scholarly journals Fault Tolerance Mechanism for Internet of Things (IoT) by Solving Nature Inspired Computing Algorithm (NIC)

2021 ◽  
Vol 9 (2) ◽  
pp. 308-312
Author(s):  
A. Prasanth Rao, Et. al.
Keyword(s):  
Genetic Algorithm ◽  
Fault Tolerance ◽  
Internet Of Things ◽  
Research Work ◽  
Cluster Head ◽  
Life Time ◽  
Base Station ◽  
Sensor Nodes ◽  
Tolerance Mechanism ◽  
Network Efficiency

In clustering approach the sensor nodes are grouped to form a cluster. The nodes of a clustering network have low powered battery capability and limited processing capabilities. These nodes continuously exchange the data to cluster head which is in turn transforming the data to its base station. Few of these nodes in network may be faulty or may not support life time processing data due to its low power battery. All these sensor nodes measure the temperature, humidity, sound and pollution from environment and collected data is send to cloud for further processing. The fault tolerance mechanism of these nodes is solved by applying genetic algorithm by implementing chromosome technique to identify and avoid fault nodes in the network.  This proposed research work increases detection of fault nodes in a network, increase network efficiency, lifetime and reach energy optimization results in Internet of Things (IoT) concept. The performance evaluation shows that the data accuracy in Genetic Algorithm (GA) is higher when compared with Direct Diffusion (DD) Algorithm and Ad-hoc on demand Distance Vector (AODV) Algorithm.

An Optimal Routing Algorithm for Internet of Things Enabling Technologies

2017 ◽  
Vol 4 (3) ◽  
pp. 1-16 ◽  
Author(s):  
Amol V. Dhumane ◽  
Rajesh S. Prasad ◽  
Jayashree R. Prasad
Keyword(s):  
Genetic Algorithm ◽  
Internet Of Things ◽  
Energy Cost ◽  
High Reliability ◽  
Routing Algorithm ◽  
Optimal Path ◽  
Cluster Head ◽  
Sensor Nodes ◽  
Trust Level ◽  
Enabling Technologies

In Internet of things and its relevant technologies the routing of data plays one of the major roles. In this paper, a routing algorithm is presented for the networks consisting of smart objects, so that the Internet of Things and its enabling technologies can provide high reliability while the transmitting the data. The proposed technique executes in two stages. In first stage, the sensor nodes are clustered and an optimal cluster head is selected by using k-means clustering algorithm. The clustering is performed based on energy of sensor nodes. Then the energy cost of the cluster head and the trust level of the sensor nodes are determined. At second stage, an optimal path will be selected by using the Genetic Algorithm (GA). The genetic algorithm is based on the energy cost at cluster head, trust level at sensor nodes and path length. The resultant optimal path provides high reliability, better speed and more lifetimes.

Design of Hybridized Wireless Sensor Network using K-Means Clustering and Genetic Algorithm

2017 ◽  
Vol 2 (5) ◽  
pp. 1-6
Author(s):  
M. D. Gbolagade ◽  
R. G. Jimoh ◽  
K. A. Gbolagade ◽  
O. V. Mejabi
Keyword(s):  
Data Transfer ◽  
Cluster Head ◽  
Simulated Data ◽  
Life Time ◽  
Base Station ◽  
Sensor Nodes ◽  
Wireless Sensor ◽  
Battery Life ◽  
Cluster Heads ◽  
Main Challenge

Prolonging the network lifetime in wireless sensor networks (WSNs), Clustering has been recognized has one of the significant methods in achieving this, It entails grouping of sensor nodes into clusters and electing cluster heads (CHs) for all the clusters. CH’s accept data from relevant cluster’s nodes and forward the aggregate data to base station. A main challenge in WSNs is the selection of appropriate cluster heads. This work proposes a system that is efficient, scalable and load balanced. The proposed scheme combines two known algorithms namely k-means clustering and genetic algorithms based on the weaknesses identified in the two. The simulated data is obtained through the enhancement of clustering by the cluster head (base station) that helps in locating the nearest node that is important in the data transfer instead of transferring to a node that is not necessary, thereby wasting time and resources. The obtained simulation results indicate that this approach is efficient and last longer in elongating the battery life time than the conventional method by 60%.

