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LETISA: Latency optimal Edge computing Technique for IoT based Smart Applications

Author

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  • Mahmood Hussain Mir
  • D. Ravindran

Abstract

Internet of Things is a technological change which is not only “connected” but it is “smart”, connected is not equal to smart. The switch from connected to smart is the ability of performing analytics at device level, which implies moving beyond, means not just only sensing the data but also processing the data. IoT in “smart” sense also called as Internet of Everything (IoE) or Internet of Anything (IoA). Edge Computing is used as an intermediary layer between the cloud and end users to reduce the latency time and extra communication cost that is usually found high in cloud based systems. The transmission time of cloud based computing is intolerable almost for every smart application. In this paper the existing system is studied and also the drawbacks of system are highlighted and problems associated with it are discussed. Keeping existing system in view a novel and efficient system is proposed, which tries to eliminate the drawbacks of the existing system. The LETISA is based on edge computing, which is a new and emerging technology that brings the services close to the proximity of data sources, as IoT devices are not only used as data gathering devices but are also acting as data consumers. The LETISA improves the efficiency of IoT based applications by deploying some of the computational capabilities at the edge devices. Finally the performance of the system is compared with the existing system, which shows the LETISA can significantly overcome the end-to-end latency.

Suggested Citation

  • Mahmood Hussain Mir & D. Ravindran, 2017. "LETISA: Latency optimal Edge computing Technique for IoT based Smart Applications," International Journal of Scientific Research in Computer Science, Engineering and Information Technology, International Journal of Scientific Research in Computer Science, Engineering and Information Technology, vol. 2(4), pages 688-694, August.
  • Handle: RePEc:jbh:ijsrcs:v2:y2017:i4:id:hcseit1724178
    Note: Article URL: https://ijsrcseit.com/CSEIT1724178
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