Reza MotahariNasab

Work place: Faculty of Computer Engineering, University of Isfahan, Isfahan, Iran

E-mail: rezamotahari68@eng.ui.ac.ir

Website:

Research Interests: Computer Networks, Network Architecture, Network Security

Biography

Reza MotahariNasab, received the BS degrees in Computer engineering from the Payam-e-Noor’s Isfahan University, Iran in 2012. He is now completing the MSC degree at the University of Isfahan, Iran. His research interests include wireless communication and wireless sensor networks.

Author Articles
An Energy-Aware Data-Gathering Protocol Based on Clustering using AUV in Underwater Sensor Networks

By Reza MotahariNasab Ali Bohlooli Neda Moghim

DOI: https://doi.org/10.5815/ijcnis.2016.12.05, Pub. Date: 8 Dec. 2016

Underwater Wireless Sensor Networks (UWSNs) consist of certain number of sensors and vehicles interacting with each other to collect data. In recent years, the use of Autonomous Underwater Vehicle (AUV) has improved the data delivery ratio and maximized the energy efficiency in UWSNs. Clustering is one of the effective techniques in energy management which increases the lifetime of these networks. One of the most important parameters in creating optimized clusters is the choice of appropriate cluster head (CH), which not only increases the lifetime of the network and the received data in the sink, but also reduces energy consumption. Clustering of networks was primary done via distributed methods in previous researches. It spends too much energy and also involves too many nodes in the clustering process and fades their main functionality, which is gathering data in sensor networks. It also causes more damping of the network. However, in the proposed protocol, instead of having them distributed by the network and the nodes, the stages of clustering and selecting the appropriate CH is the task of the AUV (Autonomous Underwater Vehicle). Since all the necessary measures to cluster in the network will be carried out by the AUV by this method, many control overheads in the process of clustering the network will be removed and energy consumption caused by nodes reduces significantly. With this method, the network scalability will also be manageable and under control. For simulating and implementing our method we mainly used the OPNET software. The results show that energy consumption of nodes in the proposed algorithm has been significantly improved compared to previous results.

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