scholarly journals Hesitant Fuzzy Entropy-Based Opportunistic Clustering and Data Fusion Algorithm for Heterogeneous Wireless Sensor Networks

Sensors ◽  
2020 ◽  
Vol 20 (3) ◽  
pp. 913
Author(s):  
Junaid Anees ◽  
Hao-Chun Zhang ◽  
Sobia Baig ◽  
Bachirou Guene Lougou ◽  
Thomas Gasim Robert Bona
Keyword(s):  
Wireless Sensor Networks ◽  
Energy Consumption ◽  
Sensor Networks ◽  
Data Fusion ◽  
Network Lifetime ◽  
Cluster Formation ◽  
Fuzzy Entropy ◽  
Sensor Nodes ◽  
Wireless Sensor ◽  
Heterogeneous Clustering

Limited energy resources of sensor nodes in Wireless Sensor Networks (WSNs) make energy consumption the most significant problem in practice. This paper proposes a novel, dynamic, self-organizing Hesitant Fuzzy Entropy-based Opportunistic Clustering and data fusion Scheme (HFECS) in order to overcome the energy consumption and network lifetime bottlenecks. The asynchronous working-sleeping cycle of sensor nodes could be exploited to make an opportunistic connection between sensor nodes in heterogeneous clustering. HFECS incorporates two levels of hierarchy in the network and energy heterogeneity is characterized using three levels of energy in sensor nodes. HFECS gathers local sensory data from sensor nodes and utilizes multi-attribute decision modeling and the entropy weight coefficient method for cluster formation and the cluster head election procedure. After cluster formation, HFECS uses the same techniques for performing data fusion at the first hierarchical level to reduce the redundant information flow from the first-second hierarchical levels, which can lead to an improvement in energy consumption, better utilization of bandwidth and extension of network lifetime. Our simulation results reveal that HFECS outperforms the existing benchmark schemes of heterogeneous clustering for larger network sizes in terms of half-life period, stability period, average residual energy, network lifetime, and packet delivery ratio.

scholarly journals A New Data Fusion Algorithm for Wireless Sensor Networks Inspired by Hesitant Fuzzy Entropy

Sensors ◽  
2019 ◽  
Vol 19 (4) ◽  
pp. 784 ◽  
Author(s):  
Jiayao Wang ◽  
Olamide Tawose ◽  
Linhua Jiang ◽  
Dongfang Zhao
Keyword(s):  
Wireless Sensor Networks ◽  
Energy Consumption ◽  
Sensor Networks ◽  
Data Fusion ◽  
Base Station ◽  
Fuzzy Entropy ◽  
Sensor Nodes ◽  
Wireless Sensor ◽  
Fusion Algorithm ◽  
Time Performance

The wireless sensor network (WSN) is mainly composed of a large number of sensor nodes that are equipped with limited energy and resources. Therefore, energy consumption in wireless sensor networks is one of the most challenging problems in practice. On the other hand, data fusion can effectively decrease data redundancy, reduce the amount of data transmission and energy consumption in the network, extend the network life cycle, improve the utilization of bandwidth, and thus overcome the bottleneck on energy and bandwidth consumption. This paper proposes a new data fusion algorithm based on Hesitant Fuzzy Entropy (DFHFE). The new algorithm aims to reduce the collection of repeated data on sensor nodes from the source, and strives to utilize the information provided by redundant data to improve the data reliability. Hesitant fuzzy entropy is exploited to fuse the original data from sensor nodes in the cluster at the sink node to obtain higher quality data and make local decisions on the events of interest. The sink nodes periodically send local decisions to the base station that aggregates the local decisions and makes the final judgment, in which process the burden for the base station to process all the data is significantly released. According to our experiments, the proposed data fusion algorithm greatly improves the robustness, accuracy, and real-time performance of the entire network. The simulation results demonstrate that the new algorithm is more efficient than the state-of-the-art in terms of both energy consumption and real-time performance.

scholarly journals Calculating the Number of Cluster Heads Based on the Rate-Distortion Function in Wireless Sensor Networks

