IoT Security and Computation Management on a Multi-Robot System for Rescue Operations Based on a Cloud Framework.

There is a growing body of literature that recognizes the importance of Multi-Robot coordination and Modular Robotics. This work evaluates the secure coordination of an Unmanned Aerial Vehicle (UAV) via a drone simulation in Unity and an Unmanned Ground Vehicle (UGV) as a rover. Each robot is equipped with sensors to gather information to send to a cloud server where all computations are performed. Each vehicle is registered by blockchain ledger-based network security. In addition to these, relevant information and alerts are displayed on a website for the users. The usage of UAV-UGV cooperation allows for autonomous surveillance due to the high vantage field of view. Furthermore, the usage of cloud computation lowers the cost of microcontrollers by reducing their complexity. Lastly, blockchain technology mitigates the security issues related to adversarial or malicious robotic nodes connecting to the cluster and not agreeing to privacy rules and norms.

Internet de las cosas en el ámbito de la atención médica: tendencias y desafíos / Internet of Things in the Field of Health Care: Trends and Challenges

La internet de las cosas ha mantenido un crecimiento continuo en los últimos años. Las potencialidades de uso que muestra en diferentes campos han sido ampliamente documentadas. Su utilización efectiva en el campo de la salud puede traer consigo mejoras en la eficiencia de los tratamientos médicos, prevenir situaciones de riesgo, ayudar a elevar la calidad del servicio y proporcionar soporte a la toma de decisiones. La presente revisión profundiza en aspectos medulares de su utilización con el objetivo de explorar las principales tendencias y desafíos relacionados con la creciente utilización de la internet de las cosas en la salud, prestando mayor atención a los aspectos relacionados con las arquitecturas utilizadas para el despliegue de sistemas de internet de las cosas en ese ámbito, el manejo de la seguridad de estos sistemas y las herramientas para el apoyo a la toma de decisiones empleadas. Mediante el análisis documental se logra mostrar las principales características de estos sistemas, así como su arquitectura, herramientas utilizadas para la gestión de los datos capturados y mecanismos de seguridad. La utilización de la internet de las cosas en el campo de la salud tiene gran impacto, mejorando la vida de millones de personas en todo el mundo y brindando grandes oportunidades para el desarrollo de sistemas inteligentes de salud(AU)

The internet of things has maintained continuous growth in recent years. The potentialities of use that it shows in different fields have been widely documented. Its effective use in the field of health can bring improvements in the efficiency of medical treatments, prevention of risky situations, help raising the quality of service and provide support for decision-making. The present review explores into core aspects of its use in order to analyze trends, challenges and strengths. Document analysis was used to show the main characteristics of these systems, as well as their architecture, tools used for the management of the captured data and security mechanisms. The use of the internet of things in the health field has a great impact, improving the lives of millions of people around the world and providing great opportunities for the development of intelligent health systems(AU)

Application of AI and IoT in Clinical Medicine: Summary and Challenges.

The application of artificial intelligence (AI) technology in the medical field has experienced a long history of development. In turn, some long-standing points and challenges in the medical field have also prompted diverse research teams to continue to explore AI in depth. With the development of advanced technologies such as the Internet of Things (IoT), cloud computing, big data, and 5G mobile networks, AI technology has been more widely adopted in the medical field. In addition, the in-depth integration of AI and IoT technology enables the gradual improvement of medical diagnosis and treatment capabilities so as to provide services to the public in a more effective way. In this work, we examine the technical basis of IoT, cloud computing, big data analysis and machine learning involved in clinical medicine, combined with concepts of specific algorithms such as activity recognition, behavior recognition, anomaly detection, assistant decision-making system, to describe the scenario-based applications of remote diagnosis and treatment collaboration, neonatal intensive care unit, cardiology intensive care unit, emergency first aid, venous thromboembolism, monitoring nursing, image-assisted diagnosis, etc. We also systematically summarize the application of AI and IoT in clinical medicine, analyze the main challenges thereof, and comment on the trends and future developments in this field.

Pindex: Private multi-linked index for encrypted document retrieval.

