Health 4.0: Applications, Management, Technologies and Review

The Industry 4.0 Standard (I4S) employs technologies for automation and data exchange through cloud computing, Big Data (BD), Internet of Things (IoT), forms of wireless Internet, 5G technologies, cryptography, the use of semantic database (DB) design, Augmented Reality (AR) and Content-Based Image Retrieval (CBIR). Its healthcare extension is the so-called Health 4.0. This study informs about Health 4.0 and its potential to extend, virtualize and enable new healthcare-related processes (e.g., home care, finitude medicine, and personalized/remotely triggered pharmaceutical treatments) and transform them into services. In the future, these services will be able to virtualize multiple levels of care, connect devices and move to Personalized Medicine (PM). The Health 4.0 Cyber-Physical System (HCPS) contains several types of computers, communications, storage, interfaces, biosensors, and bioactuators. The HCPS paradigm permits observing processes from the real world, as well as monitoring patients before, during and after surgical procedures using biosensors. Besides, HCPSs contain bioactuators that accomplish the intended interventions along with other novel strategies to deploy PM. A biosensor detects some critical outer and inner patient conditions and sends these signals to a Decision-Making Unit (DMU). Mobile devices and wearables are present examples of gadgets containing biosensors. Once the DMU receives signals, they can be compared to the patient’s medical history and, depending on the protocols, a set of measures to handle a given situation will follow. The part responsible for the implementation of the automated mitigation actions are the bioactuators, which can vary from a buzzer to the remote-controlled release of some elements in a capsule inside the patient’s body.             Decentralizing health services is a challenge for the creation of health-related applications. Together, CBIR systems can enable access to information from multimedia and multimodality images, which can aid in patient diagnosis and medical decision-making. Currently, the National Health Service addresses the application of communication tools to patients and medical teams to intensify the transfer of treatments from the hospital to the home, without disruption in outpatient services. HCPS technologies share tools with remote servers, allowing data embedding and BD analysis and permit easy integration of healthcare professionals expertise with intelligent devices.  However, it is undeniable the need for improvements, multidisciplinary discussions, strong laws/protocols, inventories about the impact of novel techniques on patients/caregivers as well as rigorous tests of accuracy until reaching the level of automating any medical care technological initiative

IEEE Access Special Section Editorial: Cloud-Fog-Edge Computing in Cyber–Physical–Social Systems (CPSS)

Cyber–Physical–Social Systems (CPSS) integrate the cyber, physical, and social spaces together. One of the ultimate goals of cyber–physical–social systems is to make our lives more convenient and intelligent by providing prospective and personalized services for users. To achieve this goal, a wide range of data in CPSS are employed as the starting point for research, since the data contain the users' historical behavior trajectory and the users' demand preference. Generated and collected from social and physical spaces and integrated into the cyberspace, CPSS data are complex and heterogeneous, recording all aspects of users' lives in the forms of image, audio, video, and text. Generally, the collected or generated data in CPSS satisfy the 4Vs (volume, variety, velocity, and veracity) of big data. Thus, knowing how to deal with CPSS big data efficiently is the key to providing services for users. From another perspective, CPSS big data are specified as the global historical data and the local real-time data. Cloud computing, as a powerful paradigm for implementing the data-intensive applications, has an irreplaceable role in processing global historical data. On the other hand, with the increasing computing capacity and communication capabilities of mobile terminal devices, fog-edge computing, as an important and effective supplement of cloud computing, has been widely used to process local real-time data. Therefore, the question of how to systematically and efficiently process CPSS big data (including both the global historical data and the local real-time data) in CPSS has become the key for providing services. The goal of this Special Section is therefore to provide insights and views into the area of Cloud-Fog-Edge Computing in CPSS, as well as to provide directions for research in the field

