In healthcare, there are rapid emergency response systems that necessitate real-time actions where speed and efficiency are critical; this may suffer as a result of cloud latency because of the delay caused by the cloud. Therefore, fog computing is utilized in real-time healthcare applications. There are still limitations in response time, latency, and energy consumption. Thus, a proper fog computing architecture and good task scheduling algorithms should be developed to minimize these limitations. In this study, an Energy-Efficient Internet of Medical Things to Fog Interoperability of Task Scheduling (EEIoMT) framework is proposed. This framework schedules tasks in an efficient way by ensuring that critical tasks are executed in the shortest possible time within their deadline while balancing energy consumption when processing other tasks. In our architecture, Electrocardiogram (ECG) sensors are used to monitor heart health at home in a smart city. ECG sensors send the sensed data continuously to the ESP32 microcontroller through Bluetooth (BLE) for analysis. ESP32 is also linked to the fog scheduler via Wi-Fi to send the results data of the analysis (tasks). The appropriate fog node is carefully selected to execute the task by giving each node a special weight, which is formulated on the basis of the expected amount of energy consumed and latency in executing this task and choosing the node with the lowest weight. Simulations were performed in iFogSim2. The simulation outcomes show that the suggested framework has a superior performance in reducing the usage of energy, latency, and network utilization when weighed against CHTM, LBS, and FNPA models.

Over the last recent years, applications related to the internet of things have become the most important techniques in the world that facilitate interactions among humans and things to enhance the quality of life. So, the number of devices used in these applications will increase, leading to the creation of huge amounts of data. Cisco proposed fog computing in 2012, which located between the end-users (Internet of Things devices) and cloud computing. Fog computing is not a replacement for cloud computing, but it reduces the drawbacks of cloud computing, makes it efficient and provides storage and computing services at the edge of the internet. Resource management is the key factor that decides the performance of fog computing. Whereas scheduling plays an important role in managing resources in fog computing, task scheduling is the ability to map tasks to the appropriate resources in fog computing. The task is a small part of a work that must be performed within a specific time. Because fog computing contains heterogeneous and distributed resources, task scheduling becomes complex. Task scheduling is an NP-hard problem that needs to apply effective task scheduling strategies to reach an ideal solution. There were many proposed algorithms about scheduling in the previous years; most of them were applied in cloud computing, while the minority were applied in fog computing. This paper aims to comprehensively review and analyze the most important up-to-date scheduling algorithms in fog computing.

Association Rule Mining is used for data mining using the associate rules for detecting frequent items. Hence, this research introduces a novel sequential rule mining for predicting user category using the proposed hybrid Tasmanian Water Devil Optimization based map reduce framework. The introduced Tasmanian Water Devil Optimization integrates the feeding behavior of the Tasmanian devil in the Tasmanian devil optimization with the water cycle process of streams and rivers flowing toward the sea in the water cycle algorithm for choosing the optimal rule from the rule set. In this, the big data based on the retail transaction is utilized for identifying the frequent items using the proposed Tasmanian Water Devil Optimization Sequential Associate Mining technique at the Mapper side. Then, the integration of rules is employed at the Reducer side for the prediction of user category using the Deep Convolutional Neural Network. Thus, the proposed Tasmanian Water Devil Optimization Sequential Associate Mining + Deep Convolutional Neural Network outperformed other conventional methods, obtained enhanced performance in terms of rules mined, execution time, recall, and precision, and obtained the values of 183, 7.70, 0.88, and 0.88, respectively.