Cryptographic cloud storage is used to make optimal use of the cloud storage infrastructure to outsource sensitive and mission-critical data. The continuous growth of encrypted data outsourced to cloud storage requires continuous updating. Attacks like file-injection are reported to compromise confidentiality of the user as a consequence of information leakage during update. It is required that dynamic schemes provide forward privacy guarantees. Updates should not leak information to the untrusted server regarding the previously issued queries. Therefore, the challenge is to design an efficient searchable encryption scheme with dynamic updates and forward privacy guarantees. In this paper, a novel private multi-linked dynamic index for encrypted document retrieval namely Pindex is proposed. The multi-linked dynamic index is constructed using probabilistic homomorphic encryption mechanism and secret orthogonal vectors. Full security proofs for correctness and forward privacy in the random oracle model is provided. Experiments on real world Enron dataset demonstrates that our construction is practical and efficient. The security and performance analysis of Pindex shows that the dynamic multi-linked index guarantees forward privacy without significant loss of efficiency.

Clouds and Silver Linings: COVID-19 Pandemic Is an Opportune Moment to Democratize Diabetes Care Through Telehealth.

With the recent pivot to telehealth as a direct result of the COVID-19 pandemic, there is an imperative to ensure that access to affordable devices and technologies with remote monitoring capabilities for people with diabetes becomes equitable. In addition, expanding the use of remote Diabetes Self-Management Education and Support (DSMES) and Medical Nutrition Therapy (MNT) services will require new strategies for achieving long-term, effective, continuous, data-driven care. The current COVID-19 pandemic has especially impacted underserved US communities that were already disproportionately impacted by diabetes. Historically, these same communities have faced barriers in accessing timely and effective diabetes care including access to DSMES and MNT services, and diabetes technologies. Our call to action encourages all involved to urge US Federal representatives to widen access to the array of technologies necessary for successful telehealth-delivered care beyond COVID-19.

A generic construction for revocable identity-based encryption with subset difference methods.

To deal with dynamically changing user's credentials in identity-based encryption (IBE), providing an efficient key revocation method is a very important issue. Recently, Ma and Lin proposed a generic method of designing a revocable IBE (RIBE) scheme that uses the complete subtree (CS) method by combining IBE and hierarchical IBE (HIBE) schemes. In this paper, we propose a new generic method for designing an RIBE scheme that uses the subset difference (SD) method instead of using the CS method. In order to use the SD method, we generically design an RIBE scheme by combining IBE, identity-based revocation (IBR), and two-level HIBE schemes. If the underlying IBE, IBR, and HIBE schemes are adaptively (or selectively) secure, then our RIBE scheme is also adaptively (or selectively) secure. In addition, we show that the layered SD (LSD) method can be applied to our RIBE scheme and a chosen-ciphertext secure RIBE scheme also can be designed generically.

Incentivizing for Truth Discovery in Edge-assisted Large-scale Mobile Crowdsensing.

The recent development of human-carried mobile devices has promoted the great development of mobile crowdsensing systems. Most existing mobile crowdsensing systems depend on the crowdsensing service of the deep cloud. With the increasing scale and complexity, there is a tendency to enhance mobile crowdsensing with the edge computing paradigm to reduce latency and computational complexity, and improve the expandability and security. In this paper, we propose an integrated solution to stimulate the strategic users to contribute more for truth discovery in the edge-assisted mobile crowdsensing. We design an incentive mechanism consisting of truth discovery stage and budget feasible reverse auction stage. In truth discovery stage, we estimate the truth for each task in both deep cloud and edge cloud. In budget feasible reverse auction stage, we design a greedy algorithm to select the winners to maximize the quality function under the budget constraint. Through extensive simulations, we demonstrate that the proposed mechanism is computationally efficient, individually rational, truthful, budget feasible and constant approximate. Moreover, the proposed mechanism shows great superiority in terms of estimation precision and expandability.

The role of narrative in collaborative reasoning and intelligence analysis: A case study.

This paper explores the significance of narrative in collaborative reasoning using a qualitative case study of two teams of intelligence analysts who took part in an exercise using an online collaborative platform. Digital ethnographic methods were used to analyze the chat transcripts of analysts as they reasoned with evidence provided in a difficult, fictional intelligence-type problem and produced a final intelligence report. These chat transcripts provided a powerful "microscope" into the reasoning processes and interactions involved in complex, collaborative reasoning. We found that Individuals and teams used narrative to solve the kinds of complex problems organizations and intelligence agencies face daily. We observed that team members generated what we term "micro-narratives", which provided a means for testing, assessing and weighing alternative hypotheses through mental simulation in the context of collaborative reasoning. The creation of micro-narratives assisted in the teams' reasoning with evidence, an integral part of collaborative reasoning and intelligence analysis. Micro-narratives were combined into, and compared with, an ideal or 'virtual' narrative which informed the judgements the team came to in their final intelligence report. The case study developed in this paper provides evidence that narrative thought processes play an important role in complex collaborative problem-solving, reasoning with evidence and problem-solving. This is contrary to a widespread perception that narrative thinking is fundamentally distinct from formal, logical reasoning.

