[PERSON] et al. [8] describe a framework that addresses the problem of traffic congestion by integrating multiple data sources measuring traffic status with smart tools and services, such as a smart traffic light algorithm to manage congestion at intersections, as well as tweet classification and image-processing algorithms for decision-making. [PERSON] et al. [9] contribute to the development of publicly available labeled datasets in the domain of cyberattacks on Internet of Flying Things (IoFT). To this purpose, they propose the ECU-IoFT dataset, documenting three known cyber attacks targeting Wi-Fi communications and the lack of security in an affordable drone model. [PERSON], [PERSON], and [PERSON] [10] propose a spectrum resource allocation scheme based on reinforcement learning to maximize the total capacity of Vehicle to Vehicle (V2V) and Vehicle to Infrastructure (V2I) links and achieve optimal spectrum allocation, effectively showing maximization of V2V and V2I link capacity. [PERSON] et al. [11] describe a comparative approach for the evaluation of the role that IoT technologies play in the yearly expenses of drivers. To this end, they propose a two-stage methodology to understand the rationale of car expenses by collecting expert opinions through a survey of customers with both connected and non-connected vehicles. The survey results indicated that IoT devices integrated with cars' instrumentation tools lead to significantly fewer expenses regarding maintenance. [PERSON] and [PERSON] [12] discuss the vulnerabilities of IoT shadow devices and connections and how they represent a risk and a challenge for both security and forensic investigations. To this aim, they present the development of a conceptual model for smart digital forensic readiness with shadow IoT devices. [PERSON], [PERSON], and [PERSON] [13] propose an Urban Open Platform (UOP) supporting data acquisition, aggregation, and processing, which has been applied in the context of two European capital cities, Helsinki and Tallinn. [PERSON] et al. [14] present a wearable remote patient monitoring system based on the Internet of Medical Things (IoMT), Artificial Intelligence (AI), and edge computing. The proposed system is used for remote monitoring of cardiovascular patients, implementing a Machine Learning (ML) model that makes inferences about the user's health status, makes predictions based on real-time data, as well as produces alerts and notifications in case of emergency. [PERSON] et al. [15] describe a method for energy load prediction that combines kernel principal component analysis (KPCA) with kernel extreme learning machine (KELM) optimized by fireworks algorithm (FWA). The KPCA method is exploited to select input features, and the FWA approach is employed to optimize the parameters of KELM. The method is used for load prediction of distributed energy systems. [PERSON] et al. [16] propose a cloud service selection and recommendation system (CS-SR) to find an optimal service based on users' custom requirements by considering both the quantitative and qualitative quality of service (QoS). [PERSON] et al. [17] research and investigate the perception of telemedicine for humans through a mobile app providing telemedicine for pets. As a result of the statistical validation tests, users with negative perceptions about pet telemedicine were also found to have negative feelings and insights about telemedicine for humans. [PERSON] et al. [18] conduct a review and assessment of IoT platforms based on the following evaluation criteria: communication protocols, data visualization, data processing, integration with external services and security, installation procedure, and documentation. Over 30 platforms were evaluated, and the FIWARE platform had the overall best performance when considering all the above-mentioned criteria. [PERSON] et al. [19] evaluate the relevant literature about the design of smart warehouses.