Optimal Sensor Deployment in Internet of Things based Wireless Sensor Network for Irrigation Management System

In recent years, several applications are found to be exploiting under Wireless Sensor Networks (WSNs), and more particularly civil and military applications. However, Target Coverage (TCOV) and Network Connectivity (NCON) are found to be the most crucial issues that have to be resolved to attain effective robust data communication and environmental sensing in WSN. This paper has made an attempt to propose a new effective sensor deployment model in the application of the irrigation management system in agriculture sector. The data logger (data collector) IoT devices are typically placed over the field, which does the communication one another. The main point of this design is the IoT device TCOV and NCON, and the real-time issue related to this design is the device mobility that consumes more power thereby minimizes the lifetime of network. The proposed model intends to solve the comprised NCON and TCOV with the aid of Euclidean Spanning Tree Model (ECST). Further, this paper introduces a new Fitness Interrelated Whale Optimization Algorithm (FI-WOA) that insisted in the minimum movement of mobile sensors over the network. This novel characteristic of sensor deployment model would create the effectual impacts in the irrigation management system. Further, the adopted ECST-WOA model is compared with conventional models and the results attained from the execution demonstrate the enhanced performance of the implemented technique.

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Field irrigation management system using Wireless Sensor Network

2011 Nirma University International Conference on Engineering ◽

10.1109/nuicone.2011.6153317 ◽

2011 ◽

Cited By ~ 7

Author(s):

Jignesh B. Patel ◽

Chetan B. Bhatt ◽

Bhargav Patel ◽

Kunal Parwani ◽

Chirag Sohaliya

Keyword(s):

Wireless Sensor Network ◽

Sensor Network ◽

Management System ◽

Irrigation Management ◽

Wireless Sensor

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Cluster-based optimal wireless sensor deployment for structural health monitoring

Structural Health Monitoring ◽

10.1177/1475921717689967 ◽

2017 ◽

Vol 17 (2) ◽

pp. 266-278 ◽

Cited By ~ 9

Author(s):

Kun Fang ◽

Chengyin Liu ◽

Jun Teng

Keyword(s):

Wireless Sensor Network ◽

Structural Health Monitoring ◽

Sensor Network ◽

Health Monitoring ◽

Data Communication ◽

Base Station ◽

Wireless Sensor ◽

Sensor Deployment ◽

Structural Health ◽

Cluster Heads

A well-designed wireless sensor deployment method not only directly influences the number of deployed sensors and data accuracy, but also influences on network topology. As most of the energy cost comes from the transmission and receiving of data packets, clustering optimization in wireless sensor network becomes an important issue for energy-efficient coordination among the densely deployed nodes for data communication. In a typical hierarchical wireless sensor network, total intra-cluster communication distance and total distance of cluster heads to base station depend on number of cluster heads. This work presents a novel approach by selecting the number of clusters in hierarchical wireless sensor network. We analyze and demonstrate the validity of the cluster optimization for wireless sensor deployment using an example of a numerically simulated simply supported truss, in terms of efficient use of the constrained wireless sensor network resources. Followed by a cluster-based optimization framework, we show how to adopt our approach to achieve scalable and efficient deployment, through a comprehensive optimization study of a realistic wireless structural health monitoring system. Finally, we suggest optimal deployment scheme based on the comparative performance evaluation results in the case study.

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Firefly Algorithm for Intelligent Context-Aware Sensor Deployment Problem in Wireless Sensor Network

Journal of Circuits System and Computers ◽

10.1142/s0218126619500944 ◽

2019 ◽

Vol 28 (06) ◽

pp. 1950094 ◽

Cited By ~ 5

Author(s):

Puri Vishal ◽

A. Ramesh Babu

Keyword(s):

Low Cost ◽

Network Connectivity ◽

Sensor Nodes ◽

Wireless Sensor ◽

Sensor Deployment ◽

Second Phase ◽

Intelligent Sensor ◽

Bee Colony ◽

Improved Performance ◽

Effective Network

Wireless sensor networks (WSNs) provide acceptable low cost and efficient deployable solutions to execute the target tracking, checking and identification of substantial measures. The primary step necessary for WSN is to organize all the sensor nodes in their positions to build up an effective network. In the sensor deployment model, Target COVerage (TCOV) and Network CONnectivity (NCON) are the basic issues in WSNs that have obtained significant consideration in sensor deployment. This paper intends to develop an intelligent context awareness algorithm for sensor deployment process in WSN. Accordingly, the process is divided into two phases. In the first phase, the TCOV process is performed, whereas the second phase of the algorithm establishes NCON among the sensors. An objective model to meet both TCOV and NCON is formulated as a minimization problem. The problem is solved using FireFly (FF) optimization to determine the optimal locations for sensors. It leads to an intelligent sensor deployment model that can determine the optimal locations for the sensors in the WSN. Further, the proposed FF-TCOV and FF-NCON models are compared against the conventional algorithms, namely, genetic algorithm, particle swarm optimization, artificial bee colony, differential evolution and evolutionary algorithm-based TCOV and NCON models. The results achieved from the simulation show the improved performance of the proposed technique.

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Deployment of Context-Aware Sensor in Wireless Sensor Network Based on the Variants of Genetic Algorithm

International Journal of Artificial Life Research ◽

10.4018/ijalr.2018070101 ◽

2018 ◽

Vol 8 (2) ◽

pp. 1-24

Author(s):

Puri Vishal ◽

Ramesh Babu A.

Keyword(s):

Genetic Algorithm ◽

Network Connectivity ◽

Sensor Nodes ◽

Wireless Sensor ◽

Sensor Deployment ◽

Adaptive Genetic Algorithm ◽

Main Concern ◽

Context Aware ◽

Pollution Levels ◽

Effective Network

Wireless sensor networks (WSNs) are generally a group of spatially scattered and devoted sensors to record and monitor the physical environmental condition, and the collected data is grouped at a central location. In fact, the environmental conditions such as sound, humidity, temperature, wind, pollution levels, etc., can be clearly determined by WSNs. The principal objective of WSNs is to organize the whole sensor nodes in their related positions, thereby developing an effective network. In WSNs, target COVerage (TCOV) and Network CONnectivity (NCON) are the main concern of the sensor deployment problem. Many research works aspire the evolvement of smart context awareness algorithm for sensor deployment issues in WSN. Here the TCOV and NCON process are deployed as the minimization problem. This article makes an analysis of different GA variations in attaining the objective. The GA variations are as follows: self-adaptive genetic algorithm (SAGA), deterministic-adaptive genetic algorithm (DAGA), Individual- Adaptive Genetic Algorithm (IAGA). Finally, the methods are compared to one another in terms of connectivity and coverage performance.

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