Sehrish Saleem

Work place: Muhammad Nawaz Sharif University of Engineering & Technology Multan Pakistan

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Research Interests: Computer systems and computational processes, Artificial Intelligence, Distributed Computing, Data Structures and Algorithms

Biography

Sehrish Saleem did Bachelor of Science in Computer Science from NFC Institute of Engineering and Technology Multan and MPhil in Computer Science from National College of Business Administration & Economics Lahore Pakistan in 2013 and 2016, respectively. She is serving as a Lecturer in Department of Computer Science at Muhammad Nawaz Sharif University of Engineering & Technology Multan Pakistan. She focused on Software Engineering and her research interest includes Big Data Analytics, distributed Computing, Artificial Intelligence and machine learning.

Author Articles
Sagacious Communication Link Selection Mechanism for Underwater Wireless Sensors Network

By Shahzad Ashraf Sehrish Saleem Tauqeer Ahmed

DOI: https://doi.org/10.5815/ijwmt.2020.04.03, Pub. Date: 8 Aug. 2020

In underwater environment, the sensor nodes are deployed for collecting information and sending back to the base station. Establishing astute communication link among these sensor nodes in a multi-link routing environment is a key challenge for all underwater routing protocols. A sagacious communication link can only guarantee the maximum data transfer rate. The link selection mechanism of three underwater routing protocol i.e, Energy-aware Opportunistic Routing (EnOR) protocol, Shrewd Underwater Routing Synergy using Porous Energy Shell (SURS-PES) and Underwater Shrewd Packet Flooding Mechanism (USPF) have been investigated. After analyzing performance results of these protocols interms of packet delivery ratio, end-to-end ‎delay, network lifespan and energy consumption using NS2 with AquaSim 2.0 simulator. The protocol existing, with sagacious link selection mechanism in multi-link routing environment has been identified. The identification of this sagacious link selection mechanism is a novel approach which can give specific knowledge for targeted output without wasting resources for irrelevant objectives.

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