Clustering Algorithm to Reduce Power Consumptions for Wireless Sensor Networks

2012 ◽  
Vol 433-440 ◽  
pp. 5228-5232
Author(s):  
Mohammad Ahmadi ◽  
Hamid Faraji ◽  
Hossien Zohrevand
Keyword(s):  
Wireless Sensor Networks ◽  
Sensor Networks ◽  
Clustering Algorithm ◽  
Cluster Head ◽  
Life Time ◽  
Base Station ◽  
Sensor Nodes ◽  
Wireless Sensor ◽  
Cluster Head Selection ◽  
Cluster Heads

A sensor network has many sensor nodes with limited energy. One of the important issues in these networks is the increase of the life time of the network. In this article, a clustering algorithm is introduced for wireless sensor networks that considering the parameters of distance and remaining energy of each node in the process of cluster head selection. The introduced algorithm is able to reduce the amount of consumed energy in the network. In this algorithm, the nodes that have more energy and less distance from the base station more probably will become cluster heads. Also, we use algorithm for finding the shortest path between cluster heads and base station. The results of simulation with the help of Matlab software show that the proposed algorithm increase the life time of the network compared with LEACH algorithm.

An Optimal Routing Algorithm for Internet of Things Enabling Technologies

2020 ◽  
pp. 522-538
Author(s):  
Amol V. Dhumane ◽  
Rajesh S. Prasad ◽  
Jayashree R. Prasad
Keyword(s):  
Genetic Algorithm ◽  
Internet Of Things ◽  
Energy Cost ◽  
High Reliability ◽  
Routing Algorithm ◽  
Optimal Path ◽  
Cluster Head ◽  
Sensor Nodes ◽  
Trust Level ◽  
Enabling Technologies

In Internet of things and its relevant technologies the routing of data plays one of the major roles. In this paper, a routing algorithm is presented for the networks consisting of smart objects, so that the Internet of Things and its enabling technologies can provide high reliability while the transmitting the data. The proposed technique executes in two stages. In first stage, the sensor nodes are clustered and an optimal cluster head is selected by using k-means clustering algorithm. The clustering is performed based on energy of sensor nodes. Then the energy cost of the cluster head and the trust level of the sensor nodes are determined. At second stage, an optimal path will be selected by using the Genetic Algorithm (GA). The genetic algorithm is based on the energy cost at cluster head, trust level at sensor nodes and path length. The resultant optimal path provides high reliability, better speed and more lifetimes.

scholarly journals FLOWER POLLINATION AND GENETIC ALGORITHM BASED OPTIMIZATION FOR NODE DEPLOYMENT IN WIRELESS SENSOR NETWORKS

2020 ◽  
Vol 5 (2) ◽  
pp. 281-293
Author(s):  
Kapil Keswani ◽  
Dr. Anand Bhaskar
Keyword(s):  
Genetic Algorithm ◽  
Dynamic Environment ◽  
Life Time ◽  
Base Station ◽  
Sensor Nodes ◽  
Wireless Sensor ◽  
Field Energy ◽  
Main Concern ◽  
Node Placement ◽  
Flower Pollination

Wireless sensor network (WSN) most popular area of research where lots of work done in this field. Energy efficiency is one of the most focusing areas because life time of network is most common issue. In the WSN, the node placement is very essential part for the proper communication between the sensor nodes and base station (BS). For better communication nodes should be aware about their own or neighbor node’s location. Better optimization of resources and performance improvement are the main concern for the WSN. Optimal techniques should be utilized to place the nodes at the best possible locations for achieving the desired goal. For node placement, flower pollination optimization and genetic algorithm are useful to generate better result. BS is responsible for the communication of nodes with each other and it should be reachable to nodes. For this Region of Interest (RoI) is helpful to choose the best location. Placement of BS in the middle is suitable place for the static nodes deployment and there should be other strategy for the dynamic environment. Nodes should be connected to each other for the transmission of data from the source to BS properly. From the MATLAB simulation, it has been shown that the proposed methodology improves the network performance in terms of dead nodes, energy remaining and various packets sent to BS.

scholarly journals Review of Energy Efficient Techniques of IoT

2019 ◽  
Vol 7 (5) ◽  
pp. 17-21 ◽  
Author(s):  
Ritesh Awasthi ◽  
Navneet Kaur
Keyword(s):  
Energy Consumption ◽  
Internet Of Things ◽  
Energy Efficient ◽  
Research Work ◽  
Base Station ◽  
Sensor Nodes ◽  
Dynamic Nature ◽  
Work Related ◽  
Reduce Energy Consumption ◽  
The Internet Of Things