Data Lakes, Clouds, and Commons: A Review of Platforms for Analyzing and Sharing Genomic Data.

Data commons collate data with cloud computing infrastructure and commonly used software services, tools, and applications to create biomedical resources for the large-scale management, analysis, harmonization, and sharing of biomedical data. Over the past few years, data commons have been used to analyze, harmonize, and share large-scale genomics datasets. Data ecosystems can be built by interoperating multiple data commons. It can be quite labor intensive to curate, import, and analyze the data in a data commons. Data lakes provide an alternative to data commons and simply provide access to data, with the data curation and analysis deferred until later and delegated to those that access the data. We review software platforms for managing, analyzing, and sharing genomic data, with an emphasis on data commons, but also cover data ecosystems and data lakes.

Cybersecurity and privacy issues for socially integrated mobile healthcare applications operating in a multi-cloud environment.

Social media has enabled information-sharing across massively large networks of people without spending much financial resources and time that are otherwise required in the print and electronic media. Mobile-based social media applications have overwhelmingly changed the information-sharing perspective. However, with the advent of such applications at an unprecedented scale, the privacy of the information is compromised to a larger extent if breach mitigation is not adequate. Since healthcare applications are also being developed for mobile devices so that they also benefit from the power of social media, cybersecurity privacy concerns for such sensitive applications have become critical. This article discusses the architecture of a typical mobile healthcare application, in which customized privacy levels are defined for the individuals participating in the system. It then elaborates on how the communication across a social network in a multi-cloud environment can be made more secure and private, especially for healthcare applications.

An improved robust heteroscedastic probabilistic neural network based trust prediction approach for cloud service selection.

Trustworthiness is a comprehensive quality metric which is used to assess the quality of the services in service-oriented environments. However, trust prediction of cloud services based on the multi-faceted Quality of Service (QoS) attributes is a challenging task due to the complicated and non-linear relationships between the QoS values and the corresponding trust result. Recent research works reveal the significance of Artificial Neural Network (ANN) and its variants in providing a reasonable degree of success in trust prediction problems. However, the challenges with respect to weight assignment, training time and kernel functions make ANN and its variants under continuous advancements. Hence, this work presents a novel multi-level Hypergraph Coarsening based Robust Heteroscedastic Probabilistic Neural Network (HC-RHRPNN) to predict trustworthiness of cloud services to build high-quality service applications. HC-RHRPNN employs hypergraph coarsening to identify the informative samples, which were then used to train HRPNN to improve its prediction accuracy and minimize the runtime. The performance of HC-RHRPNN was evaluated using Quality of Web Service (QWS) dataset, a public QoS dataset in terms of classifier accuracy, precision, recall, and F-Score.

Cloud Computing for Infectious Disease Surveillance and Control: Development and Evaluation of a Hospital Automated Laboratory Reporting System.

BACKGROUND: Outbreaks of several serious infectious diseases have occurred in recent years. In response, to mitigate public health risks, countries worldwide have dedicated efforts to establish an information system for effective disease monitoring, risk assessment, and early warning management for international disease outbreaks. A cloud computing framework can effectively provide the required hardware resources and information access and exchange to conveniently connect information related to infectious diseases and develop a cross-system surveillance and control system for infectious diseases. OBJECTIVE: The objective of our study was to develop a Hospital Automated Laboratory Reporting (HALR) system based on such a framework and evaluate its effectiveness. METHODS: We collected data for 6 months and analyzed the cases reported within this period by the HALR and the Web-based Notifiable Disease Reporting (WebNDR) systems. Furthermore, system evaluation indicators were gathered, including those evaluating sensitivity and specificity. RESULTS: The HALR system reported 15 pathogens and 5174 cases, and the WebNDR system reported 34 cases. In a comparison of the two systems, sensitivity was 100% and specificity varied according to the reported pathogens. In particular, the specificity for Streptococcus pneumoniae, Mycobacterium tuberculosis complex, and hepatitis C virus were 99.8%, 96.6%, and 97.4%, respectively. However, the specificity for influenza virus and hepatitis B virus were only 79.9% and 47.1%, respectively. After the reported data were integrated with patients' diagnostic results in their electronic medical records (EMRs), the specificity for influenza virus and hepatitis B virus increased to 89.2% and 99.1%, respectively. CONCLUSIONS: The HALR system can provide early reporting of specified pathogens according to test results, allowing for early detection of outbreaks and providing trends in infectious disease data. The results of this study show that the sensitivity and specificity of early disease detection can be increased by integrating the reported data in the HALR system with the cases' clinical information (eg, diagnostic results) in EMRs, thereby enhancing the control and prevention of infectious diseases.

