Proposal of a health care network based on big data analytics for PDs

Health care networks for Parkinson's disease (PD) already exist and have been already proposed in the literature, but most of them are not able to analyse the vast volume of data generated from medical examinations and collected and organised in a pre-defined manner. In this work, the authors propose a novel health care network based on big data analytics for PD. The main goal of the proposed architecture is to support clinicians in the objective assessment of the typical PD motor issues and alterations. The proposed health care network has the ability to retrieve a vast volume of acquired heterogeneous data from a Data warehouse and train an ensemble SVM to classify and rate the motor severity of a PD patient. Once the network is trained, it will be able to analyse the data collected during motor examinations of a PD patient and generate a diagnostic report on the basis of the previously acquired knowledge. Such a diagnostic report represents a tool both to monitor the follow up of the disease for each patient and give robust advice about the severity of the disease to clinicians

Fog paradigm for local energy management systems

Cloud Computing infrastructures have been extensively deployed to support energy computation within built environments. This has ranged from predicting potential energy demand for a building (or a group of buildings), undertaking heat profile/energy distribution simulations, to understanding the impact of climate and weather on building operation. Cloud computing usage in these scenarios have benefited from resource elasticity, where the number and types of resources can change based on the complexity of the simulation being considered. While there are numerous advantages of using a cloud based energy management system, there are also significant limitations. For instance, many such systems assume that the data has been pre-staged at a cloud platform prior to simulation, and do not take account of data transfer times from the building to the simulation platform. The need for supporting computation at edge resources, which can be hosted within the building itself or shared within a building complex, has become important over recent year. Additionally, network connectivity between the sensing infrastructure within a built environment and a data centre where analysis is to be carried out can be intermittent or may fail. There is therefore also a need to better understand how computation/analysis can be carried out closer to the data capture site to complement analysis that would be undertaken at the data centre. We describe how the Fog computing paradigm can be used to support some of these requirements, extending the capability of a data centre to support energy simulation within built environments

Towards a cyber physical system for personalised and automatic OSA treatment

Obstructive sleep apnea (OSA) is a breathing disorder that takes place in the course of the sleep and is produced by a complete or a partial obstruction of the upper airway that manifests itself as frequent breathing stops and starts during the sleep. The real-time evaluation of whether or not a patient is undergoing OSA episode is a very important task in medicine in many scenarios, as for example for making instantaneous pressure adjustments that should take place when Automatic Positive Airway Pressure (APAP) devices are used during the treatment of OSA. In this paper the design of a possible Cyber Physical System (CPS) suited to real-time monitoring of OSA is described, and its software architecture and possible hardware sensing components are detailed. It should be emphasized here that this paper does not deal with a full CPS, rather with a software part of it under a set of assumptions on the environment. The paper also reports some preliminary experiments about the cognitive and learning capabilities of the designed CPS involving its use on a publicly available sleep apnea database

Crossing Roads of Federated Learning and Smart Grids: Overview, Challenges, and Perspectives

Consumer's privacy is a main concern in Smart Grids (SGs) due to the sensitivity of energy data, particularly when used to train machine learning models for different services. These data-driven models often require huge amounts of data to achieve acceptable performance leading in most cases to risks of privacy leakage. By pushing the training to the edge, Federated Learning (FL) offers a good compromise between privacy preservation and the predictive performance of these models. The current paper presents an overview of FL applications in SGs while discussing their advantages and drawbacks, mainly in load forecasting, electric vehicles, fault diagnoses, load disaggregation and renewable energies. In addition, an analysis of main design trends and possible taxonomies is provided considering data partitioning, the communication topology, and security mechanisms. Towards the end, an overview of main challenges facing this technology and potential future directions is presented

Internet of Things-aided Smart Grid: Technologies, Architectures, Applications, Prototypes, and Future Research Directions

Traditional power grids are being transformed into Smart Grids (SGs) to address the issues in existing power system due to uni-directional information flow, energy wastage, growing energy demand, reliability and security. SGs offer bi-directional energy flow between service providers and consumers, involving power generation, transmission, distribution and utilization systems. SGs employ various devices for the monitoring, analysis and control of the grid, deployed at power plants, distribution centers and in consumers' premises in a very large number. Hence, an SG requires connectivity, automation and the tracking of such devices. This is achieved with the help of Internet of Things (IoT). IoT helps SG systems to support various network functions throughout the generation, transmission, distribution and consumption of energy by incorporating IoT devices (such as sensors, actuators and smart meters), as well as by providing the connectivity, automation and tracking for such devices. In this paper, we provide a comprehensive survey on IoT-aided SG systems, which includes the existing architectures, applications and prototypes of IoT-aided SG systems. This survey also highlights the open issues, challenges and future research directions for IoT-aided SG systems

