposed PFuzzyACO algorithm behaves better than the comparative models for the system of 50 nodes with the values of 10, 0.08783, and 0.67651 for the number of alive nodes, network energy, and the throughput, respectively.
Abstract: Cluster head selection is one of the prime challenges in the routing of the Wireless Sensor Network (WSN). Literature works have introduced various techniques for finding the optimal cluster head for establishing the communication path. The uncertainty issues prevailing in the WSN makes way for the selection of the cluster head through the optimization algorithms. In this paper, the Fuzzy based optimization approach has been introduced for the optimal selection of the cluster head. This paper proposes the Penguin Fuzzy based Ant colony optimization (PFuzzyACO) algorithm for the cluster head selection in the WSN. The proposed PFuzzyACO algorithm utilizes a multi-objective fitness function based on the parameters, such as energy, distance, delay, traffic density, and link lifetime. The results prove that the proposed PFuzzyACO algorithm behaves better than the comparative models for the system of 50 nodes with the values of 10, 0.08783, and 0.67651 for the number of alive nodes, network energy, and the throughput, respectively. The proposed PFuzzyACO model has better values of 26, 0.10857, and 0.68116 for the nodes, network energy, and the throughput respectively for WSN with 100 nodes.
7 citations
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07 Mar 2019
TL;DR: This work proposes the development of an application, using iOS and Android platforms to help as a virtual scheduler to users, through organizationing and reminding.
Abstract: Nowadays, there are several technological advances that attenuate parental responsibility and help with their tasks. Furthermore, the importance of one or more computational systems that contribute to parental tasks is sensible. Therefore, is possible to notice a relation between motherhood and applications. Whereas, in present times, people are more and more busy and have less and less time, everyone needs aid to remember and execute everyday tasks. Thereby, the question: what is necessary to develop a mobile system that can help parents, in special first time ones? Stands out, therefore we propose the development of an application, using iOS and Android platforms to help as a virtual scheduler to users, through organizationing and reminding
6 citations
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TL;DR: An attempt has been made to increase the transmission efficiency and network lifetime of a wireless sensor network (WSN) by clustering method using Fuzzy logic, which provides an efficient approach for WSN.
Abstract: In this paper, an attempt has been made to increase the transmission efficiency and network lifetime of a wireless sensor network (WSN) by clustering method using Fuzzy logic. Here, the cluster head (CH) is elected based on the Fuzzy logic. Enhancement of lifetime for the nodes operating in WSN is an important issue that needs to be resolved for increasing the system efficiency and performance. The technique of clustering has found large number of benefits with respect to achieving system efficiency and least energy consumption. The protocols employed in an intelligent WSN should favor maximum transmission efficiency and provide maximum network lifetime from the utilized algorithm that is exactly attempted to be achieved through this method. The first node dead (FND) and the lifetime of the network using the fuzzy logic in the proposed work are compared with four other mechanisms. Both FND and lifetime are found to be better in the present work which provides an efficient approach for WSN.
4 citations
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References
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01 Jan 1967
TL;DR: The k-means algorithm as mentioned in this paper partitions an N-dimensional population into k sets on the basis of a sample, which is a generalization of the ordinary sample mean, and it is shown to give partitions which are reasonably efficient in the sense of within-class variance.
Abstract: The main purpose of this paper is to describe a process for partitioning an N-dimensional population into k sets on the basis of a sample. The process, which is called 'k-means,' appears to give partitions which are reasonably efficient in the sense of within-class variance. That is, if p is the probability mass function for the population, S = {S1, S2, * *, Sk} is a partition of EN, and ui, i = 1, 2, * , k, is the conditional mean of p over the set Si, then W2(S) = ff=ISi f z u42 dp(z) tends to be low for the partitions S generated by the method. We say 'tends to be low,' primarily because of intuitive considerations, corroborated to some extent by mathematical analysis and practical computational experience. Also, the k-means procedure is easily programmed and is computationally economical, so that it is feasible to process very large samples on a digital computer. Possible applications include methods for similarity grouping, nonlinear prediction, approximating multivariate distributions, and nonparametric tests for independence among several variables. In addition to suggesting practical classification methods, the study of k-means has proved to be theoretically interesting. The k-means concept represents a generalization of the ordinary sample mean, and one is naturally led to study the pertinent asymptotic behavior, the object being to establish some sort of law of large numbers for the k-means. This problem is sufficiently interesting, in fact, for us to devote a good portion of this paper to it. The k-means are defined in section 2.1, and the main results which have been obtained on the asymptotic behavior are given there. The rest of section 2 is devoted to the proofs of these results. Section 3 describes several specific possible applications, and reports some preliminary results from computer experiments conducted to explore the possibilities inherent in the k-means idea. The extension to general metric spaces is indicated briefly in section 4. The original point of departure for the work described here was a series of problems in optimal classification (MacQueen [9]) which represented special
22,533 citations
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TL;DR: The concept of sensor networks which has been made viable by the convergence of micro-electro-mechanical systems technology, wireless communications and digital electronics is described.
