scholarly journals An Implementation of Grouping Nodes in Wireless Sensor Network Based on Distance by Using k-Means Clustering

2018 ◽  
Vol 12 (2) ◽  
pp. 97
Author(s):  
Rizqi Fauzil Azhar ◽  
Ahmad Zainudin ◽  
Prima Kristalina ◽  
Bagas Mardiasyah Prakoso ◽  
Niam Tamami
Keyword(s):  
Wireless Sensor Network ◽  
Sensor Network ◽  
Power Efficiency ◽  
Clustering Algorithm ◽  
Nearest Neighbor ◽  
Cluster Head ◽  
Channel Modeling ◽  
Sensor Nodes ◽  
Wireless Sensor ◽  
Observation Area

Wireless Sensor Network (WSN) is a network consisting of several sensor nodes that communicate with each other and work together to collect data from the surrounding environment. One of the WSN problems is the limited available power. Therefore, nodes on WSN need to communicate by using a cluster-based routing protocol. To solve this, the researchers propose a node grouping based on distance by using k-means clustering with a hardware implementation. Cluster formation and member node selection are performed based on the nearest device of the sensor node to the cluster head. The k-means algorithm utilizes Euclidean distance as the main grouping nodes parameter obtained from the conversion of the Received Signal Strength Indication (RSSI) into the distance estimation between nodes. RSSI as the parameter of nearest neighbor nodes uses lognormal shadowing channel modeling method that can be used to get the path loss exponent in an observation area. The estimated distance in the observation area has 27.9% error. The average time required for grouping is 58.54 s. Meanwhile, the average time used to retrieve coordinate data on each cluster to the database is 45.54 s. In the system, the most time-consuming process is the PAN ID change process with an average time of 14.20 s for each change of PAN ID. The grouping nodes in WSN using k-means clustering algorithm can improve the power efficiency by 6.5%.

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 Design and Analysis of Clustering Routing Protocol in Wireless Sensor Network of Frozen Food

2017 ◽  
Vol 13 (12) ◽  
pp. 37
Author(s):  
Jianjun Wu ◽  
Xiao Feng ◽  
Huidang Zhang ◽  
Wei Lv
Keyword(s):  
Sensor Network ◽  
Clustering Algorithm ◽  
Nearest Neighbor ◽  
Cluster Head ◽  
Sensor Nodes ◽  
Wireless Sensor ◽  
Centralized Control ◽  
Equal Distribution ◽  
Frozen Food ◽  
Uniform Coverage

<span style="font-family: 'Times New Roman',serif; font-size: 10pt; mso-fareast-font-family: 'Times New Roman'; mso-ansi-language: EN-US; mso-fareast-language: DE; mso-bidi-language: AR-SA;">In order to address the issues of uneven energy dissipation and uniform cluster coverage in wireless sensor networks (WSNs) of frozen food, we developed a load balancing and uniform coverage clustering (LBUCC) algorithm to find an efficient way to generate clusters. Considering node density and the coverage radius of cluster heads, the nearest neighbor clustering algorithm was adopted to cluster the sensor network. On the basis of the number of neighbors and the residual energy of nodes, the LBUCC algorithm ensures the equal distribution of cluster head responsibility among sensor nodes and performs well in periodic data gathering with selected cluster head. As the storage and distribution of frozen food is frequent, the clustering strategy was proposed for dynamic topology in this paper. The LBUCC algorithm was compared with LEACH-C and DHAC algorithms which are well-known in using centralized control algorithm to select cluster head. The simulation results demonstrate that the LBUCC algorithm has longer network lifetime and uniform coverage than the clustering protocols LEACH-C and DHAC do.</span>

scholarly journals Unequal clustering in wireless sensor network: a review

2021 ◽  
Vol 22 (1) ◽  
pp. 419
Author(s):  
Khalid Waleed Al-ani ◽  
Fairuz Bin Abdullah ◽  
Salman Yossuf
Keyword(s):  
Wireless Sensor Network ◽  
Sensor Network ◽  
Clustering Algorithm ◽  
Cluster Head ◽  
Sensor Nodes ◽  
Wireless Sensor ◽  
Unequal Clustering ◽  
Energy Hole ◽  
Node Location ◽  
Energy Hole Problem

