Deep learning; Fixed-variant search; Network lifespan; Survivability network; Wireless sensor network; Dominant set; Energy; Graph neural networks; Lifespans; Minimal dominant set; State vector; Survivability networks; Electrical and Electronic Engineering
Abstract :
[en] Inadequate energy of sensors is one of the most significant challenges in the development of a reliable wireless sensor network (WSN) that can withstand the demands of growing WSN applications. Implementing a sleep-wake scheduling scheme while assigning data collection and sensing chores to a dominant group of awake sensors while all other nodes are in a sleep state seems to be a potential way for preserving the energy of these sensor nodes. When the starting energy of the nodes changes from one node to another, this issue becomes more difficult to solve. The notion of a dominant set-in graph has been used in a variety of situations. The search for the smallest dominant set in a big graph might be time-consuming. Specifically, we address two issues: first, identifying the smallest possible dominant set, and second, extending the network lifespan by saving the energy of the sensors. To overcome the first problem, we design and develop a deep learning-based Graph Neural Network (DL-GNN). The GNN training method and back-propagation approach were used to train a GNN consisting of three networks such as transition network, bias network, and output network, to determine the minimal dominant set in the created graph. As a second step, we proposed a hybrid fixed-variant search (HFVS) method that considers minimal dominant sets as input and improves overall network lifespan by swapping nodes of minimal dominating sets. We prepared simulated networks with various network configurations and modeled different WSNs as undirected graphs. To get better convergence, the different values of state vector dimensions of the input vectors are investigated. When the state vector dimension is 3 or 4, minimum dominant set is recognized with high accuracy. The paper also presents comparative analyses between the proposed HFVS algorithm and other existing algorithms for extending network lifespan and discusses the trade-offs that exist between them. Lifespan of wireless sensor network, which is based on the dominant set method, is greatly increased by the techniques we have proposed.
Disciplines :
Computer science
Author, co-author :
Sivakumar, Nithya Rekha; Department of Computer Sciences, College of Computer and Information Science, Princess Nourah bint Abdulrahman University, Riyadh, Saudi Arabia
NAGARAJAN, Senthil Murugan ; University of Luxembourg > Faculty of Science, Technology and Medicine (FSTM) > Department of Mathematics (DMATH)
Devarajan, Ganesh Gopal ; Department of Computer Science and Engineering, SRM Institute of Science and Technology, Uttar Pradesh, India
Pullagura, Lokaiah ; Department of Computer Science & Engineering, JAIN University, Bangalore, India
Mahapatra, Rajendra Prasad; Department of Computer Science and Engineering, SRM Institute of Science and Technology, Uttar Pradesh, India
External co-authors :
yes
Language :
English
Title :
Enhancing network lifespan in wireless sensor networks using deep learning based Graph Neural Network
University Grants Commission Princess Nourah Bint Abdulrahman University Department of Science and Technology, Ministry of Science and Technology, India
Funding text :
Authors thanks to Princess Nourah bint Abdulrahman University, Riyadh, Saudi Arabia, for its support (Project Number PNURSP2022R194).Nithya Rekha Sivakumar received a grant from UGC and completed Ph.D., as a Full-Time Research Scholar in Periyar University awarded with ”UGC BSR Research Fellowship in Science for Meritorious Students” by University Grants Commission, New Delhi, Govt. of India from 2010 to 2013. She received Travel Grant from Department of Science and Technology (DST), Govt. of India to go to United Stated of America for her Ph.D. research. She received the ”BEST DISTINGUISHED RESEARCHER AWARD” for the year 2015-2016 from the College of Computer, Qassim Private Colleges, Buraydah, Kingdom of Saudi Arabia. She has received Research grant for Research Identity Fast-track Funding Program, from Deputyship for Research & Innovation, Ministry of Education and Researchers Supporting Project Award from Princess Nourah bint Adbulrahman University in Saudi Arabia. She is a Panel of External Examiner for Ph.D. more than 15 candidates have awarded Ph.D. She is the author of over 35 peer-reviewed articles. She has also 3 Patents. She is also the reviewer in reputed journal like IEEE Access and Springer. She is supervising several graduate (B.S. and M.S) students. Her research interests include the Mobile Computing, Artificial Intelligence, Internet of Things, Deep Learning, Machine Learning, Wireless networks, Network/Cyber security, Blockchain, VANET, Cognitive Radio Networks and Cloud computing. She is currently an Associate Professor in the Department of Computer Sciences, College of Computer and Information Sciences, Princess Nourah Bint Abdulrahman University, Riyadh, Saudi Arabia.
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