Cyber physical security of avionic systems

“Cyber-physical security is a significant concern for critical infrastructures. The exponential growth of cyber-physical systems (CPSs) and the strong inter-dependency between the cyber and physical components introduces integrity issues such as vulnerability to injecting malicious data and projecting fake sensor measurements. Traditional security models partition the CPS from a security perspective into just two domains: high and low. However, this absolute partition is not adequate to address the challenges in the current CPSs as they are composed of multiple overlapping partitions. Information flow properties are one of the significant classes of cyber-physical security methods that model how inputs of a system affect its outputs across the security partition. Information flow supports traceability that helps in detecting vulnerabilities and anomalous sources, as well as helps in rendering mitigation measures. To address the challenges associated with securing CPSs, two novel approaches are introduced by representing a CPS in terms of a graph structure. The first approach is an automated graph-based information flow model introduced to identify information flow paths in the avionics system and partition them into security domains. This approach is applied to selected aspects of the avionic systems to identify the vulnerabilities in case of a system failure or an attack and provide possible mitigation measures. The second approach is based on graph neural networks (GNN) to classify the graphs into different security domains. Using these two approaches, successful partitioning of the CPS into different security domains is possible in addition to identifying their optimal coverage. These approaches enable designers and engineers to ensure the integrity of the CPS. The engineers and operators can use this process during design-time and in real-time to identify failures or attacks on the system”--Abstract, page iii

Road2CPS priorities and recommendations for research and innovation in cyber-physical systems

This document summarises the findings of the Road2CPS project, co-financed by the European Commission under the H2020 Research and Innovation Programme, to develop a roadmap and recommendations for strategic action required for future deployment of Cyber-Physical Systems (CPS). The term Cyber-Physical System describes hardware-software systems, which tightly couple the physical world and the virtual world. They are established from networked embedded systems that are connected with the outside world through sensors and actuators and have the capability to collaborate, adapt, and evolve. In the ARTEMIS Strategic Research Agenda 2016, CPS are described as ‘Embedded Intelligent ICT Systems’ that make products smarter, more interconnected, interdependent, collaborative, and autonomous. In the future world of CPS, a huge number of devices connected to the physical world will be able to exchange data with each other, access web services, and interact with people. Moreover, information systems will sense, monitor and even control the physical world via Cyber-Physical Systems and the Internet of Things (HiPEAC Vision 2015). Cyber-Physical Systems find their application in many highly relevant areas to our society: multi-modal transport, health, smart factories, smart grids and smart cities amongst others. The deployment of Cyber-Physical Systems (CPS) is expected to increase substantially over the next decades, holding great potential for novel applications and innovative product development. Digital technologies have already pervaded day-to-day life massively, affecting all kinds of interactions between humans and their environment. However, the inherent complexity of CPSs, as well as the need to meet optimised performance and comply with essential requirements like safety, privacy, security, raises many questions that are currently being explored by the research community. Road2CPS aims at accelerating uptake and implementation of these efforts. The Road2CPS project identifying and analysing the relevant technology fields and related research priorities to fuel the development of trustworthy CPS, as well as the specific technologies, needs and barriers for a successful implementation in different application domains and to derive recommendations for strategic action. The document at hand was established through an interactive, community-based approach, involving over 300 experts from academia, industry and policy making through a series of workshops and consultations. Visions and priorities of recently produced roadmaps in the area of CPS, IoT (Internet of Things), SoS (System-of-Systems) and FoF (Factories of the Future) were discussed, complemented by sharing views and perspectives on CPS implementation in application domains, evolving multi-sided eco-systems as well as business and policy related barriers, enablers and success factors. From the workshops and accompanying activities recommendations for future research and innovation activities were derived and topics and timelines for their implementation proposed. Amongst the technological topics, and related future research priorities ‘integration, interoperability, standards’ ranged highest in all workshops. The topic is connected to digital platforms and reference architectures, which have already become a key priority theme for the EC and their Digitisation Strategy as well as the work on the right standards to help successful implementation of CPSs. Other themes of very high technology/research relevance revealed to be ‘modelling and simulation’, ‘safety and dependability’, ‘security and privacy’, ‘big data and real-time analysis’, ‘ubiquitous autonomy and forecasting’ as well as ‘HMI/human machine awareness’. Next to this, themes emerged including ‘decision making and support’, ‘CPS engineering (requirements, design)’, ‘CPS life-cycle management’, ‘System-of-Systems’, ‘distributed management’, ‘cognitive CPS’, ‘emergence, complexity, adaptability and flexibility’ and work on the foundations of CPS and ‘cross-disciplinary research/CPS Science’

