Mohamed Riduan Abid

Maad Ebrahim, Abdelhakim Senhaji Hafid, Mohamed Riduan Abid

Fog computing emerged as a promising paradigm to address the challenges of processing and managing data generated by the Internet of Things (IoT). Load balancing (LB) plays a crucial role in Fog computing environments to optimize the overall system performance. It requires efficient resource allocation to improve resource utilization, minimize latency, and enhance the quality of service for end-users. In this work, we improve the performance of privacy-aware Reinforcement Learning (RL) agents that optimize the execution delay of IoT applications by minimizing the waiting delay. To maintain privacy, these agents optimize the waiting delay by minimizing the change in the number of queued requests in the whole system, i.e., without explicitly observing the actual number of requests that are queued in each Fog node nor observing the compute resource capabilities of those nodes. Besides improving the performance of these agents, we propose in this paper a lifelong learning framework for these agents, where lightweight inference models are used during deployment to minimize action delay and only retrained in case of significant environmental changes. To improve the performance, minimize the training cost, and adapt the agents to those changes, we explore the application of Transfer Learning (TL). TL transfers the knowledge acquired from a source domain and applies it to a target domain, enabling the reuse of learned policies and experiences. TL can be also used to pre-train the agent in simulation before fine-tuning it in the real environment; this significantly reduces failure probability compared to learning from scratch in the real environment. To our knowledge, there are no existing efforts in the literature that use TL to address lifelong learning for RL-based Fog LB; this is one of the main obstacles in deploying RL LB solutions in Fog systems.

Ghezlane Halhoul Merabet, Mohamed Essaaidi, Mohamed Ben Haddou et al.

Building operations represent a significant percentage of the total primary energy consumed in most countries due to the proliferation of Heating, Ventilation and Air-Conditioning (HVAC) installations in response to the growing demand for improved thermal comfort. Reducing the associated energy consumption while maintaining comfortable conditions in buildings are conflicting objectives and represent a typical optimization problem that requires intelligent system design. Over the last decade, different methodologies based on the Artificial Intelligence (AI) techniques have been deployed to find the sweet spot between energy use in HVAC systems and suitable indoor comfort levels to the occupants. This paper performs a comprehensive and an in-depth systematic review of AI-based techniques used for building control systems by assessing the outputs of these techniques, and their implementations in the reviewed works, as well as investigating their abilities to improve the energy-efficiency, while maintaining thermal comfort conditions. This enables a holistic view of (1) the complexities of delivering thermal comfort to users inside buildings in an energy-efficient way, and (2) the associated bibliographic material to assist researchers and experts in the field in tackling such a challenge. Among the 20 AI tools developed for both energy consumption and comfort control, functions such as identification and recognition patterns, optimization, predictive control. Based on the findings of this work, the application of AI technology in building control is a promising area of research and still an ongoing, i.e., the performance of AI-based control is not yet completely satisfactory. This is mainly due in part to the fact that these algorithms usually need a large amount of high-quality real-world data, which is lacking in the building or, more precisely, the energy sector.

Ghezlane Halhoul Merabet, Mohamed Essaaidi, Mohamed Ben-Haddou et al.

Different factors such as thermal comfort, humidity, air quality, and noise have significant combined effects on the acceptability and quality of the activities performed by the building occupants who spend most of their times indoors. Among the factors cited, thermal comfort, which contributes to the human well-being because of its connection with the thermoregulation of the human body. Therefore, the creation of thermally comfortable and energy efficient environments is of great importance in the design of the buildings and hence the heating, ventilation and air-conditioning systems. Recent works have been directed towards more advanced control strategies, based mainly on artificial intelligence which has the ability to imitate human behavior. This systematic literature review aims to provide an overview of the intelligent control strategies inside building and to investigate their ability to balance thermal comfort and energy efficiency optimization in indoor environments. Methods. A systematic literature review examined the peer-reviewed research works using ACM Digital Library, Scopus, Google Scholar, IEEE Xplore (IEOL), Web of Science, and Science Direct (SDOL), besides other sources from manual search. With the following string terms: thermal comfort, comfort temperature, preferred temperature, intelligent control, advanced control, artificial intelligence, computational intelligence, building, indoors, and built environment. Inclusion criteria were: English, studies monitoring, mainly, human thermal comfort in buildings and energy efficiency simultaneously based on control strategies using the intelligent approaches. Preferred Reporting Items for Systematic Reviews and Meta-Analysis guidelines were used. Initially, 1,077 articles were yielded, and 120 ultimately met inclusion criteria and were reviewed.