Falls indeed contribute significantly to injury-related fatalities among the elderly population worldwide. Thus detecting falls efficiently is crucial for reducing risks of harm. This process of detection is in general achieved through wearable gadgets or environmental sensors which encounter challenges such as user adherence or false alerts. Alternatively, video cameras offer an interesting passive solution, yet susceptible to lighting changes and privacy issues. From a machine learning standpoint, crafting a reliable fall detection system poses difficulties due to the infrequency and diverse nature of falls. Among the important challenges and issues reported in literature is the difficulty of fair comparison and analysis between fall detection machine learning techniques. This challenge arises due to variations in datasets, evaluation metrics, and experimental setups across studies. This paper utilizes a model-based approach to select hyper-parameters for training Support Vector Machine (SVM) models in the context of failure detection. Experimental results demonstrate that this method enables the discovery of parameter sets that yield higher values for two evaluation metrics: accuracy and F1-score. The proposed model-based approach effectively selects hyper-parameters for SVM models in failure detection, resulting in higher accuracy and F1 scores. It outperforms existing solutions and algorithms in predicting fall detection.

Currently, the IoT performs important functions in several diverse applications. As a consequence of such diversity, there are also several issues to deal with, and the communication of devices is one of these prominent challenges. Due to its heterogeneity, the communication of IoT devices is an eminent system function, and, thus, distinct communication protocols have been proposed for these systems. Even under the same operating conditions, the performance of these protocols can differ significantly. In this paper, we present a performance evaluation of IoT communication protocols for the application layer. We address the problem of the efficiency of IoT protocols by means of a formal model-based approach, which encompasses features of various protocols and aims at analyzing and comparing the performance of these protocols. The proposed models have been proven useful by considering the verification and comparison of three well-known IoT communication protocols, namely MQTT, AMQP and CoAP protocols.

Cloud computing is an on-demand computing model, using virtualization technology to provide services to users in the form of virtual machines (VMs) through internet. Job-scheduling is one of the fundamental reasons to have high performance in a cloud environment. Therefore, there is a need for protocols that can efficiently allocate jobs to resources. However, due to the lack of an explicit and formal description of the resource perspective in the existing cloud platforms, the correctness of Cloud resources management can not be verified. The aim of the present work is to offer a formal description as a step towards ensuring a correct and consistent Cloud resource allocation modeling. In particular, we propose a distributed formal model for the description of a job-scheduling protocol in the cloud which considers the types of jobs and the resource availability in its scheduling decision. The formal verification of different properties of the proposed model has been performed automatically using Model-checking.