Enhancing Mobile App User Understanding and Marketing with Heterogeneous Crowdsourced Data: A Review

© 2013 IEEE. The mobile app market has been surging in recent years. It has some key differentiating characteristics which make it different from traditional markets. To enhance mobile app development and marketing, it is important to study the key research challenges such as app user profiling, usage pattern understanding, popularity prediction, requirement and feedback mining, and so on. This paper reviews CrowdApp, a research field that leverages heterogeneous crowdsourced data for mobile app user understanding and marketing. We first characterize the opportunities of the CrowdApp, and then present the key research challenges and state-of-the-art techniques to deal with these challenges. We further discuss the open issues and future trends of the CrowdApp. Finally, an evolvable app ecosystem architecture based on heterogeneous crowdsourced data is presented

Mobile Link Prediction: Automated Creation and Crowd-sourced Validation of Knowledge Graphs

Building trustworthy knowledge graphs for cyber-physical social systems (CPSS) is a challenge. In particular, current approaches relying on human experts have limited scalability, while automated approaches are often not accountable to users resulting in knowledge graphs of questionable quality. This paper introduces a novel pervasive knowledge graph builder that brings together automation, experts' and crowd-sourced citizens' knowledge. The knowledge graph grows via automated link predictions using genetic programming that are validated by humans for improving transparency and calibrating accuracy. The knowledge graph builder is designed for pervasive devices such as smartphones and preserves privacy by localizing all computations. The accuracy, practicality, and usability of the knowledge graph builder is evaluated in a real-world social experiment that involves a smartphone implementation and a Smart City application scenario. The proposed knowledge graph building methodology outperforms the baseline method in terms of accuracy while demonstrating its efficient calculations on smartphones and the feasibility of the pervasive human supervision process in terms of high interactions throughput. These findings promise new opportunities to crowd-source and operate pervasive reasoning systems for cyber-physical social systems in Smart Cities

Dejä vu and Recognition Memory in Temporal Lobe Epilepsy

Déjà vu is a uniquely curious experience with which many of us are familiar. The experience can be so transient and unpredictable that it typically subsides, just as quickly as it appeared, before one can engage in any meaningful introspective evaluation. At the core of the experience is an impression of familiarity that co-exists with the feeling that it is inappropriate. Currently, there is no consensus among researchers about which theory can best account for the cognitive and neural mechanisms that underlie the experience. The goal of the current study was to examine déjà vu in temporal-lobe epilepsy (TLE) within the framework of the cognitive dual process-model of recognition memory that distinguishes between familiarity and recollection. It was reasoned that TLE patients with déjà vu should also experience deficits in recognition memory inter-ictally and that the exact nature of these deficits might offer insight into the cognitive mechanisms underlying the pathological, subjective experience of déjà vu associated with their seizures. The particular hypothesis tested was that the memory impairments in TLE patients with déjà vu are selective or most pronounced in the domain of familiarity assessment. Toward this end, two experimental recognition memory tasks derived from the cognitive neuroscience literature were administered to patients with unilateral or bilateral seizure origin. In general, converging evidence from the two tasks administered suggests that unilateral TLE patients with déjà vu do indeed have selective familiarity impairments and intact recollection. However, in bilateral cases deficits were found to be broader and included impairments in recollection as well. Inaccurate feelings of familiarity may represent the functional consequence of seizure activity in a region of the MTL critical for assessing feelings of familiarity. Further, these data hint that the cognitive process responsible for identifying the inappropriateness of the sense of familiarity during déjà vu may not be recollection, as previously suggested in the literature. Together, the present findings suggest that probing the cognitive correlates of déjà vu in TLE inter-ictally can advance our understanding of mechanisms involved in déjà vu at a time when experimental paradigms to elicit the experience in the cognitive laboratory are still missing