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

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

Puri Vishal (Sinhgad College of Engineering, Pune, India) and Ramesh Babu A. (Hindustan Institute of Technology and Science, Chennai, India)
Copyright: © 2018 |Pages: 24
DOI: 10.4018/IJALR.2018070101

Abstract

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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1. Introduction

Wireless sensor network (WSN) refers to a collection of spatially dispersed and dedicated sensors for monitoring and recording the physical conditions of the environment and categorize the collected data at a central location. WSNs measure environmental conditions like temperature, sound, pollution levels, humidity, wind, and so on. Most of the modern networks are bi-directional, also enabling control of sensor activity. Sensor is said to be a small, cheap and low-intensity multi-resourceful device that have the characteristics of communication, sensing, and processing. WSN (Elshrkawey et al., 2018) have thousands of sensor nodes in a distributed environment, and it is developed for controlling and verifies the natural characteristics of the sensor network. WSN is still on improving and is said to be a popular technology for checking and to work on the risky events on humans. The local sensor data that is got from the sensor node is then moved to the sink, and hence it acts as a remote base station. The surveillance can be achieved effectively when a sufficient amount of sensor coverage is available in WSN (Wang et al., 2018) (Luo et al., 2018) (Sherifi& Senja, 2015) (Mahammad & Rao, 2015). Sensor failure occurs when they positioned the static nodes physically, and thereby it causes a deficiency in network self-healing competence.

Sensor nodes are placed randomly or physically in a WSN application (Nagaraju et al., 2018) (Zhang & Li, 2017). While placing physically, the number of sensor node needed is very simple to find and thus satisfies the targeted connectivity, reliability, coverage, delay, etc. But in random deployment (Kong et al., 2012) (Deif & Gadallah, 2014) (Parrado-García et al., 2017) (Boubrima et al., 2017), the number of sensor nodes that are required is a complex one to found out. The nodes said to have restricted communication capabilities because the coverage in source node should be within the maximum transmission range. Through the intermediate nodes, they transmit messages to their destinations. The most important performance metrics is the Coverage and Connectivity (Joshi & Younis, 2016). Thereby they are employed in achieving the optimal solution for the limited resources. The combined metric is said to be a connected coverage if both the symmetries attain within a definite targeted degree. Connected coverage estimates how much of sensed data with the derived information gets transmitted to the sink.

Numerous methods are implemented for the sensor deployment in WSN (Li et al., 2015; Otero et al., 2010; Ates et al., 2017; Susila & Arputhavijayaselvi, 2015). An improved particle swarm algorithm is used in the situations like uneven distribution and incomplete coverage of nodes by accepting the cloud model in the deployment methods. The coverage maximization problem (CMP) can also be implemented because of the simplicity in sensor nodes. Some other algorithms are there including Range-based localization algorithms have been developed for calculating the nodes spaces between them by utilizing Time of Arrival (ToA), Angle of Arrival (AoA), Time Difference of Arrival (TDoA) and Received Signal Strength Indicator (RSSI). Similarly, Connected Dominating Set (CDS) is the first algorithm that proposed to analyze the virtual backbone, and the efficiency of energy is improved by using the CDS based topology control (TC) in WSN (Akila & Venkatesan, 2016; Zhou et al., 2017). It is subdivided as topology construction and topology maintenance. Very-large-scale integration (VLSI) and wireless radio technologies are still on improving in the field of the embedded sensor, and hence it is simple to implement in the field of application (Madhuri et al, 2017), and the cost is considerably low. Connected k-coverage working sets construction approach (CWSC) is presented to work on connectivity and k-coverage.

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