A.5. I/O and Data Communications
Farzane Shirazi; Nazbanoo Farzaneh
Abstract
Efficient allocation of parking spaces in urban environments remains a significant challenge due to diverse user preferences such as cost, proximity, and convenience. This paper proposes a novel intelligent parking assignment framework based on the Cheetah Optimization Algorithm (COA), a bio-inspired ...
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Efficient allocation of parking spaces in urban environments remains a significant challenge due to diverse user preferences such as cost, proximity, and convenience. This paper proposes a novel intelligent parking assignment framework based on the Cheetah Optimization Algorithm (COA), a bio-inspired metaheuristic mimicking the adaptive hunting behavior of cheetahs. The method integrates user-specific criteria in a multi-stage process, first collecting system and driver data, then applying COA to optimize parking space allocation. Compared to deep reinforcement learning and other metaheuristics like Genetic Algorithm and Whale Optimization Algorithm, COA demonstrates faster convergence, and improved solution quality. The results confirm that COA is an effective and robust approach for real-time, personalized smart parking management in dynamic urban settings.
J. Barazande; N. Farzaneh
Abstract
One of the crucial applications of IoT is developing smart cities via this technology. Smart cities are made up of smart components such as smart homes. In smart homes, a variety of sensors are used for making the environment smart, and the smart things, in such homes, can be used for detecting the activities ...
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One of the crucial applications of IoT is developing smart cities via this technology. Smart cities are made up of smart components such as smart homes. In smart homes, a variety of sensors are used for making the environment smart, and the smart things, in such homes, can be used for detecting the activities of the people inside them. Detecting the activities of the smart homes’ users may include the detection of activities such as making food or watching TV. Detecting the activities of residents of smart homes can tremendously help the elderly or take care of the kids or, even, promote security issues. The information collected by the sensors could be used for detecting the kind of activities; however, the main challenge is the poor precision of most of the activity detection methods. In the proposed method, for reducing the clustering error of the data mining techniques, a hybrid learning approach is presented using Water Strider Algorithm. In the proposed method, Water Strider Algorithm can be used in the feature extraction phase and exclusively extract the main features for machine learning. The analysis of the proposed method shows that it has precision of 97.63 %, accuracy of 97. 12 %, and F1 index of 97.45 %. It, in comparison with similar algorithms (such as Butterfly Optimization Algorithm, Harris Hawks Optimization Algorithm, and Black Widow Optimization Algorithm), has higher precision while detecting the users’ activities.
