## 2 Literature Review  
The pervasive influence of emerging technologies on our daily lives underscores the belief that there exists an Information Technology (IT) solution for nearly every societal challenge. One such solution gaining increasing prominence is the Internet of Things (IoT) [2]. As described in an IEEE special report, the IoT embodies a self-configuring, adaptive, and intricate network through which \"things\" communicate via standard protocols. These interconnected entities possess sensing and activation capabilities, programmability, and unique detectability [3]. The IoT represents a groundbreaking concept where physical objects, ranging from wearables to vehicles and from the literature to environments, can be interconnected, addressed, and managed remotely. Advancements in technologies like radio frequency identification (RFID) sensors have accelerated the evolution of the IoT [1]. This transformative technology has permeated various domains, including healthcare, transportation, computing, and manufacturing [4]. Among its most critical and compelling applications lies pharmacovigilance and healthcare. The IoT holds the potential to revolutionize facets of healthcare delivery, including telehealth, fitness monitoring, chronic disease management, and eldercare. It facilitates remote patient monitoring, enhances reporting mechanisms, and streamlines homecare services. Through IoT-enabled healthcare solutions, medical costs stand to decrease, quality of life to improve, and user experiences to be enhanced. Moreover, for healthcare providers, the IoT enables remote device management, potentially reducing downtime and facilitating rapid equipment replacement [5]. Due to the continuous and advanced technology transformation phase, healthcare industries are still migrating their legacy and outdated eHealth systems and applications to provide a better scope of experiences to their customer/patient [6]. The healthcare sector, characterized by its unpredictable nature and critical time-sensitive scenarios, demands swift and accurate communication and localization during emergencies. Cloud-based mechanisms often struggle to deliver timely notifications during emergencies [7]. Traditional cloud servers are unable to meet the low latency requirements of IoT medical equipment and consumers. Because of IoT data transfer, it is therefore vital to reduce network latency, computation delay, and energy consumption. Using FC, data can be stored, processed, and analyzed. Cloud computing data are located at a network edge to reduce high latency [8]. In scenarios where a sensor-to-cloud architecture is impractical or prohibited, a reliable healthcare system capable of real-time patient monitoring becomes imperative. One such solution is fog computing [9]. Fog computing, an IoT-based architecture, leverages established technologies such as cloud computing, distributed control systems, cloudlets, and wireless networks. Unlike traditional IT architectures where intelligence resides either in the cloud or at the endpoint, this architecture offers edge computing capabilities, enabling low latency, geographic distribution, and real-time analytics [10;11]. Despite its potential, the vast amounts of data generated by IoT devices pose challenges and opportunities alike. Data mining emerges as a pivotal technique to extract actionable insights from large datasets, thereby enhancing IoT systems' intelligence [12]. Nowadays, big data have become an increasingly popular domain in various fields that are associated with society, technology, science, and engineering. A huge amount of data is recorded and produced from diverse sectors, from distinct resources like sensor networks, mobile applications, high throughput instruments, and streaming machines and also in other fields like the healthcare industry [13]. In the realm of healthcare, the IoT has garnered significant attention, particularly in disease management. Among these, diabetes stands out as a focal point.