scholarly journals LIFETIME MAXIMIZATION OF A MOBILE WSN USING ZRP-FUZZY CLUSTERING PROTOCOL BASED ON ANT-LION OPTIMIZER

2021 ◽  
Vol 1 (1) ◽  
pp. 70-82
Author(s):  
Amnah A. Saadi ◽  
Osama A. Awad
Keyword(s):  
Fuzzy Logic ◽  
Clustering Algorithm ◽  
Cluster Head ◽  
Residual Energy ◽  
Base Station ◽  
Sensor Nodes ◽  
Ant Lion Optimization ◽  
Zone Routing Protocol ◽  
Ant Lion ◽  
Selection Of

Wireless Sensor Networks require energy-efficient protocols for communication and data fusion to integrate data and extend the lifetime of the network. An efficient clustering algorithm for sensor nodes will optimize the energy efficiency of  WSNs. However, the clustering process requires additional overhead, such as selection of cluster head, cluster creation, and deployment. This paper prepared a modified ZRP  for mobile WSN  clustering scheme and optimization using ant-lion optimization algorithm and so far named as mobility cluster head fuzzy logic based on the zone routing protocol (ZRP-FMC-ALO). Which proposed fuzzy logic approach based on three descriptors node for the selection of the CH nodes such as, residual energy, the concentration, and the centrality of the node and also exploited the concept of the mobility of the  Base Station (BS) to prolong the life span of a WSN. The performance of the proposed protocol compared with the famous protocol such as LEACH. Using the MATLAB simulator and the result shows that it outperforms in terms of the WSN network lifetime, the average remaining-consuming energy, and the number of a live node.  

scholarly journals Comparison of Residual Energy-Based Clustering Algorithms for Wireless Sensor Network

2012 ◽  
Vol 2012 ◽  
pp. 1-10 ◽  
Author(s):  
Asis Kumar Tripathy ◽  
Suchismita Chinara
Keyword(s):  
Wireless Sensor Network ◽  
Sensor Network ◽  
Network Architecture ◽  
Clustering Algorithm ◽  
Cluster Head ◽  
Residual Energy ◽  
Base Station ◽  
Sensor Nodes ◽  
Wireless Sensor ◽  
Energy Awareness

Wireless sensor network swears an exceptional fine-grained interface between the virtual and physical worlds. The clustering algorithm is a kind of key technique used to reduce energy consumption. Many clustering, power management, and data dissemination protocols have been specifically designed for wireless sensor network (WSN) where energy awareness is an essential design issue. Each clustering algorithm is composed of three phases cluster head (CH) selection, the setup phase, and steady state phase. The hot point in these algorithms is the cluster head selection. The focus, however, has been given to the residual energy-based clustering protocols which might differ depending on the application and network architecture. In this paper, a survey of the state-of-the-art clustering techniques in WSNs has been compared to find the merits and demerits among themselves. It has been assumed that the sensor nodes are randomly distributed and are not mobile, the coordinates of the base station (BS) and the dimensions of the sensor field are known.

scholarly journals Optimal Cluster Head Selection and Clustering for WSN using PSO

2020 ◽  
Vol 9 (3) ◽  
pp. 389-395
Keyword(s):  
Wireless Sensor Network ◽  
Cluster Head ◽  
Residual Energy ◽  
Base Station ◽  
Sensor Nodes ◽  
Wireless Sensor ◽  
Cluster Head Selection ◽  
Cluster Distance ◽  
Optimal Cluster ◽  
Selection Of

Nowadays, Wireless Sensor Network is the promising and booming technology used in a variety of applications like disaster monitoring, health care, environmental monitoring, agriculture, industrial automation, etc. However the main drawback of the wireless sensor network is the limited energy source of the sensor nodes. Consequently, efficient utilization of the energy becomes essential for increasing the lifetime of network. Clustering protocol is one of the best energy efficient approach for saving the energy and maximizing the network lifetime. But the improper selection of cluster heads (CHs) may lead to the death of the CHs which deteriorate the performance of the network. Therefore the proper selection of cluster head becomes important for the energy conservation of sensor nodes and to maximize the lifetime of network. In this paper, we have presented PSO based optimal cluster head selection algorithm, in which the best possible CHs are chosen on the basis of parameters like residual energy, intra-cluster distance, and inter-cluster distance of the sensor node. With the effective scheme of particle encoding and fitness function, the proposed PSO algorithm is implemented for reducing the energy consumption and improving lifetime of network. The proposed algorithm also ensures the uniform distribution of the energy over network, by changing the role of CHs after each round. We extend our research to cluster formation approach where the sensor nodes are joined to the CH on the basis distance and energy of cluster head. The proposed algorithm is simulated extensively under various conditions like number of sensor nodes in the field, number of CHs, the position of the base station, constant energy and random energy, etc. and the simulation results are analyzed with the extant algorithms. Under all the circumstances the proposed algorithm outperforms the existing LEACH and SEP protocols in terms of average residual energy, the network lifetime and number of data packets received by the base station. Because of the improvement in the lifetime of the network, the proposed algorithm can be used in the applications like environmental monitoring, agriculture etc.

