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IEEE Final Year Project Topics for CSE

Base Paper Title

Artificial Intelligence Driven Mechanism for Edge Computing based Industrial Applications

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IEEE Project Abstract

Due to various limitations i.e., computational complexity and more delay in cloud computing is overtaken by edge computing for efficient and fair resource allocation such as, power and battery lifetime in internet of things (IoT) based industrial applications. In the meantime intelligent and accurate resource management by artificial intelligence (AI) has become the center of attention especially in industrial applications. With the coordination of AI at the edge will remarkably enhance the range and computational speed of IoT based devices in industries. But the challenging issue in these power hungry, short battery lifetime and delay intolerant portable devices is inappropriate and inefficient classical trends of fair resource allotment. Also, it is interpreted through extensive industrial data sets that dynamic wireless channel could not be supported by the typical power saving and battery lifetime techniques for example, predictive transmission power control (PTPC) and Baseline. Thus, this paper proposes i) a forward central dynamic and available approach (FCDAA) by adapting the running time of sensing and transmission processes in IoT-based portable devices ii) a system-level battery model by evaluating the energy dissipation in IoT devices iii) a data reliability model for edge artificial intelligence based IoT devices over hybird TPC and duty-cycle network. Two important cases for instance, static (i.e.,product processing) and dynamic (i.e., vibration and fault diagnosis) are introduced for proper monitoring of industrial platform. Experimental test-bed reveals that proposed FCDAA enhances energy efficiency and battery lifetime at acceptable reliability (~0.95) by appropriately tuning duty-cycle and TPC unlike conventional methods.

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