Secure Collaborative Augmented Reality Framework for Biomedical Informatics

Augmented reality is currently a great interest in biomedical health informatics. At the same time, several challenges have been appeared, in particular with the rapid progress of smart sensors technologies, and medical artificial intelligence. This yields the necessity of new needs in biomedical health informatics. Collaborative learning and privacy are some of the challenges of augmented reality technology in biomedical health informatics. This paper introduces a novel secure collaborative augmented reality framework for biomedical health informatics-based applications. Distributed deep learning is first performed across a multi-agent system platform. The privacy strategy is developed for ensuring better communications of the different intelligent agents in the system. In this research work, a system of multiple agents is created for the simulation of the collective behaviours of the smart components of biomedical health informatics. Augmented reality is also incorporated for better visualization of the resulted medical patterns. A novel privacy strategy based on blockchain is investigated for ensuring the confidentiality of the learning process. Experiments are conducted on the real use case of the biomedical segmentation process. Our strong experimental analysis reveals the strength of the proposed framework when directly compared to state-of-the-art biomedical health informatics solutions.acceptedVersio

Intelligent blockchain management for distributed knowledge graphs in IoT 5G environments

This article introduces a new problem of distributed knowledge graph, in IoT 5G setting. We developed an end-to-end solution for solving such problem by exploring the blockchain management and intelligent method for producing the better matching of the concepts and relations of the set of knowledge graphs. The concepts and the relations of the knowledge graphs are divided into several components, each of which contains similar concepts and relations. Instead of exploring the whole concepts and the relations of the knowledge graphs, only the representative of these components is compared during the matching process. The framework has outperformed state-of-the-art knowledge graph matching algorithms using different scenarios as input in the experiments. In addition, to confirm the usability of our suggested framework, an in-depth experimental analysis has been done; the results are very promising in both runtime and accuracy.publishedVersio

Современные методы обеспечения целостности данных в протоколах управления киберфизических систем

At present, the problem of creating methodological security of cyberphysical systems, in particular, the design and implementation of information security subsystems is acute. At the same time, the landscape of threats and vulnerabilities typical for a wide range of hardware and software technologies used in cyberphysical systems is extremely wide and complex. In this context, the security of application layer protocols is of paramount importance, as these protocols are the basis for interaction between applications and services running on different devices, as well as in cloud infrastructures. With the constant interaction of the systems under study with the real physical infrastructure, the challenge is to determine effective measures to ensure the integrity of the transferred control commands, as disruption of the performed critical processes can affect human life and health. The paper provides an analytical review of the main methods of data integrity assurance in management protocol of cyberphysical systems, as well as an overview of application layer protocols vulnerabilities widely used in cyberphysical systems of different types. Classical methods of data integrity assurance, new methods, in particular, blockchain, as well as the main directions of increasing the efficiency of data integrity protocols in cyberphysical systems are considered. Analysis of application layer vulnerabilities is carried out on the example of the most popular MQTT, CoAP, AMQP, DDS, XMPP specifications and their implementations. It is established that despite the presence of basic security mechanisms in all these protocols, researchers continue to regularly identify vulnerabilities in popular implementations, that often endangers critical infrastructure services. In the course of preparing the review of the existing methods of data integrity assurance for the examined class of systems, the key problems of these methods integration and ways of their solution were defined.В настоящее время остро стоит проблема создания методологического обеспечения безопасности киберфизических систем, в частности проектирования и реализации подсистем информационной безопасности. При этом ландшафт угроз и уязвимостей, характерных для применяемого в киберфизических системах широкого спектра аппаратных и программных технологий, чрезвычайно широк и сложен. В этом контексте безопасность протоколов прикладного уровня имеет первостепенное значение, поскольку эти протоколы лежат в основе взаимодействия между приложениями и службами, работающими на различных устройствах, а также в облачных инфраструктурах. В условиях постоянного взаимодействия исследуемых систем с реальной физической инфраструктурой актуальна проблема определения эффективных мер по обеспечению целостности передаваемых команд управления, поскольку нарушение выполняемых критически важных процессов может затрагивать жизнь и здоровье людей. Представлен обзор основных методов обеспечения целостности данных в протоколах управления киберфизических систем, а также обзор уязвимостей протоколов прикладного уровня, широко используемых в различных киберфизических системах. Рассмотрены классические методы обеспечения целостности и новые методы, в частности блокчейн, а также основные направления повышения эффективности протоколов обеспечения целостности данных в киберфизических системах. Анализ уязвимостей прикладного уровня проведен на примере наиболее популярных спецификаций MQTT, CoAP, AMQP, DDS, XMPP, а также их реализаций. Установлено, что несмотря на наличие во всех перечисленных протоколах базовых механизмов обеспечения безопасности, исследователи продолжают регулярно выявлять уязвимости в популярных реализациях, что зачастую ставит под угрозу сервисы критической инфраструктуры. В ходе подготовки обзора существующих методов обеспечения целостности данных для исследуемого класса систем были определены ключевые проблемы интеграции этих методов и способы их решения

