Basic Informations

C.V

Doha Mostafa Abdelazim Mahmoud Contact Information Address: Nasser, Beni _Suef, Egypt Mobile: 01147651962 Email: dohamostafahekal1995@gmail.com dohamostafa@fcis.bsu.edu.eg Education Reference 2019-2023 • Master, Faculty of computer science and information system. Information Technology department. Helwan University, Egypt. • Thesis Title: A New Energy-Efficient Multi-Hop Routing Protocol for Heterogeneous Wireless Sensor Networks 2017-2018 • Premaster, Faculty of computer science and information system. Information Technology department. Helwan University, Egypt. 2013-2017 • B.Sc. Information Systems, Faculty of Computers and Information (Beni _Suef University). • Grade: Excellent (GPA: 3.74). • Major: Information Technology. • Graduation project grade: Excellent Work Experience 2018 till now: • Teaching assistant at faculty of computer science Beni Suef University, Information Technology department. Technical Skills Network simulation tool: • Opnet • Cisco packet tracer Web technologies: • HTML • CSS • Asp.net Programming Skills: • C++. • C# • Matlab Database: • SQL. Graduation Project: • Simulation of FCAI network with OPNET Tool and cloud computing services • Graduation project grade: Excellent Summer Training • Networking course in Beni_suif University. • ASP.net course in FCI Beni_suif University. • Telecommunication training in TE-DATA Company. Languages Arabic: Native Language. English: Good Personal Skills • Team Leader • Good Communicator • Adaptability in a hard working environment Personal Information Birth date: 13/8/1995 Nationality: Egyptian. Resident of: Nasser, Beni _Suef, Egypt Marital Status: Married. References Available upon request.

Master Title

A New Energy-Efficient Multi-Hop Routing Protocol for Heterogeneous Wireless Sensor Networks

Master Abstract

Major goals of routing optimization approaches for heterogeneous wireless sensor networks (HWSNs) are to optimize the use of sensor nodes' power and prolong the lifetime of HWSNs. Most routing techniques work on WSNs with static nodes, so node positions are known, and once formed, the cluster remains constant throughout the network's lifetime. However, the challenge with WSN dynamic node positions is that node positions change dynamically, requiring cluster formation changes and routing techniques to handle node positions. In the proposed multi-hop routing protocol using moving HWSNs nodes.The challenge is to dynamically control cluster formation and CHs selection so that the routing protocol can deal with changes in node location and energy. A new energy-efficient multi-hop routing protocol for HWSNs with sensor node mobility is proposed, with the main goal of improving HWSNs performance by minimizing energy consumption, extending network lifetime, and eliminating redundancy in sensor data to improve throughput. The proposed multi-hop routing protocol is based on the Grey Wolf Optimizer (GWO) and Tabu Search Algorithm (TSA). The proposed routing protocol consists of three phases. First phase: GWO technique used for nodes deployment and cluster formation since clusters dynamically changes because of nodes mobility. Second phase: cluster head (CH) selection by GWO using a fitness function depends on the sensor nodes' residual energy and the average distance between the CH and the base station (BS) because of node mobility. Third phase: optimal route selection from CHs to BS by TSA based on forwarding reliable route packets (FRRPs). Data aggregation is another important issue for HWSNs, as it eliminates data redundancy and improves the accuracy of collected data. Sensory data is often noisy and redundant, so the data is aggregated to extract important information and save on transmission costs. Data aggregation technology attempts to decrease communication time between sensor nodes, reduce energy consumption, and extend the lifespan of HWSNs. In this thesis, a data aggregation scheme is proposed that uses a Kalman filter (KF) at each node to filter data before sending it to the CH and an extreme learning machine (ELM) on each CH to transmit aggregated data to the BS. By decreasing network power consumption, prolonging network lifetime, and transferring aggregated and accurate data from CHs to BS. The routing optimization protocol and data aggregation scheme improve the overall HWSN's performance. The proposed GWO-TSA with data aggregation scheme improved QoS attributes like reliability, scalability, and dynamic cluster formation. This was enhanced by using GWO with a fitness function based on nodes' remaining energy and distance to BS, and load balancing was improved by discovering numerous optimum data transmission paths from CHs to BS and selecting the best route based on forwarding reliably routed packets (FRRPs) through the TSA algorithm. Experiments demonstrate that the proposed GWO-TSA routing optimization protocol reduces HWSNs' power consumption by 34%, 23%, and 20%, increases lifetime by 35%, 24%, and 21%, and improves throughput by 32%, 26%, and 20%. Over GWO-CSO (Grey Wolf Optimizer with Crow Search Optimization), GA-TSA (Genetic Algorithm with Tabu Search Algorithm),and RBMRP (Region-Based Mobile Routing Protocol) respectively. The performance of the four compared protocols in terms of routing optimization with a data aggregation scheme shows that GWO-TSA improved the energy consumption of HWSNs by 39%, 29%, and 23%, increased lifetime by 38%, 30%, and 24%, and enhanced network throughput by 41%, 33%, and 25%. The three protocols, GWO-CSO, GA-TSA, and RBMRP, were compared. Finally, routing optimization with a data aggregation scheme achieves the fundamental goals of increasing the performance of HWSNs by conserving power consumption, extending network lifetime, and eliminating redundancy in sensory data.

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