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Abstract The increasing attacks against Internet of Things (IoT) systems are a determining factor to focus attention on IoT security. The use of Smart Cards (SCs) to protect these systems is pertinent, as they are resistant to physical attacks, portable, and have low cost. Nonetheless, SC processing and memory capacity are not sufficient to meet IoT demands. Therefore, the use of multiple clustered SCs seems interesting. The Field Programmable Gate Array can manage multiple SCs since it has the ability to parallelize tasks. In this work, Smart Card Cluster (SCC) is applied to ensure message authenticity and integrity through hardware signing. For instance, with only three SCs, a SCC supports signing about 26 messages per minute. In this paper, we propose a SCC that is modular, portable, flexible and cost-effective. Through experiments, we demonstrate that our SCC outperforms existing solutions by as much as 69%, while the cost is lower. [ABSTRACT FROM AUTHOR]

Recently, Federated Learning (FL) has been explored as a new paradigm that preserves both data privacy and end-users knowledge while reducing latency during model training. While often applied between a central server and the edge devices, FL deals with a wide range of end-user applications and devices. Given the Internet of Things (IoT) current popularity, its relevance and its penetration in several new application domains, this work examines the new challenges faced when combining IoT with the classic FL model. The limited resources of IoT edge devices require careful adaptation of the way how FL should be structured in this scenario. In addition, since FL is a distributed paradigm that shares deep learning artifacts through a network, there are also communication issues reminiscent to IoT networks that need special consideration. Thus, it is necessary to optimize the use of both processing and communication resources when considering the use of IoT edge devices as part of a FL. This paper systematically reviews current advances and steps taken towards dealing with resource optimization when using FL as part of IoT scenarios. We examine the published works over the last ten years and discuss the main goals, scenarios, and developed solutions to solve the problems encountered. We also present the main metrics used to quantify the effectiveness and to evaluate the performance of existing IoT based architectures with FL support. • Federated Learning preserves data privacy and end-users knowledge. • Federated Learning deals with a wide range of end-user applications and devices. • Internet of Things application offers a substantial source of data. • Federated Learning is seen as a suitable approach for building smart IoT systems. • Federated Learning and IoT integration present peculiar IoT-scenario challenges. [ABSTRACT FROM AUTHOR]

Human falls are a global public health issue resulting in over 37.3 million severe injuries and 646,000 deaths yearly. Falls result in direct financial cost to health systems and indirectly to society productivity. Unsurprisingly, human fall detection and prevention are a major focus of health research. In this article, we consider deep learning for fall detection in an IoT and fog computing environment. We propose a Convolutional Neural Network composed of three convolutional layers, two maxpool, and three fully-connected layers as our deep learning model. We evaluate its performance using three open data sets and against extant research. Our approach for resolving dimensionality and modelling simplicity issues is outlined. Accuracy, precision, sensitivity, specificity, and the Matthews Correlation Coefficient are used to evaluate performance. The best results are achieved when using data augmentation during the training process. The paper concludes with a discussion of challenges and future directions for research in this domain. [ABSTRACT FROM AUTHOR]