Big Data Analytics and Mobile Cloud Computing in the Internet of Things

In the context of increasingly necessary energy transition, the precise modeling of profiles for low-voltage (LV) network consumers is crucial to enhance hosting capacity. Typically, load curves for these consumers are estimated through measurement campaigns conducted by Distribution System Operators (DSOs) for a representative subset of customers or through the aggregation of load curves from household appliances within a residence. With the instrumentation of smart meters becoming more common, a new approach to modeling profiles for residential customers is proposed to make the most of the measurements from these meters. The disaggregation model estimates the load profile of customers on a low-voltage network by disaggregating the load curve measured at the secondary substation level. By utilizing only the maximum power measured by Linky smart meters, along with the load curve of the secondary substation, this model can estimate the daily profile of customers. For 48 secondary substations in our dataset, the model obtained an average symmetric mean average percentage error (SMAPE) error of 4.91% in reconstructing the load curve of the secondary substation from the curves disaggregated by the model. This methodology can allow for an estimation of the daily consumption behaviors of the low-voltage customers. In this way, we can safely envision solutions that enhance the grid hosting capacity. Full article

Non-terrestrial network (NTN) is a trending topic in the field of communication, as it shows promise for scenarios in which terrestrial infrastructure is unavailable. Unmanned autonomous intelligent systems (UAISs), as a physical form of artificial intelligence (AI), have gained significant attention from academia and industry. These systems have various applications in autonomous driving, logistics, area surveillance, and medical services. With the rapid evolution of information and communication technology (ICT), 5G and beyond-5G communication have enabled numerous intelligent applications through the comprehensive utilization of advanced NTN communication technology and artificial intelligence. To meet the demands of complex tasks in remote or communication-challenged areas, there is an urgent need for reliable, ultra-low latency communication networks to enable unmanned autonomous intelligent systems for applications such as localization, navigation, perception, decision-making, and motion planning. However, in remote areas, reliable communication coverage is not available, which poses a significant challenge for intelligent systems applications. The rapid development of non-terrestrial networks (NTNs) communication has shed new light on intelligent applications that require ubiquitous network connections in space, air, ground, and sea. However, challenges arise when using NTN technology in unmanned autonomous intelligent systems. Our research examines the advancements and obstacles in academic research and industry applications of NTN technology concerning UAIS, which is supported by unmanned aerial vehicles (UAV) and other low-altitude platforms. Nevertheless, edge computing and cloud computing are crucial for unmanned autonomous intelligent systems, which also necessitate distributed computation architectures for computationally intensive tasks and massive data offloading. This paper presents a comprehensive analysis of the opportunities and challenges of unmanned autonomous intelligent systems in UAV NTN, along with NTN-based unmanned autonomous intelligent systems and their applications. A field trial case study is presented to demonstrate the application of NTN in UAIS. Full article