2014 ◽  
Vol 2014 ◽  
pp. 1-7
Author(s):  
Mingxin Yang ◽  
Jingsha He ◽  
Yuqiang Zhang
Keyword(s):  
Wireless Sensor Networks ◽  
Energy Consumption ◽  
Sensor Networks ◽  
Data Fusion ◽  
Rate Distortion ◽  
Distortion Function ◽  
Sensor Nodes ◽  
Wireless Sensor ◽  
Wireless Sensor Nodes ◽  
Cluster Heads

Due to limited resources in wireless sensor nodes, energy efficiency is considered as one of the primary constraints in the design of the topology of wireless sensor networks (WSNs). Since data that are collected by wireless sensor nodes exhibit the characteristics of temporal association, data fusion has also become a very important means of reducing network traffic as well as eliminating data redundancy as far as data transmission is concerned. Another reason for data fusion is that, in many applications, only some of the data that are collected can meet the requirements of the sink node. In this paper, we propose a method to calculate the number of cluster heads or data aggregators during data fusion based on the rate-distortion function. In our discussion, we will first establish an energy consumption model and then describe a method for calculating the number of cluster heads from the point of view of reducing energy consumption. We will also show through theoretical analysis and experimentation that the network topology design based on the rate-distortion function is indeed more energy-efficient.

An Energy Aware Routing Based on Swarm Intelligence for Wireless Sensor Networks

2013 ◽  
Vol 706-708 ◽  
pp. 635-638
Author(s):  
Yong Lv
Keyword(s):  
Wireless Sensor Networks ◽  
Energy Consumption ◽  
Sensor Networks ◽  
Swarm Intelligence ◽  
Network Lifetime ◽  
Average Energy ◽  
Sensor Nodes ◽  
Wireless Sensor ◽  
Energy Aware ◽  
Energy Efficient Routing

Wireless Sensor Networks consisting of nodes with limited power are deployed to collect and distribute useful information from the field to the other sensor nodes. Energy consumption is a key issue in the sensor’s communications since many use battery power, which is limited. In this paper, we describe a novel energy efficient routing approach which combines swarm intelligence, especially the ant colony based meta-heuristic, with a novel variation of reinforcement learning for sensor networks (ARNet). The main goal of our study was to maintain network lifetime at a maximum, while discovering the shortest paths from the source nodes to the sink node using an improved swarm intelligence. ARNet balances the energy consumption of nodes in the network and extends the network lifetime. Simulation results show that compared with the traditional EEABR algorithm can obviously improve adaptability and reduce the average energy consumption effectively.

Research on Wireless Sensor Networks Data Fusion Algorithm Based on COA-BP

2014 ◽  
Vol 539 ◽  
pp. 247-250
Author(s):  
Xiao Xiao Liang ◽  
Li Cao ◽  
Chong Gang Wei ◽  
Ying Gao Yue
Keyword(s):  
Neural Network ◽  
Wireless Sensor Networks ◽  
Energy Consumption ◽  
Sensor Networks ◽  
Data Fusion ◽  
Network Lifetime ◽  
Bp Neural Network ◽  
Network Traffic ◽  
Bp Algorithm ◽  
Wireless Sensor

To improve the wireless sensor networks data fusion efficiency and reduce network traffic and the energy consumption of sensor networks, combined with chaos optimization algorithm and BP algorithm designed a chaotic BP hybrid algorithm (COA-BP), and establish a WSNs data fusion model. This model overcomes shortcomings of the traditional BP neural network model. Using the optimized BP neural network to efficiently extract WSN data and fusion the features among a small number of original date, then sends the extracted features date to aggregation nodes, thus enhance the efficiency of data fusion and prolong the network lifetime. Simulation results show that, compared with LEACH algorithm, BP neural network and PSO-BP algorithm, this algorithm can effectively reduce network traffic, reducing 19% of the total energy consumption of nodes and prolong the network lifetime.

scholarly journals Increasing network lifetime using data compression in wireless sensor networks with energy harvesting