Caregiver and Health Care Provider Perspectives on Cloud-Based Shared Care Plans for Children With Medical Complexity.

OBJECTIVES: Shared care plans play an essential role in coordinating care across health care providers and settings for children with medical complexity (CMC). However, existing care plans often lack shared ownership, are out-of-date, and lack universal accessibility. In this study, we aimed to establish requirements for shared care plans to meet the information needs of caregivers and providers and to mitigate current information barriers when caring for CMC. METHODS: We followed a user-centered design methodology and conducted in-depth semistructured interviews with caregivers and providers of CMC who receive care at a tertiary care children's hospital. We applied inductive, thematic analysis to identify salient themes. Analysis occurred concurrently with data collection; therefore, the interview guide was iteratively revised as new questions and themes emerged. RESULTS: Interviews were conducted with 17 caregivers and 22 providers. On the basis of participant perspectives, we identified 4 requirements for shared care plans that would help meet information needs and mitigate current information barriers when caring for CMC. These requirements included the following: (1) supporting the accessibility of care plans from multiple locations (eg, cloud-based) and from multiple devices, with alert and search features; (2) ensuring the organization is tailored to the specific user; (3) including collaborative functionality such as real-time, multiuser content management and secure messaging; and (4) storing care plans on a secure platform with caregiver-controlled permission settings. CONCLUSIONS: Although further studies are needed to understand the optimal design and implementation strategies, shared care plans that meet these specified requirements could mitigate perceived information barriers and improve care for CMC.

A new telemetry-based system for assessing cardiovascular function in group-housed large animals. Taking the 3Rs to a new level with the evaluation of remote measurement via cloud data transmission.

INTRODUCTION: A newly developed total implant telemetry system for cardiovascular (CV), electrophysiological and body temperature measurement was evaluated. A cloud-based transmission of the physiological signals allowed an assessment of the quality of the physiological signals despite the physical separation between the instrumented animals and the evaluating home laboratory. The new system is intended to be used for safety pharmacological evaluations of drug candidates in various species. METHODS: Two female minipigs, 6 Labrador-mixed breed dogs and 4 female Cynomolgus monkeys were instrumented with a newly developed total implant system (TSE SYSTEMS). The implants feature a microprocessor, internal memory (1 GB), 2 solid state pressure-tipped catheters, amplifiers and a radio transmitter. Sampling rates for each measurement can be selected within a range between 0.1 and 1 kHz. Biological signals are selected in a programmable fashion on a session-by-session basis according to a user-defined protocol. The pressure sensors are at the tip of an electrical lead having a length customized to each species. Core temperature measurement and activity monitoring (3D accelerometer) are included in the system. Digital transmission range using a single antenna is 5 m with up to 16 animals held together and monitored simultaneously. The range can be expanded with more antennas in an array coupled to a single receiver. The antenna/receiver station consists of a single USB powered mobile unit connected to a PC or laptop. The battery life provides 110 days of continuous recording. The dogs and minipigs were instrumented and monitored in Germany. A novel cloud-based data transmission system was developed to monitor the physiological signals in real-time from the Cynomolgus monkeys, still kept in Mauritius, from the data evaluation laboratory in Germany. After recovery from the surgical implantation, aortic pressure (AP), left ventricular pressure (LVP), ECG and body temperature were recorded for 24 hr monitoring sessions in all animals. Additionally, moxifloxacin (10, 30 and 100 mg/kg) was tested in the dog model using a modified Latin square cross-over study design. RESULTS: The implant was well tolerated and the animals recovered rapidly from the implantation procedure. Excellent signal quality was obtained and stable hemodynamic and electrophysiological parameters could be measured, with little signal artefact or drop-out, over 24 h in each species. After oral dosing of moxifloxacin to the dogs, a substantial, dose-dependent increase in the QT-interval duration could be shown, as anticipated for this agent. Cloud-based data acquisition from the animals in Mauritius and the data evaluation lab in Germany worked well. CONCLUSION: This new CV telemetry system provides a novel alternative to fluid-filled catheter telemetry systems and the coupling to a cloud-based data transmission allows for flexibility in the location of the instrumented animals and data acquisition and the location of the site for data analysis. For the first time it is technically feasible to conduct a CV safety pharmacology study in Cynomolgus monkeys without having to ship them long distances to the home laboratory.

