Mohamed Elshaikh Elobaid Said Ahmed
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Preferred name
Mohamed Elshaikh Elobaid Said Ahmed
Official Name
Mohamed Elshaikh Elobaid , Said Ahmed
Alternative Name
Ahmed, Mohamed Elshaikh Elobaid Said
Said Ahmed, Mohamed Elshaikh Elobaid
Elobaid, Mohamed Elshaikh
Main Affiliation
Scopus Author ID
57190012447
Researcher ID
R-7502-2019

Research Output
Now showing 1 - 6 of 6
  • Publication
    Real-time drowsiness detection system for driver monitoring
    (IOP Publishing, 2020)
    M Arunasalam
    ;
    Naimah Yaakob
    ;
    Amiza Amir
    ;
    Mohamed Elshaikh Elobaid Said Ahmed
    ;
    N F Azahar
    Nowadays, the rate of road accidents due to microsleep has been alarming. During microsleep, people might doze off without realizing it. For many decades, drowsiness detection system for vehicles was not among the major concerns though it turns out as one of imperative features that could have avoid microsleep and thus should be implemented in all vehicles in order to ensure safety of drivers and other vehicles on the road. To the best of our knowledge, enforcements on driving restriction during drowsiness state is yet to be implemented. The absence of such system in the current transportation systems expose drivers to great danger especially at night because accidents are highly likely to happen at night due to drowsy and fatigue drivers. Therefore, this project proposes a real-time drowsiness detection system for vehicles, featuring ignition lock to reduce accidents. An eye blink sensor is embedded in a wearable glasses and heart beat sensor is used to detect drowsiness level of drivers. The system also includes SMS notification system to relatives or friends with location details of the drowsy driver. This project is able to detect and react based on 3 levels of drowsiness by alerting the driver through buzzer. Ignition lock will be applied when high level of drowsiness is detected. Consequently, the vehicle will be slowed down and eventually stopped when dangerous level of drowsiness is detected as a safety precaution.
  • Publication
    Velocity Based Performance Analysis of GreedLea Routing Protocol in Internet of Vehicle (IoV)
    ( 2024-12-01)
    Normaliza Omar
    ;
    Naimah Yaakob
    ;
    Mohamed Elshaikh Elobaid Said Ahmed
    ;
    Zulkifli Husin
    ;
    Iszaidy Ismail
    ;
    Alaa KY Dafhalla
    Intelligent routing protocols for IoV have also been made possible by the convergence of IoT and machine learning algorithm. In order to make informed routing decisions, these intelligent routing protocols examine data gathered from IoT devices like vehicle sensors and traffic monitoring systems using machine learning algorithms. Moreover, as the number of vehicles increases and road networks become more complex, traditional routing protocols for ad hoc networks are being replaced by more advanced and efficient protocols. The purpose of this study is to concentrate on these unique qualities of IoVs network scenario. A combined routing method has been developed to construct periodic connectivity and find routes on-demand in order to save route data as graphs. The simulation's findings show that GreedLea routing protocol outperforms GPSR and AODV routing protocols in terms of delay and packet delivery ratio (PDR). The results demonstrate that the average AODV latency is significantly higher when there are fewer vehicles on the network. This is due to the fact that connections are frequently lost at higher speeds and lower densities, and re-establishing new channels takes a lot of time. As the number of vehicles rises, efficiency improves and the wait gets shorter. The average latency, yet, keeps increasing as vehicle density increases due to the additional overheads related with routing.
      3  28
  • Publication
    Design and Development of GreedLea Routing Protocol for Internet of Vehicle (IoV)
    ( 2020-03-20)
    Normaliza Omar
    ;
    Naimah Yaakob
    ;
    Zulkifli Husin
    ;
    Mohamed Elshaikh Elobaid Said Ahmed
    In Internet of Vehicle (IoV), each vehicle uses a routing protocol to find a path for sending its messages to the last destination. Nowadays, the studies of IoV routing protocols and their impact on the performances of network with different network scenarios has significantly developed a precise understanding of the requirements and goals for designing an IoV routing protocol. In IoV, topology of network diverse promptly which leads to the fragmentation of network, frequent route breakage, and packet loss. This paper discusses on the development of an integrated routing protocol for IoV scenario. Greedy Perimeter Stateless Routing (GPSR) and Reinforcement Learning (RL) is integrate to determine a route based on demand. Then, the mobility model has been designed to reduce road collision. Lastly, traffic management also been focused to deal with the loss, mobility and network delay to meet the application demands.