2017 ◽  
Vol 13 (1) ◽  
pp. 155014771668968 ◽  
Author(s):  
Sunyong Kim ◽  
Chiwoo Cho ◽  
Kyung-Joon Park ◽  
Hyuk Lim
Keyword(s):  
Wireless Sensor Networks ◽  
Energy Consumption ◽  
Sensor Networks ◽  
Energy Harvesting ◽  
Data Compression ◽  
Network Lifetime ◽  
Average Energy ◽  
Sensor Nodes ◽  
Wireless Sensor ◽  
Entire Network

In wireless sensor networks powered by battery-limited energy harvesting, sensor nodes that have relatively more energy can help other sensor nodes reduce their energy consumption by compressing the sensing data packets in order to consequently extend the network lifetime. In this article, we consider a data compression technique that can shorten the data packet itself to reduce the energies consumed for packet transmission and reception and to eventually increase the entire network lifetime. First, we present an energy consumption model, in which the energy consumption at each sensor node is derived. We then propose a data compression algorithm that determines the compression level at each sensor node to decrease the total energy consumption depending on the average energy level of neighboring sensor nodes while maximizing the lifetime of multihop wireless sensor networks with energy harvesting. Numerical simulations show that the proposed algorithm achieves a reduced average energy consumption while extending the entire network lifetime.

scholarly journals Efficient organization of nodes in wireless sensor networks (clustering location-based LEACH)

2022 ◽  
Vol 12 (1) ◽  
pp. 1011
Author(s):  
Mohammed Réda El Ouadi ◽  
Abderrahim Hasbi
Keyword(s):  
Wireless Sensor Networks ◽  
Energy Consumption ◽  
Sensor Networks ◽  
Network Lifetime ◽  
Rapid Development ◽  
Cluster Head ◽  
Base Station ◽  
Sensor Nodes ◽  
Wireless Sensor ◽  
Main Element

The rapid development of connected devices and wireless communication has enabled several researchers to study wireless sensor networks and propose methods and algorithms to improve their performance. Wireless sensor networks (WSN) are composed of several sensor nodes deployed to collect and transfer data to base station (BS). Sensor node is considered as the main element in this field, characterized by minimal capacities of storage, energy, and computing. In consequence of the important impact of the energy on network lifetime, several researches are interested to propose different mechanisms to minimize energy consumption. In this work, we propose a new enhancement of low-energy adaptive clustering hierarchy (LEACH) protocol, named clustering location-based LEACH (CLOC-LEACH), which represents a continuity of our previous published work location-based LEACH (LOC-LEACH). The proposed protocol organizes sensor nodes into four regions, using clustering mechanism. In addition, an efficient concept is adopted to choose cluster head. CLOC-LEACH considers the energy as the principal metric to choose cluster heads and uses a gateway node to ensure the inter-cluster communication. The simulation with MATLAB shows that our contribution offers better performance than LEACH and LOC-LEACH, in terms of stability, energy consumption and network lifetime.

scholarly journals EFISIENSI ENERGI CHAIN-CLUSTER MENGGUNAKAN MODEL RING UNTUK WIRELESS SENSOR NETWORK

2015 ◽  
Vol 5 (1) ◽  
Author(s):  
Siti Ummi Masruroh, M.Sc. , Feri Fahrianto, M.Sc
Keyword(s):  
Wireless Sensor Networks ◽  
Energy Consumption ◽  
Wireless Sensor Network ◽  
Sensor Networks ◽  
Sensor Network ◽  
Cluster Formation ◽  
Sensor Nodes ◽  
Wireless Sensor ◽  
Ring Model ◽  
Clustering Protocols

The function of clustering protocols to minimize the energy consumption of each node, and reduce number of transmission in wireless sensor network. However, most existing clustering protocols consume large amounts of energy, incurred by cluster formation overhead and fixed-level clustering, particularly when sensor nodes are densely deployed in wireless sensor networks. In this paper, we propose Pegasis Routing based on Ring Model, which is energy consumption in the system and prolong the network lifetime, with multiple clustes will decrease the network latency. Keywords: Wireless sensor networks (WSN), PEGASIS, lifetime

Energy Consumption with Fountain Codes and Clustering in Wireless Sensor Networks: Survey & Analysis‎