Government Cloud Computing Policies: Potential Opportunities for Advancing Military Biomedical Research.

Introduction: This position paper summarizes the development and the present status of Department of Defense (DoD) and other government policies and guidances regarding cloud computing services. Due to the heterogeneous and growing biomedical big datasets, cloud computing services offer an opportunity to mitigate the associated storage and analysis requirements. Having on-demand network access to a shared pool of flexible computing resources creates a consolidated system that should reduce potential duplications of effort in military biomedical research. Methods: Interactive, online literature searches were performed with Google, at the Defense Technical Information Center, and at two National Institutes of Health research portfolio information sites. References cited within some of the collected documents also served as literature resources. Results: We gathered, selected, and reviewed DoD and other government cloud computing policies and guidances published from 2009 to 2017. These policies were intended to consolidate computer resources within the government and reduce costs by decreasing the number of federal data centers and by migrating electronic data to cloud systems. Initial White House Office of Management and Budget information technology guidelines were developed for cloud usage, followed by policies and other documents from the DoD, the Defense Health Agency, and the Armed Services. Security standards from the National Institute of Standards and Technology, the Government Services Administration, the DoD, and the Army were also developed. Government Services Administration and DoD Inspectors General monitored cloud usage by the DoD. A 2016 Government Accountability Office report characterized cloud computing as being economical, flexible and fast. A congressionally mandated independent study reported that the DoD was active in offering a wide selection of commercial cloud services in addition to its milCloud system. Our findings from the Department of Health and Human Services indicated that the security infrastructure in cloud services may be more compliant with the Health Insurance Portability and Accountability Act of 1996 regulations than traditional methods. To gauge the DoD's adoption of cloud technologies proposed metrics included cost factors, ease of use, automation, availability, accessibility, security, and policy compliance. Conclusions: Since 2009, plans and policies were developed for the use of cloud technology to help consolidate and reduce the number of data centers which were expected to reduce costs, improve environmental factors, enhance information technology security, and maintain mission support for service members. Cloud technologies were also expected to improve employee efficiency and productivity. Federal cloud computing policies within the last decade also offered increased opportunities to advance military healthcare. It was assumed that these opportunities would benefit consumers of healthcare and health science data by allowing more access to centralized cloud computer facilities to store, analyze, search and share relevant data, to enhance standardization, and to reduce potential duplications of effort. We recommend that cloud computing be considered by DoD biomedical researchers for increasing connectivity, presumably by facilitating communications and data sharing, among the various intra- and extramural laboratories. We also recommend that policies and other guidances be updated to include developing additional metrics that will help stakeholders evaluate the above mentioned assumptions and expectations.

NAPR: a Cloud-Based Framework for Neuroanatomical Age Prediction.

The availability of cloud computing services has enabled the widespread adoption of the "software as a service" (SaaS) approach for software distribution, which utilizes network-based access to applications running on centralized servers. In this paper we apply the SaaS approach to neuroimaging-based age prediction. Our system, named "NAPR" (Neuroanatomical Age Prediction using R), provides access to predictive modeling software running on a persistent cloud-based Amazon Web Services (AWS) compute instance. The NAPR framework allows external users to estimate the age of individual subjects using cortical thickness maps derived from their own locally processed T1-weighted whole brain MRI scans. As a demonstration of the NAPR approach, we have developed two age prediction models that were trained using healthy control data from the ABIDE, CoRR, DLBS and NKI Rockland neuroimaging datasets (total N = 2367, age range 6-89 years). The provided age prediction models were trained using (i) relevance vector machines and (ii) Gaussian processes machine learning methods applied to cortical thickness surfaces obtained using Freesurfer v5.3. We believe that this transparent approach to out-of-sample evaluation and comparison of neuroimaging age prediction models will facilitate the development of improved age prediction models and allow for robust evaluation of the clinical utility of these methods.