The network across which the information is sensed by the sensor devices and then forwarded to the sink is known as Internet of Things (IoT). Even though this system is deployed in several applications, there are certain issues faced in it due to its dynamic nature. The internet of things is derived from the wireless sensor networks. The sensor nodes which are deployed to sense environmental conditions are very small in size and also deployed on the far places due to which energy consumption is the major issue of internet of things. This research work related to reduce energy consumption of the network so that lifetime can be improved. In the existing system the approach of multilevel clustering is used for the data aggregation to base station. In the approach of multilevel clustering, the whole network is divided into clusters and cluster heads are selected in each cluster. The energy efficient techniques of internet of things are reviewed and analyzed in terms of certain parameters.

scholarly journals Detection of Compromised Nodes in Wireless Sensor Networks using GPSR Protocol and Iterative Filtering Algorithm

2016 ◽  
Vol 1 (3) ◽  
pp. 141-148
Author(s):  
R. Ramalakshmi ◽  
S. Subash Prabhu ◽  
C. Balasubramanian
Keyword(s):  
Sensor Node ◽  
Cluster Head ◽  
Life Time ◽  
Base Station ◽  
Sensor Nodes ◽  
Wireless Sensor ◽  
Surrounding Area ◽  
Filtering Algorithm ◽  
Iterative Filtering ◽  
Time And Energy

The sensor network is used to observe surrounding area gathered and spread the information to other sink. The advantage of this network is used to improve life time and energy. The first sensor node or group of sensor nodes in the network runs out of energy. The aggregator node can send aggregate value to the base station. The sensor node can be used to assign initial weights for each node. This sensor node calculates weight for each node. Which sensor node weight should be lowest amount they can act as a cluster head. The joint node can send false data to the aggregator node and then these node controls to adversary. The dependability at any given instant represents an comprehensive behavior of participate to be various types of defects and misconduct. The adversary can send information to aggregator node then complexity will be occurred. These nodes are used to reduce the energy and band width.

scholarly journals Improved LEACH protocol using cache nodes for wireless sensor network

2018 ◽  
Vol 7 (2.27) ◽  
pp. 138
Author(s):  
Kamini Joshi ◽  
Sandeep Singh Kang
Keyword(s):  
Energy Consumption ◽  
Wireless Sensor Network ◽  
Sensor Network ◽  
Research Work ◽  
Cluster Head ◽  
Base Station ◽  
Sensor Nodes ◽  
Wireless Sensor ◽  
Energy Distance ◽  
Leach Protocol

The wireless sensor network is the decentralized type of network which can sense information and pass it to base station. The energy consumption is the major issue of WSN due to small of sensor nodes and far deployment of the network. The clustering is the efficient approach to increase lifetime of the sensor network. In the approach of clustering cluster head are selected for the data aggregation. The fuzzy logic rules are derived based on node energy, distance to base station for the cluster head selection, which increase lifetime of sensor nodes in the existing system. In this research work, cache nodes are deployed in the network which reduce energy consumption of WSN. In the proposed approach cluster head send data to cache nodes and it will forward data to base station. The simulation is performed in MATLAB and proposed technique performs well in terms of number of packets transmitted, number of dead nodes, network lifetime, throughput and remaining energy.  

scholarly journals INCREASING THE LIFETIME OF HETEROGENEOUS SENSOR NETWORK BY USING GENETIC-FUZZY CLUSTERING

2013 ◽  
pp. 185-189
Author(s):  
SEYED MOHAMMAD ABEDINI ◽  
ABBAS KARIMI
Keyword(s):  
Genetic Algorithm ◽  
Fuzzy Logic ◽  
Wireless Sensor Network ◽  
Sensor Network ◽  
Cluster Head ◽  
Base Station ◽  
Sensor Nodes ◽  
Wireless Sensor ◽  
Process Data ◽  
To Receive

Wireless sensor network is composed of hundreds or thousands of sensor nodes which have computational, energy and memory limitation. Its duty is to receive information from its surrounding environment, analyze and process data and to send the received data to other nodes or base station. In these networks, sensor nodes are dependent on low power batteries to provide their energy. As energy is a challenging issue in these networks, clustering models are used to overcome this problem. In this paper, fuzzy logic and genetic algorithm are combined to increase the lifetime of the wireless sensor network. In other words, fuzzy logic is used to introduce the best nodes, those that in comparison to other nodes have more energy, density and centrality, to base station as cluster head candidate. Then, the number and place of cluster heads are determined in base station by using genetic algorithm. Also, the network acts heterogeneously and includes several nodes with different parameters.

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.

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