2017 ◽  
Vol 6 (1) ◽  
pp. 1
Author(s):  
Fatma Belabed ◽  
Ridha Bouallegue
Keyword(s):  
Wireless Sensor Networks ◽  
Energy Consumption ◽  
Sensor Networks ◽  
Network Lifetime ◽  
Sensor Nodes ◽  
Wireless Sensor ◽  
Erasure Codes ◽  
Fountain Codes ◽  
Crucial Issue ◽  
Energy Consumption Model

<div><p><em>Energy is the most important and crucial issue in the wireless sensor networks since the entire sensor nodes are battery powered devices. As a result, energy efficiency and prolonging network lifetime are a challenge. In order to increase the lifetime of the battery-based sensing nodes, it is essential to minimize the consumed energy in the sensing process</em>. <em>With this objective, specific erasure codes called fountain codes are introduced. Fountain codes' performances can be further improved if they are merged with the strategy of grouping sensor nodes into clusters. In order to reach the energy minimization and network lifetime prolonging, the first step, is to completely know the sources of energy consumption. In this paper, sources of energy consumption with various techniques used have been studied and investigated. Furthermore, a survey has been provided for the energy consumption model by using these two techniques. </em></p></div>

Clustering Algorithms for Wireless Sensor Networks Survey

2020 ◽  
Vol 18 (2) ◽  
pp. 143-149
Author(s):  
Sathyapriya Loganathan ◽  
Jawahar Arumugam
Keyword(s):  
Wireless Sensor Networks ◽  
Sensor Networks ◽  
Network Lifetime ◽  
Sensor Node ◽  
Cluster Formation ◽  
Clustering Algorithms ◽  
Cluster Head ◽  
Sensor Nodes ◽  
Wireless Sensor ◽  
Head Node

This paper aims to discuss a comprehensive survey on clustering algorithms for wireless sensor networks (WSN). The several real-time applications adopted the WSN with the advance features. But the capacity and size of the battery used in the sensor nodes are limited. Battery replacement or recharging is very difficult in most outdoor applications. Hence handling this kind of network is one of the issues. One of the best solutions to the energy issue is Clustering. Clustering is to balance the energy consumption of the whole network by cluster-based architecture to prolong the network lifetime. Sensor nodes grouped into clusters; one sensor node selects as the cluster head for each cluster. The cluster head sensor node collects the data from their sensor member nodes and forwards them to the sink node. In cluster-based architecture, cluster formation and the selection of the cluster head node decides the network lifetime. The paper discusses the for and against various clustering algorithms. It suggests the vital parameters for developing energy-efficient clustering algorithms and steps to overcome the limitations.

scholarly journals Energy Efficient Clustering for Wireless Sensor Networks using EASSR

2015 ◽  
Vol 15 (3) ◽  
pp. 554
Author(s):  
Y. Chalapathi Rao ◽  
Ch. Santhi Rani
Keyword(s):  
Wireless Sensor Networks ◽  
Energy Consumption ◽  
Sensor Networks ◽  
Network Lifetime ◽  
Energy Efficient ◽  
Average Energy ◽  
Rechargeable Batteries ◽  
Sensor Nodes ◽  
Wireless Sensor ◽  
Energy Efficient Clustering

<p>Wireless Sensor Networks (WSNs) consist of a large quantity of small and low cost sensor nodes powered by small non rechargeable batteries and furnish with various sensing devices. The cluster-based technique is one of the good perspectives to reduce energy consumption in WSNs. The lifetime of WSNs is maximized by using the uniform cluster location and balancing the network loading between the clusters. We have reviewed various energy efficient schemes apply in WSNs of which we concerted on clustering approach. So, in this paper we have discussed about few existing energy efficient clustering techniques and proposed an Energy Aware Sleep Scheduling Routing (EASSR) scheme for WSN in which some nodes are usually put to sleep to conserve energy, and this helps to prolong the network lifetime. EASSR selects a node as a cluster head if its residual energy is more than system average energy and have low energy consumption rate in existing round. The efforts of this scheme are, increase of network stability period, and minimize loss of sensed data. Performance analysis and compared statistic results show that EASSR has significant improvement over existing methods in terms of energy consumption, network lifetime and data units gathered at BS.</p>

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