An Energy-Efficient Protocol based on Recursive Geographic Forwarding Mechanisms for Improving Routing Performance in WSN

Abstract

In situations where a regular network may not be appropriate or is unable to function correctly, data collection and real-time transmission are commonly done using Wireless Sensor Networks (WSNs). In order to accomplish this type of operation, the network's durability and performance are extremely vital. One of the key elements that directly affect a network's lifespan and performance is the routing protocol. This study proposes a Geographic Forwarding Energy Efficient Routing Protocol (GF-EERP), an enhanced version of the Geographic Energy Aware Routing (GEAR) protocol that helps to extend the network's lifespan and performance. The introduction of node categorization, a unique technique for choosing the region head, use of multi-hop communication method and removal of dead nodes serve as the foundation of the GF-EERP. In this study, the proposed protocol's performance is also compared to other protocols already in use, like Geographic Adaptive Fidelity (GAF), Geographic Energy Aware Routing (GEAR), Greedy Perimeter Stateless Routing (GPSR) and Multihop-Geographic Energy Aware Routing (M-GEAR), using a variety of performance metrics including Network Delay, Data Delivery Ratio and Network Throughput. The simulation outcomes prove that the performance of GF-EERP, with its distinct node categorization and region head selection mechanism is superior to the existing protocols.

KEYWORDS:

• Energy efficiency
• Greedy neighbour selection
• Network parameters
• Recursive geographic forwarding
• Routing performance
• Routing protocol
• Wireless sensor network

Disclosure statement

No potential conflict of interest was reported by the author(s).

Additional information

Notes on contributors

Prasanta Pratim Bairagi

Prasanta Pratim Bairagi received the MCA degree from Tezpur University and currently pursuing his PhD in the area of wireless sensor networks from the Department of Computer Science and Engineering, Assam Down Town University. His area of interest includes MANETS, wireless sensor networks, image processing and IoT. Email: [email protected]

Mala Dutta

Mala Dutta obtained PhD degree in computer science from Gauhati University in 2013. Mala Dutta was selected as a post-doctoral research fellow in the MHRD sponsored project in the Department of Computer Science and Engineering Tezpur University, Tezpur in 2014. She has 12 years of teaching experience. Her current area of research interest is machine learning, recommender systems and network security. Corresponding author Email: [email protected]

Kanojia Sindhuben Babulal

Kanojia Sindhuben Babulal is an assistant professor in the Department of Computer Science & Engineering at Central University of Jharkhand, Ranchi, India. She received her PhD in computer science in 2012 from University of Allahabad. Her area of interest includes wireless sensor networks, MANETS, cross layer designs, 5G, and computer vision. Email: [email protected]