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

Next-Gen Security: Leveraging Advanced Technologies for Social Medical Public Healthcare Resilience

The healthcare industry is undergoing a significant change as it incorporates advanced technologies to strengthen its security infrastructure and improve its ability to withstand current challenges and  explores the important overlap between security, technology, and public health. The introductory section presents a thorough overview, highlighting the current status of public healthcare and emphasizing the crucial importance of security in protecting confidential medical data. This statement highlights the current difficulties encountered by social medical public healthcare systems and emphasizes the urgent need to utilize advanced technologies to strengthen their ability to adapt and recover. The systematic literature review explores a wide range of studies, providing insight into the various aspects of healthcare security. This text examines conventional security methods, exposes their constraints, and advances the discussion by examining cutting-edge technologies such as Artificial Intelligence (AI), Machine Learning, Blockchain, Internet of Things (IoT), and Biometric Security Solutions. Every technology is carefully examined to determine its ability to strengthen healthcare systems against cyber threats and breaches, guaranteeing the confidentiality and accuracy of patient data. The methodology section provides a clear explanation of the research design, the process of selecting participants, and the strategies used for analyzing the data. The research seeks to evaluate the present security situation and determine the best methods for incorporating advanced technologies into healthcare systems, using either qualitative or quantitative methods. The following sections elucidate the security challenges inherent in social medical public healthcare, encompassing cyber threats and privacy concerns. Drawing on case studies, the paper illustrates successful implementations of advanced technologies in healthcare security, distilling valuable lessons and best practices. The recommendations section goes beyond the technical domain, exploring the policy implications and strategies for technological implementation. The exploration of regulatory frameworks, legal considerations, and ethical dimensions is conducted to provide guidance for the smooth integration of advanced technologies into healthcare systems. Healthcare professionals are encouraged to participate in training and awareness programs to ensure a comprehensive and efficient implementation. To summarize, the paper combines the results, highlighting the importance of utilizing advanced technologies to strengthen the security framework of social medical public healthcare. The significance of healthcare resilience is emphasized, and potential areas for future research are delineated. This research is an important resource that offers valuable insights and guidance for stakeholders, policymakers, and technologists who are dealing with the intricate field of healthcare security in the age of advanced technologies. DOI: https://doi.org/10.52710/seejph.48

MPCS: Mobile-based Patient Compliance System for Chronic Illness Care

More than 100 million Americans are currently living with at least one chronic health condition and expenditures on chronic diseases account for more than 75 percent of the $2.3 trillion cost of our healthcare system. To improve chronic illness care, patients must be empowered and engaged in health self-management. However, only half of all patients with chronic illness comply with treatment regimen. The self-regulation model, while seemingly valuable, needs practical tools to help patients adopt this self-centered approach for long-term care. \par In this position paper, we propose Mobile-phone based Patient Compliance System (MPCS) that can reduce the time-consuming and error-prone processes of existing self-regulation practice to facilitate self-reporting, non-compliance detection, and compliance reminders. The novelty of this work is to apply social-behavior theories to engineer the MPCS to positively influence patients\u27 compliance behaviors, including mobile-delivered contextual reminders based on association theory; mobile-triggered questionnaires based on self-perception theory; and mobile-enabled social interactions based on social-construction theory. We discuss the architecture and the research challenges to realize the proposed MPCS

MPCS: Mobile-based Patient Compliance System for Chronic Illness Care

More than 100 million Americans are currently living with at least one chronic health condition and expenditures on chronic diseases account for more than 75 percent of the $2.3 trillion cost of our healthcare system. To improve chronic illness care, patients must be empowered and engaged in health self-management. However, only half of all patients with chronic illness comply with treatment regimen. The self-regulation model, while seemingly valuable, needs practical tools to help patients adopt this self-centered approach for long-term care. \par In this position paper, we propose Mobile-phone based Patient Compliance System (MPCS) that can reduce the time-consuming and error-prone processes of existing self-regulation practice to facilitate self-reporting, non-compliance detection, and compliance reminders. The novelty of this work is to apply social-behavior theories to engineer the MPCS to positively influence patients\u27 compliance behaviors, including mobile-delivered contextual reminders based on association theory; mobile-triggered questionnaires based on self-perception theory; and mobile-enabled social interactions based on social-construction theory. We discuss the architecture and the research challenges to realize the proposed MPCS

User's Privacy in Recommendation Systems Applying Online Social Network Data, A Survey and Taxonomy

Recommender systems have become an integral part of many social networks and extract knowledge from a user's personal and sensitive data both explicitly, with the user's knowledge, and implicitly. This trend has created major privacy concerns as users are mostly unaware of what data and how much data is being used and how securely it is used. In this context, several works have been done to address privacy concerns for usage in online social network data and by recommender systems. This paper surveys the main privacy concerns, measurements and privacy-preserving techniques used in large-scale online social networks and recommender systems. It is based on historical works on security, privacy-preserving, statistical modeling, and datasets to provide an overview of the technical difficulties and problems associated with privacy preserving in online social networks.Comment: 26 pages, IET book chapter on big data recommender system