الفهرس 
Title : A framework for monitoring road traffic using fog computing
ContantsOnly 14 pages are availabe for public view
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Abstract
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