<p><span>In recent time, the applications' diversity of wireless sensor network (WSN) attracts many researchers. WSN comprises of many sensor nodes with limited battery power. Therefore, energy consumption should be controlled to the optimum. Clustering is an efficient solution for energy management in WSN, but clustering does not consider the sink node location. It will cause the energy hole problem in multi-hop routing. Energy hole problem was solved by unequal clustering. A review of various unequal clustering mechanisms is presented in this paper. The comparison between the various mechanisms was based on cluster head election process, cluster properties, simulation parameters and energy efficiency to highlight a more efficient and scalable unequal clustering algorithm for </span>WSN.</p>

scholarly journals TRACKING OF MOVING OBJECT IN WIRELESS SENSOR NETWORK

2015 ◽  
pp. 42-47
Author(s):  
D. CHARANYA ◽  
G. V. UMA
Keyword(s):  
Energy Consumption ◽  
Wireless Sensor Network ◽  
Sensor Network ◽  
Clustering Algorithm ◽  
Cluster Head ◽  
Base Station ◽  
Moving Object ◽  
Sensor Nodes ◽  
Wireless Sensor ◽  
Current Location

A Wireless Sensor Network is a collection of sensor nodes distributed into a network to monitor the environmental conditions and send the sensed data to the Base Station. Wireless Sensor Network is one of the rapidly developing area in which energy consumption is the most important aspect to be considered while tracking, monitoring, reporting and visualization of data. An Energy Efficient Prediction-based Clustering algorithm is proposed to track the moving object in wireless sensor network. This algorithm reduces the number of hops between transmitter and receiver nodes and also the number of transmitted packets. In this method, the sensor nodes are statically placed and clustered using LEACH-R algorithm. The Prediction based clustering algorithm is applied where few nodes are selected for tracking which uses the prediction mechanism to predict the next location of the moving object. The Current Location of the target is found using Trilateration algorithm. The Current Location or Predicted Location is sent to active Cluster Head from the leader node or the other node. Based on which node send the message to the Cluster Head, the Predicted or Current Location will be sent to the base station. In real time, the proposed work is applicable in traffic tracking and vehicle tracking. The experiment is carried out using Network Stimulator-2 environment. Simulation result shows that the proposed algorithm gives a better performance and reduces the energy consumption.

scholarly journals HYBRID APPROACH USING MOBILE SINK AND FUZZY LOGIC FOR REGION BASED CLUSTERING IN WSN

2017 ◽  
Vol 16 (5) ◽  
pp. 6933-6944
Author(s):  
Amneet Kaur ◽  
Harpreet Kaur
Keyword(s):  
Wireless Sensor Network ◽  
Sensor Network ◽  
Clustering Algorithm ◽  
Research Work ◽  
Hybrid Approach ◽  
Cluster Head ◽  
Base Station ◽  
Sensor Nodes ◽  
Wireless Sensor ◽  
Fuzzy Variables

Wireless sensor network has revolutionized the way computing and software services are delivered to the clients on demand. Wireless sensor network is very important to the mankind. It consist of number of sensor called nodes and a base station. Nodes collect data and send to the base station. There are number of nodes which send data at a time. So, number of problems are occurred. So, far this nodes are divided into cluster then a cluster head will be formed. WSN is a battery powered system. When the battery is died no data send or received. So when all nodes participate for sending and receiving data then system is died earlier. Our research work proposed a new method for cluster head selection having less computational complexity. It was also found that the modified approach has improved performance to that of the other clustering approaches. The network area is divided into same sized small–small regions. Sensor nodes are randomly deployed in each predefined sub-area. Each region will have its region head (RH) and multiple member nodes. The member nodes in a specific region will send the data to the RH. RH within the region will be elected by distributed mechanism and will be based on fuzzy variables. It was found that the proposed algorithm gives a much improved network lifetime as compared to existing work. Based on our model, transmission tuning algorithm for cluster-based WSNs has been proposed to balance the load among cluster heads that fall in different regions. This algorithm is applied prior to a cluster algorithm to improve the performance of the clustering algorithm without affecting the performance of individual sensor nodes.

scholarly journals Lifetime centric load balancing mechanism in wireless sensor network based IoT environment

2021 ◽  
Vol 11 (5) ◽  
pp. 4183
Author(s):  
Veerabadrappa Veerabadrappa ◽  
Booma Poolan Marikannan
Keyword(s):  
Wireless Sensor Network ◽  
Load Balancing ◽  
Sensor Network ◽  
Clustering Algorithms ◽  
Research Work ◽  
Cluster Head ◽  
Sensor Nodes ◽  
Wireless Sensor ◽  
Communication Overhead ◽  
Physical Parameters

Wireless sensor network (WSN) is a vital form of the underlying technology of the internet of things (IoT); WSN comprises several energy-constrained sensor nodes to monitor various physical parameters. Moreover, due to the energy constraint, load balancing plays a vital role considering the wireless sensor network as battery power. Although several clustering algorithms have been proposed for providing energy efficiency, there are chances of uneven load balancing and this causes the reduction in network lifetime as there exists inequality within the network. These scenarios occur due to the short lifetime of the cluster head. These cluster head (CH) are prime responsible for all the activity as it is also responsible for intra-cluster and inter-cluster communications. In this research work, a mechanism named lifetime centric load balancing mechanism (LCLBM) is developed that focuses on CH-selection, network design, and optimal CH distribution. Furthermore, under LCLBM, assistant cluster head (ACH) for balancing the load is developed. LCLBM is evaluated by considering the important metrics, such as energy consumption, communication overhead, number of failed nodes, and one-way delay. Further, evaluation is carried out by comparing with ES-Leach method, through the comparative analysis it is observed that the proposed model outperforms the existing model.