Sensor data fusion for the industrial artificial intelligence of things

The emergence of smart sensors, artificial intelligence, and deep learning technologies yield artificial intelligence of things, also known as the AIoT. Sophisticated cooperation of these technologies is vital for the effective processing of industrial sensor data. This paper introduces a new framework for addressing the different challenges of the AIoT applications. The proposed framework is an intelligent combination of multi-agent systems, knowledge graphs and deep learning. Deep learning architectures are used to create models from different sensor-based data. Multi-agent systems can be used for simulating the collective behaviours of the smart sensors using IoT settings. The communication among different agents is realized by integrating knowledge graphs. Different optimizations based on constraint satisfaction as well as evolutionary computation are also investigated. Experimental analysis is undertaken to compare the methodology presented to state-of-the-art AIoT technologies. We show through experimentation that our designed framework achieves good performance compared to baseline solutions.publishedVersio

Hybrid intelligent framework for automated medical learning

This paper investigates the automated medical learning and proposes hybrid intelligent framework, called Hybrid Automated Medical Learning (HAML). The goal is the efficient combination of several intelligent components in order to automatically learn the medical data. Multi agents system is proposed by using distributed deep learning, and knowledge graph for learning medical data. The distributed deep learning is used for efficient learning of the different agents in the system, where the knowledge graph is used for dealing with heterogeneous medical data. To demonstrate the usefulness and accuracy of the HAML framework, intensive simulations on medical data were conducted. A wide range of experiments were conducted to verify the efficiency of the proposed system. Three case studies are discussed in this research, the first case study is related to process mining, and more precisely on the ability of HAML to detect relevant patterns from event medical data. The second case study is related to smart building, and the ability of HAML to recognize the different activities of the patients. The third one is related to medical image retrieval, and the ability of HAML to find the most relevant medical images according to the image query. The results show that the developed HAML achieves good performance compared to the most up-to-date medical learning models regarding both the computational and cost the quality of returned solutions.publishedVersio

Hybrid intelligent framework for automated medical learning

This paper investigates the automated medical learning and proposes hybrid intelligent framework, called Hybrid Automated Medical Learning (HAML). The goal is the efficient combination of several intelligent components in order to automatically learn the medical data. Multi agents system is proposed by using distributed deep learning, and knowledge graph for learning medical data. The distributed deep learning is used for efficient learning of the different agents in the system, where the knowledge graph is used for dealing with heterogeneous medical data. To demonstrate the usefulness and accuracy of the HAML framework, intensive simulations on medical data were conducted. A wide range of experiments were conducted to verify the efficiency of the proposed system. Three case studies are discussed in this research, the first case study is related to process mining, and more precisely on the ability of HAML to detect relevant patterns from event medical data. The second case study is related to smart building, and the ability of HAML to recognize the different activities of the patients. The third one is related to medical image retrieval, and the ability of HAML to find the most relevant medical images according to the image query. The results show that the developed HAML achieves good performance compared to the most up-to-date medical learning models regarding both the computational and cost the quality of returned solutionspublishedVersio

Convergence of Blockchain and Edge Computing for Secure and Scalable IIoT Critical Infrastructures in Industry 4.0

