Internet of Things (IoT) devices generate a tremendous amount of time series data that is extremely dynamic, heterogeneous and time dependent. Such types of data introduce significant challenges for the real-time prediction of QoS metrics of IoT applications with different traffic characteristics. To this end, in this paper, we propose a temporal transformer model and a unified system to predict several QoS metrics of heterogeneous IoT applications when they communicate with the Edge of the network. The transformer model also leverages an attention module to provide a solution for both short-term and long-term sequence prediction of QoS metrics that allows to better extract any time dependencies. In particular, in our framework, we firstly generate a set of datasets containing real-time traffic information of five different IoT applications such as Heating, Ventilation, and Air Conditioning (HVAC), lighting, Voice over Internet Protocol (VoIP), surveillance and emergency response using the 802.15.4 access technology and the RPL routing protocol. Following, we perform the data cleaning, downsampling and pre-processing of the datasets and we construct the QoS datasets, which include four QoS metrics, namely throughput, packet delivery ratio, packet loss ratio and latency. Finally, we evaluate the transformer model through extensive experimentation using both short-term and long-term dependencies and we show that our model can guarantee a robust performance and accurate QoS prediction.

The demands for a large number of sensors increase as the proliferation of Internet of Things (IoT) and smart cities applications are continuing at a rapid pace. This also increases the cost of the infrastructure and the installation and maintenance overhead and creates significant performance degradation in the end-to-end communication, monitoring, and orchestration of the various connected devices. In order to solve the problem of increasing sensor demands, this paper suggests replacing physical sensors with machine learning (ML) models. These software-based artificial intelligence models are called virtual sensors. Extensive research and simulation comparisons between fourteen ML models provide a solid ground decision when it comes to the selection of the most accurate model to replace physical sensors, such as temperature and humidity sensors. In this problem at hand, the virtual and physical sensors are designed to be scattered in a smart home, while being connected and run on the same IoT platform. Thus, this paper also introduces a custom lightweight IoT platform that runs on a Raspberry Pi equipped with physical temperature and humidity sensors, which may also execute the virtual sensors. The evaluation results of the devised virtual sensors in a smart home scenario are promising and corroborate the applicability of the proposed methodology.

The global challenges of sustainability intensify the demand for advanced decision-making systems capable of integrating cognitive capabilities. This survey article comprehensively examines the recent advancements, diverse applications, state-of-the-art techniques, and persistent challenges in cognition and context-aware decision-making systems (CCA-DMS) aimed at fostering sustainability across smart environments. We first present the cognition and context-aware decision-making applications covering topics such as environmental conservation, energy management, urban planning, resource usage, and healthcare. Subsequently, we make a categorization of techniques, methods and cognition-aware models employed. Through a systematic review of the literature, we highlight the significant contributions and breakthroughs in each use case. Furthermore, we identify and analyze the open challenges and research gaps that remain to be addressed, including data gathering, scalability, interpretability, robustness, and ethical considerations. By synthesizing the current state-of-the-art and future directions, this survey serves as a valuable resource for researchers, practitioners, and policymakers interested in harnessing CCA-DMS to address the complex sustainability challenges facing our planet.