An improved dynamic deployment technique based-on genetic algorithm (IDDT-GA) for maximizing coverage in wireless sensor
- PDF / 4,145,739 Bytes
- 18 Pages / 595.276 x 790.866 pts Page_size
- 95 Downloads / 171 Views
ORIGINAL RESEARCH
An improved dynamic deployment technique based‑on genetic algorithm (IDDT‑GA) for maximizing coverage in wireless sensor networks Hanaa ZainEldin1 · Mahmoud Badawy1 · Mostafa Elhosseini1,2 · Hesham Arafat1 · Ajith Abraham3 Received: 29 July 2019 / Accepted: 5 January 2020 © Springer-Verlag GmbH Germany, part of Springer Nature 2020
Abstract Recently, many researchers have paid attention to wireless sensor networks (WSNs) due to their ability to encourage the innovation of the IT industry. Although WSN provides dynamically scalable solutions with various smart applications, the growing need to maximize the area coverage with decreasing the percentage of deployed sensor nodes is still required. Random deployment is preferable for large areas that require a maximal number of nodes but result in coverage holes. As a result, mobile nodes are used to reduce coverage holes and maximize area coverage. The main objective of this study is to present an Improved Dynamic Deployment Technique based-on Genetic Algorithm (IDDT-GA) to maximize the area coverage with the lowest number of nodes as well as minimizing overlapping area between neighboring nodes. A two-point crossover novel is introduced to demonstrate the notation of variable-length encoding. Simulation results reveal that the superiority of the proposed IDDT-GA compared with other state-of-the-art techniques. IDDT-GA has better coverage rates with 9.69% and a minimum overlapping ratio with 35.43% compared to deployment based on Harmony Search (HS). Also, IDDT-GA has minimized the network cost by 13% and 7.44% than Immune Algorithm (IA) and Whale Optimization Algorithm (WOA) respectively. Besides, it confirms its stability with 83.04% compared to maximizing coverage with WOA. Keywords Coverage · Deployment techniques · Genetic algorithm (GA) · Whale optimization algorithm (WOA) · Wireless sensor network (WSN) · Quality of service (QoS)
1 Introduction * Hanaa ZainEldin [email protected] Mahmoud Badawy [email protected] Mostafa Elhosseini [email protected] Hesham Arafat [email protected] Ajith Abraham [email protected] 1
Computers Engineering and Control Systems Department, Faculty of Engineering, Mansoura University, Mansoura 35516, Egypt
2
College of Computer Science and Engineering, Taibah University, Yanbu 30 012, Saudi Arabia
3
Machine Intelligence Research Labs (MIR Labs) Scientific Network for Innovation and Research Excellence, Auburn, USA
Wireless sensor networks (WSNs) (Ezhilarasi and Krishnaveni 2018) are a group of sensor nodes with limited processing and low power capacity (Su and Zhao 2017). These nodes are spatially scattered in an ad-hoc manner for collecting physical information from the surrounding environment and relaying collected data to the sink node as well. Different environmental conditions can be recorded by WSNs such as sound, wind, pressure, room temperature, humidity, and pollution level. On other hands, WSNs heavily affect realtime applications (Sengupta et al. 2013), which c
Data Loading...
