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A framework for monitoring road traffic using fog computing / Ahmed Ramzy Ahmed Negm ; Supervised Ehab E. Hassanein , Ahmed H. Awad

By: Contributor(s): Material type: TextTextLanguage: English Publication details: Cairo : Ahmed Ramzy Ahmed Negm , 2020Description: 73 Leaves : charts , fcsmilies ; 30cmOther title:
  • إطار لمراقبة حركة المرور باستخدام حوسبة الضباب [Added title page title]
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Dissertation note: Thesis (M.Sc.) - Cairo University - Faculty of Computers and Artificial Intelligence - Department of Information Systems Summary: Total number of vehicles in most of the cities around the world has increased during past decade along with the population growth. Traffic monitoring in this situation is a big challenge. Various traffic monitoring approaches have been researched and developed to handle this increase in traffic density. Also various Traffic Monitoring Services (TMS) have been proposed to provide strong and encompassing online platform to observe the roads and the traffic status and predict the arrival time of the driver. Arrival time prediction provided by most of navigation systems is affected by several factors, such as road condition, travel time, weather condition, vehicle speed, etc. Systems that provide near real-time road condition updates, e.g. Google Maps, depend on crowdsourcing GPS data from vehicles or mobile devices on the road. GPS data thus has a long journey to travel from their sources to the analytics engine on the cloud before a status update is sent back to the client. Between the time taken for GPS data to be broadcast, received and processed, significant changes in road conditions can take place and would still be unreported, leading to wrong decisions on the route to choose.Road condition, especially average speed of vehicles, monitoring is of a local and continuous nature. It needs to be accomplished near GPS stream data sources to reduce latency and increase the accuracy of reporting. Solutions based on geo-distributed road monitoring, using the Fog Computing paradigm, provide lower latency and higher accuracy than centralized (cloud-based) approaches. Yet, they require a heavy investment and a large infrastructure, which might be a limit for its utility in some countries, e.g. Egypt. In this thesis, we propose a more dynamic approach to continuously update average speed on the road
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Item type Current library Home library Call number Copy number Status Date due Barcode
Thesis Thesis قاعة الرسائل الجامعية - الدور الاول المكتبة المركزبة الجديدة - جامعة القاهرة Cai01.20.04.M.Sc.2020.Ah.F (Browse shelf(Opens below)) Not for loan 01010110082696000
CD - Rom CD - Rom مخـــزن الرســائل الجـــامعية - البدروم المكتبة المركزبة الجديدة - جامعة القاهرة Cai01.20.04.M.Sc.2020.Ah.F (Browse shelf(Opens below)) 82696.CD Not for loan 01020110082696000

Thesis (M.Sc.) - Cairo University - Faculty of Computers and Artificial Intelligence - Department of Information Systems

Total number of vehicles in most of the cities around the world has increased during past decade along with the population growth. Traffic monitoring in this situation is a big challenge. Various traffic monitoring approaches have been researched and developed to handle this increase in traffic density. Also various Traffic Monitoring Services (TMS) have been proposed to provide strong and encompassing online platform to observe the roads and the traffic status and predict the arrival time of the driver. Arrival time prediction provided by most of navigation systems is affected by several factors, such as road condition, travel time, weather condition, vehicle speed, etc. Systems that provide near real-time road condition updates, e.g. Google Maps, depend on crowdsourcing GPS data from vehicles or mobile devices on the road. GPS data thus has a long journey to travel from their sources to the analytics engine on the cloud before a status update is sent back to the client. Between the time taken for GPS data to be broadcast, received and processed, significant changes in road conditions can take place and would still be unreported, leading to wrong decisions on the route to choose.Road condition, especially average speed of vehicles, monitoring is of a local and continuous nature. It needs to be accomplished near GPS stream data sources to reduce latency and increase the accuracy of reporting. Solutions based on geo-distributed road monitoring, using the Fog Computing paradigm, provide lower latency and higher accuracy than centralized (cloud-based) approaches. Yet, they require a heavy investment and a large infrastructure, which might be a limit for its utility in some countries, e.g. Egypt. In this thesis, we propose a more dynamic approach to continuously update average speed on the road

Issued also as CD

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