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Thesis - Real Time Distributed Fleet Monitoring System ( MASTER OF TECHNOLOGY in Computer Science & Engineering)

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It is always the challenging task for every one when it comes to security. The companies depend heavily on fleets to deliver product on time and in good condition. With existing company name on fleet, if any driver has no complaint behavior, the company may fail to protect its brand value. It’s a huge concern for the fleet serving companies to monitor different activities on going on the fleets across the different geographical location. How does a company would ensure that a driver is not recklessly driving when a fleet collided with another fleet? Varieties of such other use cases are pertinent where a monitoring of fleet is must in order to ensure the safety of people and assets. The monitoring of fleet is all sound good, but real problem arises on handling of such large volume of data. A single vehicle would capture at least one gigabyte of video data per day. If 100 vehicles are monitored, they would need hundred gigabytes of storages per day. With traditional vertical system based servers, it is extremely difficult and costly to manage such high volume of data. Furthermore, the cost for solution to operate is far higher which is never possible for most the companies to afford for monitoring. This dissertation entitled Real Time Distributed Fleet Monitoring System “RTDFMS” approaches on research to effectively handle and manage such a large volume of data. It focuses on several of aspect of parallel programming in distributed environment in order to make a system more reliable and horizontally scalable which ultimately would reduce the overall cost of monitoring fleets in near real-time.
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