      4  30
  • Publication
    GPSR Routing Performances Enhancement for VANET networks with Taguchi Optimization Mechanism
    ( 2021-07-26)
    Mohamed Elshaikh Elobaid Said Ahmed
    ;
    Dafhalla A.K.Y.
    ;
    Naimah Yaakob
    ;
    Hasimah Ali
    Routing mechanism plays an important role in the performances of Vehicular Ad Hoc Networks (VANET). Hence, various routing mechanisms are proposed to enhance VANET performances, however few researches are dedicated to optimize these routing mechanisms. In this paper an optimization mechanism is proposed to improve the performances of Greedy Perimeter stateless Routing (GPSR) protocol. Design of Experiments is used along with Taguchi Optimization method to fine tune GPSR internal routing parameters against VANET network scenarios. The target of optimization in this work is set to network performances including network throughput, delay and packet delivery ration (PDR). These targets are mathematically combined to form a single optimization target. A simulation experiments are performed to evaluate VANET performances. Obtained results showed that the proposed optimization improves the VANET performances in terms of throughput, PDR and delay. Further real-time integration of Optimization and routing mechanism can improve network performances.
      1
  • Publication
    MOVE Mobility Model in GreedLea Routing Protocol for Internet of Vehicle (IoV) Network
    ( 2021-07-26)
    Normaliza Omar
    ;
    Naimah Yaakob
    ;
    Mohamed Elshaikh Elobaid Said Ahmed
    ;
    Zulkifli Husin
    Internet of Vehicles (IoV) is a broad variety of mobile transmission purposes for file sharing [l]-[5]. There are still debates on the viability of purposes using end to end multi-hop communication, since the significant number of high mobility nodes involved in the networks. The main issue is the efficiency of IoV routing protocols in cities and highways can meet the ideal delay and throughput for such purposes. In particular, it is not usually a challenge to locate a node to hold a message in urban daytime situations, where vehicles are tightly packed. Since fewer number of vehicles are running in highway scenarios and cities at night, and it might not be possible to set up end-to-end roads. In general, each protocol offered a performance evaluation in contradiction of some other protocols, giving considerable importance to a detailed performance evaluation of each protocol type. After such an assessment, it was found that geocast routing would perform best in urban areas. GreedLea routing protocol is develop to overcome the current routing protocol drawback. The development of GreedLea routing protocol involved Greedy Perimeter Stateless Routing (GPSR) and reinforcement learning method in order to deliver better performance compared to current existing routing protocol. Urban environments without obstacles has been simulated using actual maps for example intersection density. In order to measure efficiency, the metrics are: average delivery rate, average delay, average length of path and overhead. From the analysis, it shows that GreedLea offers better performance compared to GPSR for both city and highway scenario. The first section in your paper.
      1
  • Publication
    A cloud-based automated parking system for smart campus
    (IOP Publishing, 2020)
    O C Yee
    ;
    Naimah Yaakob
    ;
    Mohamed Elshaikh Elobaid Said Ahmed
    ;
    F Azahar
    Finding a vacant parking space is becoming a real problem especially in areas with limited parking spots such as airports, shopping centres, offices, as well as universities. Searching for available parking slots is normally time consuming and always results in frustration especially when time is the major constraints. Moreover, vacant parking is hard to be noticed due to unsystematic parking system. This will result in longer searching time which can also lead to traffic congestion. In addition, lack of security enforcement on cars entering universities campus is also one of the main issues contributing to insufficient parking spaces. This might cause some unauthorized cars to take opportunity to get inside the campus without any approval and consent from security department. Therefore, A Cloud-based Automated Parking System for Smart Campus is developed in this project. It consists of a sub-system that is developed to display availability number of parking slots so that it will assist authorize users to easily find their parking spots. The proposed system can also recognize car plate number through Automated Car Plate Recognition (ACPR) mechanism which is located at the campus main entrance gate to avoid unauthorized cars from getting in. This has strengthened security level inside campus and ensure the safety of students and staffs. All the information collected are sent into the cloud and stored inside a database system. The information regarding vacant parking can also be displayed using the developed mobile apps.
      8  3