Abstract: This paper describes the concept of sensor networks which has been made viable by the convergence of micro-electro-mechanical systems technology, wireless communications and digital electronics. First, the sensing tasks and the potential sensor networks applications are explored, and a review of factors influencing the design of sensor networks is provided. Then, the communication architecture for sensor networks is outlined, and the algorithms and protocols developed for each layer in the literature are explored. Open research issues for the realization of sensor networks are also discussed.
17,354 citations
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TL;DR: This work develops and analyzes low-energy adaptive clustering hierarchy (LEACH), a protocol architecture for microsensor networks that combines the ideas of energy-efficient cluster-based routing and media access together with application-specific data aggregation to achieve good performance in terms of system lifetime, latency, and application-perceived quality.
Abstract: Networking together hundreds or thousands of cheap microsensor nodes allows users to accurately monitor a remote environment by intelligently combining the data from the individual nodes. These networks require robust wireless communication protocols that are energy efficient and provide low latency. We develop and analyze low-energy adaptive clustering hierarchy (LEACH), a protocol architecture for microsensor networks that combines the ideas of energy-efficient cluster-based routing and media access together with application-specific data aggregation to achieve good performance in terms of system lifetime, latency, and application-perceived quality. LEACH includes a new, distributed cluster formation technique that enables self-organization of large numbers of nodes, algorithms for adapting clusters and rotating cluster head positions to evenly distribute the energy load among all the nodes, and techniques to enable distributed signal processing to save communication resources. Our results show that LEACH can improve system lifetime by an order of magnitude compared with general-purpose multihop approaches.
9,655 citations
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TL;DR: This survey presents a comprehensive review of the recent literature since the publication of a survey on sensor networks, and gives an overview of several new applications and then reviews the literature on various aspects of WSNs.
Abstract: A wireless sensor network (WSN) has important applications such as remote environmental monitoring and target tracking. This has been enabled by the availability, particularly in recent years, of sensors that are smaller, cheaper, and intelligent. These sensors are equipped with wireless interfaces with which they can communicate with one another to form a network. The design of a WSN depends significantly on the application, and it must consider factors such as the environment, the application's design objectives, cost, hardware, and system constraints. The goal of our survey is to present a comprehensive review of the recent literature since the publication of [I.F. Akyildiz, W. Su, Y. Sankarasubramaniam, E. Cayirci, A survey on sensor networks, IEEE Communications Magazine, 2002]. Following a top-down approach, we give an overview of several new applications and then review the literature on various aspects of WSNs. We classify the problems into three different categories: (1) internal platform and underlying operating system, (2) communication protocol stack, and (3) network services, provisioning, and deployment. We review the major development in these three categories and outline new challenges.
5,311 citations
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TL;DR: A FORTRAN-IV coding of the fuzzy c -means (FCM) clustering program is transmitted, which generates fuzzy partitions and prototypes for any set of numerical data.
Abstract: This paper transmits a FORTRAN-IV coding of the fuzzy c -means (FCM) clustering program. The FCM program is applicable to a wide variety of geostatistical data analysis problems. This program generates fuzzy partitions and prototypes for any set of numerical data. These partitions are useful for corroborating known substructures or suggesting substructure in unexplored data. The clustering criterion used to aggregate subsets is a generalized least-squares objective function. Features of this program include a choice of three norms (Euclidean, Diagonal, or Mahalonobis), an adjustable weighting factor that essentially controls sensitivity to noise, acceptance of variable numbers of clusters, and outputs that include several measures of cluster validity.
4,444 citations
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