الفهرس 
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Abstract
Usually, Internet of Things (IoT) devices are designed to perform specific tasks, while robots have to adapt to unpredictable situations. Artificial intelligence and machine learning help these robots cope with emerging unexpected conditions. The Internet of Robotic Things (IoRT) is an evolving concept that brings together all-encompassing sensors and devices with robotic and autonomous systems. Both IoT devices and robots rely on sensors to understand the surrounding environment, to process data quickly, and decide how to respond. Nevertheless, while most IoT systems can handle only well-defined tasks, robots can handle expected situations as well. IoRT is a better IoT solution due to its ability to bridge the gap between Information Technology (IT) and real operations. The IoRT is a combination of autonomous robots and the IoT based on smart connectivity. The IoRT system is based on a wireless network that connects many robots to the smart environment and provides unique robotic services. The IoRT relies heavily on task offloading in order to take advantage of the well-developed cloud network facilities and leverage computation support provided by the cloud infrastructure. Nevertheless, given the latency limitation, the additional costs of data transmission, and distant processing, it is not easy to make optimal offloading selections. Especially, task offloading for robots is harder because they can move and connect to networks on-demand, which has a big effect on how robots and the cloud communicate with each other. This thesis introduces a proposed model to determine the optimal way of task offloading for IoRT devices for reducing the amount of energy consumed in IoRT environment and achieving the task deadline constraints. Such a schema will be based on fog computing infrastructure. This infrastructure might serve as the foundation for IoRT applications. The proposed technique to address this problem is named (IHOGCP) Improved Hybrid Optimization technique based on GA Combined with PSO. To validate the efficacy of the proposed schema, an extensive statistical simulation was conducted and compared to other related works. The proposed schema is evaluated against the Genetic Algorithm (GA), Particle Swarm Optimization (PSO), Whale Optimization Algorithm (WOA), Artificial Bee Colony (ABC), Ant Lion Optimizer (ALO), Grey Wolf Optimizer (GWO), and Salp Swarm Algorithm (SSA) to confirm its effectiveness. After 200 iterations, our proposed schema was found to be the most effective in reducing energy, achieving a reduction of 22.85%. This was followed closely by GA and ABC, which achieved reductions of 21.5%. ALO, WOA, PSO, and GWO were found to be less effective, achieving energy reductions of 19.94%, 17.21%, 16.35%, and 11.71%, respectively. The current analytical results prove the effectiveness of the suggested energy consumption optimization strategy. The experimental findings demonstrate that the suggested schema reduces the energy consumption of task requests more effectively than the current technological advances.