Distributed Entropy Energy-Efficient Clustering algorithm for cluster head selection (DEEEC)

2020 ◽  
Vol 39 (6) ◽  
pp. 8139-8147
Author(s):  
Ranganathan Arun ◽  
Rangaswamy Balamurugan
Keyword(s):  
Energy Efficient ◽  
Clustering Algorithm ◽  
Cluster Head ◽  
Residual Energy ◽  
Energy Utilization ◽  
Sensor Nodes ◽  
Second Stage ◽  
Energy Efficient Clustering ◽  
Two Stages ◽  
Ch Selection

In Wireless Sensor Networks (WSN) the energy of Sensor nodes is not certainly sufficient. In order to optimize the endurance of WSN, it is essential to minimize the utilization of energy. Head of group or Cluster Head (CH) is an eminent method to develop the endurance of WSN that aggregates the WSN with higher energy. CH for intra-cluster and inter-cluster communication becomes dependent. For complete, in WSN, the Energy level of CH extends its life of cluster. While evolving cluster algorithms, the complicated job is to identify the energy utilization amount of heterogeneous WSNs. Based on Chaotic Firefly Algorithm CH (CFACH) selection, the formulated work is named “Novel Distributed Entropy Energy-Efficient Clustering Algorithm”, in short, DEEEC for HWSNs. The formulated DEEEC Algorithm, which is a CH, has two main stages. In the first stage, the identification of temporary CHs along with its entropy value is found using the correlative measure of residual and original energy. Along with this, in the clustering algorithm, the rotating epoch and its entropy value must be predicted automatically by its sensor nodes. In the second stage, if any member in the cluster having larger residual energy, shall modify the temporary CHs in the direction of the deciding set. The target of the nodes with large energy has the probability to be CHs which is determined by the above two stages meant for CH selection. The MATLAB is required to simulate the DEEEC Algorithm. The simulated results of the formulated DEEEC Algorithm produce good results with respect to the energy and increased lifetime when it is correlated with the current traditional clustering protocols being used in the Heterogeneous WSNs.

Developing Multi-Tier Network Design for Effective Energy Consumption of Cluster Head Selection in WSN

2016 ◽  
Vol 13 (1) ◽  
pp. 116
Author(s):  
Wan Isni Sofiah Wan Din ◽  
Saadiah Yahya ◽  
Mohd Nasir Taib ◽  
Ahmad Ihsan Mohd Yassin ◽  
Razulaimi Razali
Keyword(s):  
Sensor Network ◽  
Clustering Algorithm ◽  
Cluster Head ◽  
Residual Energy ◽  
Sensor Nodes ◽  
Cluster Head Selection ◽  
Energy Usage ◽  
Network Cluster ◽  
Fuzzy Logic Approach ◽  
The Stability

Clustering in Wireless Sensor Network (WSN) is one of the methods to minimize the energy usage of sensor network. The design of sensor network itself can prolong the lifetime of network. Cluster head in each cluster is an important part in clustering to ensure the lifetime of each sensor node can be preserved as it acts as an intermediary node between the other sensors. Sensor nodes have the limitation of its battery where the battery is impossible to be replaced once it has been deployed. Thus, this paper presents an improvement of clustering algorithm for two-tier network as we named it as Multi-Tier Algorithm (MAP). For the cluster head selection, fuzzy logic approach has been used which it can minimize the energy usage of sensor nodes hence maximize the network lifetime. MAP clustering approach used in this paper covers the average of 100Mx100M network and involves three parameters that worked together in order to select the cluster head which are residual energy, communication cost and centrality. It is concluded that, MAP dominant the lifetime of WSN compared to LEACH and SEP protocols. For the future work, the stability of this algorithm can be verified in detailed via different data and energy. 

