Implementing an integrated meter and sensor system (IMSS) in existing social housing stock

The current rollout of smart meters for gas and electricity, both in the UK and internationally, will help suppliers to better forecast demand and supply accurate bills to consumers. However, even with an in-home display (IHD), the benefits of a smart meter to a domestic customer are limited by the so-called ‘double invisibility’ of energy [1] and the standardisation of IHD design for an imagined home ‘micro-resource manager’ [2]. Furthermore, low-income households may be limited in the benefits they can reap from such systems; already living within a tight budget, suggestions for further energy-related cost savings may be detrimental to their health and wellbeing. This makes it important that the impact of actions taken to save energy is communicated. This can be done using indoor environmental measures, including carbon dioxide, relative humidity and temperature, as part of an integrated meter and sensor system (IMSS) and an associated IHD or digital application. Such a system gives users the ability to make informed decisions about their energy use and indoor environmental health. This paper explores the potential barriers to implementing an IMSS in practice. It explains how an IMSS was designed, based on a review of meter and sensor systems; details the process is taken to trial the IMSS in 19 social housing properties in the English Midlands; and makes recommendations for a larger scale rollout of IMSSs. The paper also reviews current progress in cloud storage and security as relevant to IMSSs and smart metering

Innovative IoT Solutions and Wearable Sensing Systems for Monitoring Human Biophysical Parameters: A Review

none3noDigital and information technologies are heavily pervading several aspects of human activities, improving our life quality. Health systems are undergoing a real technological revolution, radically changing how medical services are provided, thanks to the wide employment of the Internet of Things (IoT) platforms supporting advanced monitoring services and intelligent inferring systems. This paper reports, at first, a comprehensive overview of innovative sensing systems for monitoring biophysical and psychophysical parameters, all suitable for integration with wearable or portable accessories. Wearable devices represent a headstone on which the IoT-based healthcare platforms are based, providing capillary and real-time monitoring of patient’s conditions. Besides, a survey of modern architectures and supported services by IoT platforms for health monitoring is presented, providing useful insights for developing future healthcare systems. All considered architectures employ wearable devices to gather patient parameters and share them with a cloud platform where they are processed to provide real-time feedback. The reported discussion highlights the structural differences between the discussed frameworks, from the point of view of network configuration, data management strategy, feedback modality, etc.Article Number: 1660openRoberto De Fazio; Massimo De Vittorio; Paolo ViscontiDE FAZIO, Roberto; DE VITTORIO, Massimo; Visconti, Paol

Networking Architecture and Key Technologies for Human Digital Twin in Personalized Healthcare: A Comprehensive Survey

Digital twin (DT), refers to a promising technique to digitally and accurately represent actual physical entities. One typical advantage of DT is that it can be used to not only virtually replicate a system's detailed operations but also analyze the current condition, predict future behaviour, and refine the control optimization. Although DT has been widely implemented in various fields, such as smart manufacturing and transportation, its conventional paradigm is limited to embody non-living entities, e.g., robots and vehicles. When adopted in human-centric systems, a novel concept, called human digital twin (HDT) has thus been proposed. Particularly, HDT allows in silico representation of individual human body with the ability to dynamically reflect molecular status, physiological status, emotional and psychological status, as well as lifestyle evolutions. These prompt the expected application of HDT in personalized healthcare (PH), which can facilitate remote monitoring, diagnosis, prescription, surgery and rehabilitation. However, despite the large potential, HDT faces substantial research challenges in different aspects, and becomes an increasingly popular topic recently. In this survey, with a specific focus on the networking architecture and key technologies for HDT in PH applications, we first discuss the differences between HDT and conventional DTs, followed by the universal framework and essential functions of HDT. We then analyze its design requirements and challenges in PH applications. After that, we provide an overview of the networking architecture of HDT, including data acquisition layer, data communication layer, computation layer, data management layer and data analysis and decision making layer. Besides reviewing the key technologies for implementing such networking architecture in detail, we conclude this survey by presenting future research directions of HDT

Internet of robotic things : converging sensing/actuating, hypoconnectivity, artificial intelligence and IoT Platforms

The Internet of Things (IoT) concept is evolving rapidly and influencing newdevelopments in various application domains, such as the Internet of MobileThings (IoMT), Autonomous Internet of Things (A-IoT), Autonomous Systemof Things (ASoT), Internet of Autonomous Things (IoAT), Internetof Things Clouds (IoT-C) and the Internet of Robotic Things (IoRT) etc.that are progressing/advancing by using IoT technology. The IoT influencerepresents new development and deployment challenges in different areassuch as seamless platform integration, context based cognitive network integration,new mobile sensor/actuator network paradigms, things identification(addressing, naming in IoT) and dynamic things discoverability and manyothers. The IoRT represents new convergence challenges and their need to be addressed, in one side the programmability and the communication ofmultiple heterogeneous mobile/autonomous/robotic things for cooperating,their coordination, configuration, exchange of information, security, safetyand protection. Developments in IoT heterogeneous parallel processing/communication and dynamic systems based on parallelism and concurrencyrequire new ideas for integrating the intelligent “devices”, collaborativerobots (COBOTS), into IoT applications. Dynamic maintainability, selfhealing,self-repair of resources, changing resource state, (re-) configurationand context based IoT systems for service implementation and integrationwith IoT network service composition are of paramount importance whennew “cognitive devices” are becoming active participants in IoT applications.This chapter aims to be an overview of the IoRT concept, technologies,architectures and applications and to provide a comprehensive coverage offuture challenges, developments and applications

FogLearn: Leveraging Fog-based Machine Learning for Smart System Big Data Analytics

Big data analytics with the cloud computing are one of the emerging area for processing and analytics. Fog computing is the paradigm where fog devices help to reduce latency and increase throughput for assisting at the edge of the client. This paper discussed the emergence of fog computing for mining analytics in big data from geospatial and medical health applications. This paper proposed and developed fog computing based framework i.e. FogLearn for application of K-means clustering in Ganga River Basin Management and realworld feature data for detecting diabetes patients suffering from diabetes mellitus. Proposed architecture employed machine learning on deep learning framework for analysis of pathological feature data that obtained from smart watches worn by the patients with diabetes and geographical parameters of River Ganga basin geospatial database. The results showed that fog computing hold an immense promise for analysis of medical and geospatial big data

Health Care Equity Through Intelligent Edge Computing and Augmented Reality/Virtual Reality: A Systematic Review

Intellectual capital is a scarce resource in the healthcare industry. Making the most of this resource is the first step toward achieving a completely intelligent healthcare system. However, most existing centralized and deep learning-based systems are unable to adapt to the growing volume of global health records and face application issues. To balance the scarcity of healthcare resources, the emerging trend of IoMT (Internet of Medical Things) and edge computing will be very practical and cost-effective. A full examination of the transformational role of intelligent edge computing in the IoMT era to attain health care equity is offered in this research. Intelligent edge computing-aided distribution and collaborative information management is a possible approach for a long-term digital healthcare system. Furthermore, IEC (Intelligent Edge Computing) encourages digital health data to be processed only at the edge, minimizing the amount of information exchanged with central servers/the internet. This significantly increases the privacy of digital health data. Another critical component of a sustainable healthcare system is affordability in digital healthcare. Affordability in digital healthcare is another key component of a sustainable healthcare system. Despite its importance, it has received little attention due to its complexity. In isolated and rural areas where expensive equipment is unavailable, IEC with AR / VR, also known as edge device shadow, can play a significant role in the inexpensive data collection process. Healthcare equity becomes a reality by combining intelligent edge device shadows and edge computing