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100			_aMohammad Ehsan Sahami
245			_aProposing an Efficient Method for Resource Allocation in the IoT Devices based on Fog Computing in Face Detection Allocations
300			_aPages 358-372
520			_aThis research addresses the imperative challenge of resource allocation efficiency in face detection tasks within the Internet of Things (IoT) paradigm by incorporating Fog Computing. The architecture proposed employs smart offloading of tasks, dynamic profiling of resources, as well as a new strategy that is multi-algorithmic in order to enhance efficiency, scalability, as well as reliability in detection of faces in IoT. Specifically, the architecture employs the Energy Valley Optimizer (EVO) as a form of energy-efficient allocation of resources, the Fire Hawk Optimizer (FHO) as a form of adaptive as well as strategic allocation of tasks, as well as the Artificial Bee Colony (ABC) algorithm as a form of decentralized as well as swarm-based optimization. The hybrid strategy efficiently solves dynamic task offloading, dynamic profiling in real time, as well as adaptive optimization in distributed Fog Computing scenarios. This integration promises unprecedented levels of optimization and adaptability, enabling dynamic allocation of processing, storage, and communication resources based on real-time demands. The comprehensive framework presented herein contributes to advancing IoT-based face detection, paving the way for enhanced real-time applications across diverse domains.
654			_aEnergy Valley Optimizer (EVO) _aFace detection _aFog Computing _aIoT _aResource allocation
773	0		_0133183 _9114370 _aHemant Kumar Varshney _tDesign of a High-Gain UWB Antenna with Band Notch Characteristics Using Frequency Selective Surface
942			_cJA
999			_c133184 _d133184