scholarly journals Two Level Intrusion Detection Mechanism For Context and Trust in Wireless Sensor Network

2020 ◽  
Vol 8 (6) ◽  
pp. 65-71
Keyword(s):  
Wireless Sensor Networks ◽  
Wireless Sensor Network ◽  
Sensor Networks ◽  
Intrusion Detection ◽  
Sensor Network ◽  
Cluster Head ◽  
Base Station ◽  
Sensor Nodes ◽  
Wireless Sensor ◽  
Trust Evaluation

21st century is considered as the era of communication, and Wireless Sensor Networks (WSN) have assumed an extremely essential job in the correspondence period. A wireless sensor network is defined as a homogeneous or heterogeneous system contains a large number of sensors, namely called nodes used to monitor different environments in cooperatives. WSN is composed of sensor nodes (S.N.), base stations (B.S.), and cluster head (C.H.). The popularity of wireless sensor networks has been increased day by day exponentially because of its wide scope of utilizations. The applications of wireless sensor networks are air traffic control, healthcare systems, home services, military services, industrial & building automation, network communications, VAN, etc. Thus the wide range of applications attracts attackers. To secure from different types of attacks, mainly intruder, intrusion detection based on dynamic state context and hierarchical trust in WSNs (IDSHT) is proposed. The trust evaluation is carried out in hierarchical way. The trust of sensor nodes is evaluated by cluster head (C.H.), whereas the trust of the cluster head is evaluated by a neighbor cluster head or base station. Hence the content trust, honest trust, and interactive trust are put forward by combining direct evaluation and feedback based evaluation in the fixed hop range. In this way, the complexity of trust management is carried in a hierarchical manner, and trust evaluation overhead is minimized.

scholarly journals An Enhanced PSO-Based Clustering Energy Optimization Algorithm for Wireless Sensor Network

2016 ◽  
Vol 2016 ◽  
pp. 1-11 ◽  
Author(s):  
C. Vimalarani ◽  
R. Subramanian ◽  
S. N. Sivanandam
Keyword(s):  
Wireless Sensor Network ◽  
Sensor Network ◽  
Clustering Algorithm ◽  
Performance Metrics ◽  
Energy Optimization ◽  
Pso Algorithm ◽  
Sensor Nodes ◽  
Wireless Sensor ◽  
Target Area ◽  
Energy Conservation Measures

Wireless Sensor Network (WSN) is a network which formed with a maximum number of sensor nodes which are positioned in an application environment to monitor the physical entities in a target area, for example, temperature monitoring environment, water level, monitoring pressure, and health care, and various military applications. Mostly sensor nodes are equipped with self-supported battery power through which they can perform adequate operations and communication among neighboring nodes. Maximizing the lifetime of the Wireless Sensor networks, energy conservation measures are essential for improving the performance of WSNs. This paper proposes an Enhanced PSO-Based Clustering Energy Optimization (EPSO-CEO) algorithm for Wireless Sensor Network in which clustering and clustering head selection are done by using Particle Swarm Optimization (PSO) algorithm with respect to minimizing the power consumption in WSN. The performance metrics are evaluated and results are compared with competitive clustering algorithm to validate the reduction in energy consumption.

scholarly journals Research on coverage control algorithm based on wireless sensor network

2020 ◽  
Vol 309 ◽  
pp. 03003
Author(s):  
Jie Zhou ◽  
Mengying Xu ◽  
Yi Lu ◽  
Rui Yang
Keyword(s):  
Wireless Sensor Network ◽  
Environmental Monitoring ◽  
Sensor Network ◽  
Control Algorithm ◽  
Cluster Head ◽  
Coverage Rate ◽  
Sensor Nodes ◽  
Wireless Sensor ◽  
Coverage Control ◽  
Fuzzy Genetic

Wireless sensor network has many sensor nodes with characteristics of limited cost, collecting data, good fault tolerance and storage. It has been used in environmental monitoring, health care, military and commercial. Coverage control is a significant issue that needs to be solved in wireless sensor networks. In order to solve the problem of overlapping coverage for environmental monitoring and improve coverage rate, an improved immune fuzzy genetic algorithm (IIFGA) based on cluster head selection is proposed. the mathematical model is systematically described. In the experiments, ant colony optimization (ACO) and simulated annealing (SA) are given to compare the performance of IIFGA. The experiments show the proposed coverage control algorithm has a higher convergence speed and improve the coverage rate.

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.

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