محمد شريف داوود

استاذ مساعد

مقر أبوظبي

+971 2 6133591

mohammad.daoud@aau.ac.ae

التعليم

Ph.D. Computer Science, De Montfort University, UK

Master of Computer Science, University of Jordan, Jordan

BSc of Computer Information System, AL-Zaytoonah University, Jordan

الاهتمامات البحثية

Wireless communications, 5th Cellular communication networks), mobility prediction and Location-based services, Artificial intelligence such as Ants' colony optimization and time table scheduling, 

منشورات مختارة

 

  • Daoud, M & Hani Mimi. A survey on Location Based-Services over Cellular Network, 8th International Conference on Information Technology ICIT'2017 IEEE International.17-18,May,20017.

 

  • Qatawneh Mohammad, Ahmad Alamoush, Sawsan Basem, Maen M. Al Assaf & Daoud, M. EMBEDDING BUS AND RING INTO HEX-CELL INTERCONNECTION NETWORK, International Journal of Computer Networks & Communications (IJCNC) Vol.7, No.3, May 2015.

 

  • Al-Fayoumi, M; Nababteh, M; Daoud, M & Alhawarat, M, Dynamic Authentication Protocol for Mobile Networks Using Public-Key Cryptography, International Journal of Science and Research (IJSR),1608-1617, Volume 4 Issue 1, January 2015.

 

  • Rasmi Mohammad, Hani Mimi & Daoud, M. Evaluating composite EC operations and their applicability to the on-the-fly and non-window multiplication methods. International Journal of Computer Applications,  (0975 – 8887) Volume 105 – No. 6, November 2014.

 

  • Daoud, M.; Hopgood, A.; Al-Fayoumi, M., Al-Mimi, H. A New Routing Area Displacement Prediction for Location-Based Services based on an Enhanced Ant Colony. Conference on Systems, Man, and Cybernetics (SMC), 2014 IEEE International, pp.3247-3252 5-8 October 2014.

 

  • Daoud, M.; Ayesh, A.; Al-Fayoumi, M. & Hopgood, A. Location Prediction based on a Sector Snapshot for Location-Based Services, Journal of Network and Systems Management(JONS), Springer, 2012, DOI: 10.1007/s10922-012-9258-9, January   2014.

 

  • Daoud, M.; Ayesh, A.; Al-Fayoumi, M. & Hopgood, A. An Enhanced Ant Colony Optimization for Routing Area Mobility Prediction over Cellular Communications Network, the 5th International Conference on Agents and Artificial Intelligence (ICAART), sponsored by INSTICC - Institute for Systems and Technologies of Information, Control and Communication, February 2013, Barcelona, Spain.

 

  • Daoud, M.; Ayesh, A.; Hopgood, A. & Al-Fayoumi, M. A new splitting-based displacement prediction approach for location-based services Systems, Man, and Cybernetics (SMC), 2011 IEEE International Conference on, 2011, 392-397.

 

المواد التدريسية

Introduction to Programming Languages ,Computer Networks, Operating Systems, User Interface, Web Programming 

العضويات

5th Generation Cellular Communication Group, Manchester Metropolitan / UK.

Conference Paper

A new splitting-based displacement prediction approach for location-based services

أبريل 08, 2019

Daoud M.; Ayesh A.; Hopgood A. & Al-Fayoumi

In location-based services (LBSs), the service is provided based on the users' locations through location determination and mobility realization. Several location prediction models have been proposed to enhance and increase the relevance of the information retrieved by users of mobile information systems, but none of them studied the relationship between accuracy rate of prediction and the performance of the model in terms of consuming resources and constraints of mobile devices. Most of the current location prediction research is focused on generalized location models, where the geographic extent is divided into regular-shape cells. These models are not suitable for certain LBSs where the objectives are to compute and present on-road services. One such technique is the Prediction Location Model (PLM), which deals with inner cell structure. The PLM technique suffers from memory usage and poor accuracy. The main goal of this paper is to propose a new path prediction technique for Location-Based Services. The new approach is competitive and more efficient compared to PLM regarding measurements such as accuracy rate of location prediction and memory usage.