Despite the fact that cloud computing is an effective solution for handling data in distributed environments, it is considered as an appropriate way to efficiently process the mass data generated by IoT devices [1]. It delivers centralized resources for data computation and storage, which can affect metrics like delay and bandwidth limitation [2, 3]. Inherently, Fog nodes are distributed within the proximity of users; a characteristic that reduces latency and establishes adjacent localized connections. Recently, the combination of cloud/fog, and IoT communication networks has received a great attention and widely emerged [4]. IoT exploits the fog computing capacities for virtualizing the tasks of IoT devices, but it still has restricted capability and acquires long delay [5]. Though the primary purpose of Fog paradigm is to achieve all tasks with high performance, the security features must be considered as part of the Fog system to guarantee

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The Internet of Thing IoT paradigm has emerged in numerous domains and it has achieved an exponential progress. Nevertheless, alongside this advancement, IoT networks are facing an ever-increasing rate of security risks because of the continuous and rapid changes in network environments. In order to overcome these security challenges, the fog system has delivered a powerful environment that provides additional resources for a more improved data security. However, because of the emerging of various breaches, several attacks are ceaselessly emerging in IoT and Fog environment. Consequently, the new emerging applications in IoT-Fog environment still require novel, distributed, and intelligent security models, controls, and decisions. In addition, the ever-evolving hacking techniques and methods and the expanded risks surfaces have demonstrated the importance of attacks detection systems. This proves that even advanced solutions face difficulties in discovering and recognizing these small variations of attacks. In fact, to address the above problems, Artificial Intelligence (AI) methods could be applied on the millions of terabytes of collected information to enhance and optimize the processes of IoT and fog systems. In this respect, this research is designed to adopt a new security scheme supported by an advanced machine learning algorithm to ensure an intelligent distributed attacks detection and a monitoring process that detects malicious attacks and updates threats signature databases in IoT-Fog environments. We evaluated the performance of our distributed approach with the application of certain machine learning mechanisms. The experiments show that the proposed scheme, applied with the Random Forest (RF) is more efficient and provides better accuracy (99.50%), better scalability, and lower false alert rates. In this regard, the distribution character of our method brings about faster detection and better learning.

The COVID-19 pandemic has had catastrophic consequences all over the world since the detection of the first case in December 2019. Currently, exponential growth is expected. In order to stop the spread of this pandemic, it is necessary to respect sanitary protocols such as the mandatory wearing of masks. In this research paper, we present an affordable artificial intelligence-based solution to increase the protection against COVID-19, covering several relevant aspects to facilitate the detection and prevention of this pandemic: noncontact temperature measurement, mask detection, automatic gel-dispensing, and automatic sterilization. Our main contribution is to provide high-quality, real-time learning and analysis. To achieve this goal, we used a deep convolutional neural network (CNN) based on MobileNetV2 architecture as the learning algorithm and Advanced Encryption Standard (AES) as an encryption protocol for sending secure data to notify hospital staff. The experimental results show the effectiveness of our model by providing 99.7% accuracy in detecting masks with a runtime of 1.54 s.

The increasing number of people with Alzheimer's disease (AD) is a significant concern in many countries. Hence, new solutions for preventing, detecting and supporting persons with AD are required. The aim of this paper is to develop a prototype that provides psychological support services and ensures secure sending of information that can be investigated by a family member to protect the person with AD. The designed wearable prototype is able to classify the detected images into two categories including family/non family member based on a Convolutional Neural Network (CNN). Moreover, our prototype enables tracking the location of the person with AD. Furthermore, our IoT prototype protects the images captured by the webcam through the steganography technique that allows the recipient to decode the original image using a key. Another feature of the developed prototype concerns the possibility of communication via voice messages between the person with AD and his/her member. Additionally, our prototype integrates Google assistant for supporting the persons with AD and therefore answering his/her questions, reducing social isolation and predicting his/her psychological status. So far, our prototype tracks the location of the person with AD and sends an alert if the person leaves a specified area. Our prototype is useful for persons who are affected by mild and moderate AD. It supports them for remembering their family members and recognizing other people after decrypting the extra information hidden in the images. Our results show that our prototype is effective for detecting the images of the family members of a person with AD while ensuring a high accuracy and precision compared to other benchmark techniques.