A Generic Multi-Layer Architecture Based on ROS-JADE Integration for Autonomous Transport Vehicles

The design and operation of manufacturing systems is evolving to adapt to different challenges. One of the most important is the reconfiguration of the manufacturing process in response to context changes (e.g., faulty equipment or urgent orders, among others). In this sense, the Autonomous Transport Vehicle (ATV) plays a key role in building more flexible and decentralized manufacturing systems. Nowadays, robotic frameworks (RFs) are used for developing robotic systems such as ATVs, but they focus on the control of the robotic system itself. However, social abilities are required for performing intelligent interaction (peer-to-peer negotiation and decision-making) among the different and heterogeneous Cyber Physical Production Systems (such as machines, transport systems and other equipment present in the factory) to achieve manufacturing reconfiguration. This work contributes a generic multi-layer architecture that integrates a RF with a Multi-Agent System (MAS) to provide social abilities to ATVs. This architecture has been implemented on ROS and JADE, the most widespread RF and MAS framework, respectively. We believe this to be the first work that addresses the intelligent interaction of transportation systems for flexible manufacturing environments in a holistic form.This work was financed by MINECO/FEDER, UE (grant number DPI2015-68602-R) and by UPV/EHU (grant number PPG17/56)

Integrating Degradation Forecasting and Abatement Framework Into Advanced Distribution Management System

Future distribution grids are expected to face an increasing penetration of heterogeneous distributed energy resources (DERs) and electric vehicles (EVs). This landscape change will pose challenges to the control and management of distribution grids because of the variability of renewable energy resources and EV charging. In addition, multiple DERs dispersed over networks can also challenge the grid operation and maintenance as various DERs at various locations are needed to be monitored and managed. However, customers will not be content with reductions in power quality, reliability, economy, safety, or security. To enhance the effectiveness of grid control and management, future grids will be given more autonomy in the form of advanced distribution management systems (ADMS). Energy management (EM) is one of the main constituents of ADMS to enhance system efficiency. EM typically considers only saving fuel consumption costs. However, grids’ components degrade over time, and it adds up to the systems’ operation cost. Knowing the degradation behaviors of grids’ components to control them properly can reduce their degradation, and consequentially it can reduce the total operation cost. In addition, in order to maintain the highest reliability of the system, degradation models should also be developed along with appropriate decision-making strategies that allow information regarding components’ status to be integrated with ADMS. This dissertation proposes a framework to integrate a degradation forecasting (DF) layer into ADMS to abate components’ degradation processes, reduce the total operation cost, and enhance system reliability. The DF layer will collaborate with EM to find a solution that compromises fuel consumption costs and degradation costs

Federated Learning in Intelligent Transportation Systems: Recent Applications and Open Problems

Intelligent transportation systems (ITSs) have been fueled by the rapid development of communication technologies, sensor technologies, and the Internet of Things (IoT). Nonetheless, due to the dynamic characteristics of the vehicle networks, it is rather challenging to make timely and accurate decisions of vehicle behaviors. Moreover, in the presence of mobile wireless communications, the privacy and security of vehicle information are at constant risk. In this context, a new paradigm is urgently needed for various applications in dynamic vehicle environments. As a distributed machine learning technology, federated learning (FL) has received extensive attention due to its outstanding privacy protection properties and easy scalability. We conduct a comprehensive survey of the latest developments in FL for ITS. Specifically, we initially research the prevalent challenges in ITS and elucidate the motivations for applying FL from various perspectives. Subsequently, we review existing deployments of FL in ITS across various scenarios, and discuss specific potential issues in object recognition, traffic management, and service providing scenarios. Furthermore, we conduct a further analysis of the new challenges introduced by FL deployment and the inherent limitations that FL alone cannot fully address, including uneven data distribution, limited storage and computing power, and potential privacy and security concerns. We then examine the existing collaborative technologies that can help mitigate these challenges. Lastly, we discuss the open challenges that remain to be addressed in applying FL in ITS and propose several future research directions