Secure-Aware Multipath Routing Using Atom Search Rider Optimization Algorithm in Wireless Sensor Networks

2021 ◽  
Vol 12 (2) ◽  
pp. 36-55
Author(s):  
M. B. Shyjith ◽  
C. P. Maheswaran ◽  
V. K. Reshma
Keyword(s):  
Routing Protocol ◽  
Multipath Routing ◽  
Cluster Head ◽  
Residual Energy ◽  
Sensor Nodes ◽  
Throughput Rate ◽  
Cluster Heads ◽  
Average Residual ◽  
Maximal Average ◽  
Selection Of

WSN is comprised of sensor nodes that sense the data for various applications. The nodes are employed for transmitting sensed data to BS through intermediate nodes or the cluster heads in multi-hop environment. Erroneous selection of CHs may lead to large energy consumption and thereby degrades system performance. Hence, an effective technique was developed by proposing Rider-ASO for secure-aware multipath routing in the WSN. The proposed routing protocol offers security to the network concerning various trust factors. Initially, cluster head selection is done using RCSO. Then, the trust values of the cluster heads that are selected is computed to ensure security while routing. For the multipath routing, proposed Rider-ASO is developed by combining ASO and ROA. Thus, the proposed algorithm finds multiple secured paths from the source into destination based on selected CHs. The developed Rider-ASO outperformed other methods with minimal delay of 0.009 sec, maximal average residual energy 0.5494 J, maximal PDR of 97.82%, maximal throughput rate of 96.07%, respectively.

A Probability-Based Clustering Algorithm with CH Election for Expanding WSN Life Span

2020 ◽  
Vol 9 (1) ◽  
pp. 1-14
Author(s):  
Dimitris N. Kanellopoulos ◽  
Pratik Gite
Keyword(s):  
Clustering Algorithm ◽  
Cluster Head ◽  
Residual Energy ◽  
Base Station ◽  
Wireless Sensor ◽  
Signal Power ◽  
Aggregated Data ◽  
Simulation Results ◽  
Buffer Length ◽  
Energy Buffer

Clustering achieves energy efficiency and scalable performance in wireless sensor networks (WSNs). A cluster is formed by several sensors nodes, and one of them is elected as Cluster-head (CH). A CH collects information from the cluster members and sends aggregated data to the base station or another CH. This article proposes a new clustering algorithm (EMESISC) that is based on each node's probability of becoming a CH. This node's probability depends on its residual energy, buffer length, and received signal power. We compared EMESISC with LEACH algorithm. Simulation results showed that EMESISC is superior to LEACH.

scholarly journals Irrelevant data elimination based on a k-means clustering algorithm for efficient data aggregation and human activity classification in smart home sensor networks

2020 ◽  
Vol 16 (6) ◽  
pp. 155014772092982
Author(s):  
Siriporn Pattamaset ◽  
Jae Sung Choi
Keyword(s):  
Human Activity ◽  
Data Aggregation ◽  
Smart Home ◽  
Clustering Algorithm ◽  
Cluster Head ◽  
Original Data ◽  
Base Station ◽  
Sensor Nodes ◽  
Activity Classification ◽  
Successful Operation

For the successful operation of smart home environments, it is important to know the state or activity of an occupant. A large number of sensors can be deployed and embedded in places or things. All sensor nodes measure the physical world and send data to the base station for processing. However, the processing of all collected data from every sensor node can consume significant energy and time. In order to enhance the sensor network in smart home applications, we propose the irrelevant data elimination based on k-means clustering algorithm to enhance data aggregation. This approach embeds the cluster head–based algorithm into cluster heads to omit irrelevant data from the base station. The pattern of measured data in each room can be clustered as an active pattern when human activity happens in that room and a stable pattern when human activity does not happen in the room. The irrelevant data elimination based on k-means clustering algorithm approach can reduce 55.94% of the original data with similar results in human activity classification. This study proves that the proposed approach can eliminate meaningless data and intelligently aggregate data by delivering only data from rooms in which human activity likely occurs.

PROCEDURE OF SELECTING A CLUSTER HEAD NODE IN A WIRELESS SENSOR NETWORK USING FUZZY LOGIC

2018 ◽  
pp. 81-89
Author(s):  
Ekaterina Andreevna Evstifeeva ◽  
Valeriy Dmitrievich Semeykin
Keyword(s):  
Fuzzy Logic ◽  
Wireless Sensor Network ◽  
Sensor Network ◽  
Power Efficiency ◽  
Cluster Head ◽  
Residual Energy ◽  
Base Station ◽  
Wireless Sensor ◽  
Head Node ◽  
Input Parameters

Clustering, as one of the energy-efficient approaches, is widely used in wireless sensor networks. This method is based on creating clusters and selecting cluster head nodes in a wireless sensor network. Clustering saves network energy because data transfer is restricted between multiple nodes. Thus, clustering is provided between several nodes, and the service life of the wireless sensor network can be extended. Since the parent cluster node interacts with other nodes of the network, a node with a high level of residual energy must be selected to perform this role. When the energy level of the selected cluster head node becomes lower than the threshold value, then the re-election of this node takes place. It should be noted that multiple patterns of choosing cluster head nodes built using various parameters (residual node energy, distance from the base station to a node, distance between the head node and a cluster member, the number and proximity of neighboring nodes, etc.) lacked for a factor of energy consumption, i.e. how many times nodes communicated to each other. To cope with the problem, this paper presents a prognostic algorithm for selecting a cluster head node using fuzzy logic. This algorithm suggests using a number of input parameters, such as the residual energy of the node, the proximity of neighboring nodes, and the centralization of the node in the cluster. The proposed algorithm has been implemented using the software package MATLAB Fuzzy Logic Toolbox. The simulation results prove the advantages of the proposed technique; application of the input parameters mentioned above helps select optimal cluster head nodes in a wireless sensor network, which increases power efficiency of a wireless sensor network.

Energy Efficient Cluster-based Routing using Hybrid Approach of Fuzzy and Ida-Star for Wireless Sensor Networks

2021 ◽  
Author(s):  
Mohaideen Pitchai K
Keyword(s):  
Fuzzy Logic ◽  
Energy Consumption ◽  
Energy Efficient ◽  
Clustering Algorithm ◽  
Cluster Formation ◽  
Hybrid Approach ◽  
Cluster Head ◽  
Residual Energy ◽  
The Other ◽  
Cluster Head Selection

Abstract Appropriate cluster head selection can significantly reduce energy consumption and enhance the lifetime of the WSN. The choice of cluster heads, which is a pivotal step in the cluster-based algorithm, can seriously influence the performance of the clustering algorithm. Under normal circumstances, whether a node can be a cluster head or not depends not only on its energy level but also on the other factors such as energy consumption, channel lost, neighbor density, etc. In this sense, the selection of the cluster head can be regarded as a multiple criteria decision-making issue. This paper presents an Energy efficient Cluster Head selection using Fuzzy Logic (ECHFL) protocol, which combines the approaches of the fuzzy and IDA-star algorithm. This protocol selects the appropriate cluster head by using fuzzy inference rules. It uses three parametric descriptors such as residual energy, expected residual energy, and node centrality for the cluster formation and cluster head selection processes. These parameters contribute mainly for avoiding over-dissipation of energy in the network by selecting the suitable cluster head for the network. This protocol shows how fuzzy logic can be used in the cluster formation process to distribute the tasks and energy consumption over all the nodes. As a summary, the proposed protocol gives good performance results in comparison with the other protocols.

DEHCA: Load Balance Clustering Algorithm for Energy Heterogeneous WSN Based on Distance

2010 ◽  
Vol 44-47 ◽  
pp. 3294-3298 ◽  
Author(s):  
Ying Zhang ◽  
Gui Ling Sun ◽  
Wei Xiang Li
Keyword(s):  
Load Balance ◽  
Clustering Algorithm ◽  
Consumption Rate ◽  
Cluster Head ◽  
Residual Energy ◽  
Stability Period ◽  
New Concepts ◽  
The Stability ◽  
Energy Consumption Rate ◽  
Selection Of

Considering the characteristics of energy heterogeneous and the requirement of load balance in Wireless Sensor Network (WSN), a novel clustering routing, DEHCA was presented for energy heterogeneous WSN (EHWSN). Based on Node Distance in EHWSN, new concepts as Residual Energy, Energy Consumption Rate, Centroid Superiority, and Cluster Head Carrier Capacity, were introduced to weigh the selection of cluster head and realize the energy heterogeneous load balance. Simulation shows that load balance clustering routing for EHWSN based on distance could better prolong the stability period and enhance the load balance capacity.

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