This is the author accepted manuscript. The final version is available from IEEE via the DOI in this recordCritical infrastructure systems are vital to underpin the functioning of a society and economy. Due to ever-increasing number of Internet-connected Internet-of-Things (IoTs) / Industrial IoT (IIoT), and high volume of data generated and collected, security and scalability are becoming burning concerns for critical infrastructures in industry 4.0. The blockchain technology is essentially a distributed and secure ledger that records all the transactions into a hierarchically expanding chain of blocks. Edge computing brings the cloud capabilities closer to the computation tasks. The convergence of blockchain and edge computing paradigms can overcome the existing security and scalability issues. In this paper, we first introduce the IoT/IIoT critical infrastructure in industry 4.0, and then we briefly present the blockchain and edge computing paradigms. After that, we show how the convergence of these two paradigms can enable secure and scalable critical infrastructures. Then, we provide a survey on state-of-the-art for security and privacy, and scalability of IoT/IIoT critical infrastructures. A list of potential research challenges and open issues in this area is also provided, which can be used as useful resources to guide future research.Engineering and Physical Sciences Research Council (EPSRC

A Dynamic Service Trading in a DLT-Assisted Industrial IoT Marketplace

With the increasing demand for digitalization and participation in Industry 4.0, new challenges have emerged concerning the market of digital services to compensate for the lack of processing, computation, and other resources within Industrial Internet of Things (IIoTs). At the same time, the complexity of interplay among stakeholders has grown in size, granularity, and variation of trust. In this paper, we consider an IIoT resource market with heterogeneous buyers such as manufacturer owners. The buyers interact with the resource supplier dynamically with specific resource demands. This work introduces a broker between the supplier and the buyers, equipped with Distributed Ledger Technologies (DLT) providing a service for market security and trustworthiness. We first model the DLT-assisted IIoT market analytically to determine an offline solution and understand the selfish interactions among different entities (buyers, supplier, broker). Considering the non-cooperative heterogeneous buyers in the dynamic market, we then follow an independent learners framework to determine an online solution. In particular, the decision-making procedures of buyers are modeled as a Partially Observable Markov Decision Process which is solved using independent Q-learning. We evaluate both the offline and online solutions with analytical simulations, and the results show that the proposed approaches successfully maximize players’ satisfaction. The results further demonstrate that independent Q-learners achieve equilibrium in a dynamic market even without the availability of complete information and communication, and reach a better solution compared to that of centralized Q-learning

Outdoor operations of multiple quadrotors in windy environment

Coordinated multiple small unmanned aerial vehicles (sUAVs) offer several advantages over a single sUAV platform. These advantages include improved task efficiency, reduced task completion time, improved fault tolerance, and higher task flexibility. However, their deployment in an outdoor environment is challenging due to the presence of wind gusts. The coordinated motion of a multi-sUAV system in the presence of wind disturbances is a challenging problem when considering collision avoidance (safety), scalability, and communication connectivity. Performing wind-agnostic motion planning for sUAVs may produce a sizeable cross-track error if the wind on the planned route leads to actuator saturation. In a multi-sUAV system, each sUAV has to locally counter the wind disturbance while maintaining the safety of the system. Such continuous manipulation of the control effort for multiple sUAVs under uncertain environmental conditions is computationally taxing and can lead to reduced efficiency and safety concerns. Additionally, modern day sUAV systems are susceptible to cyberattacks due to their use of commercial wireless communication infrastructure. This dissertation aims to address these multi-faceted challenges related to the operation of outdoor rotor-based multi-sUAV systems. A comprehensive review of four representative techniques to measure and estimate wind speed and direction using rotor-based sUAVs is discussed. After developing a clear understanding of the role wind gusts play in quadrotor motion, two decentralized motion planners for a multi-quadrotor system are implemented and experimentally evaluated in the presence of wind disturbances. The first planner is rooted in the reinforcement learning (RL) technique of state-action-reward-state-action (SARSA) to provide generalized path plans in the presence of wind disturbances. While this planner provides feasible trajectories for the quadrotors, it does not provide guarantees of collision avoidance. The second planner implements a receding horizon (RH) mixed-integer nonlinear programming (MINLP) model that is integrated with control barrier functions (CBFs) to guarantee collision-free transit of the multiple quadrotors in the presence of wind disturbances. Finally, a novel communication protocol using Ethereum blockchain-based smart contracts is presented to address the challenge of secure wireless communication. The U.S. sUAV market is expected to be worth $92 Billion by 2030. The Association for Unmanned Vehicle Systems International (AUVSI) noted in its seminal economic report that UAVs would be responsible for creating 100,000 jobs by 2025 in the U.S. The rapid proliferation of drone technology in various applications has led to an increasing need for professionals skilled in sUAV piloting, designing, fabricating, repairing, and programming. Engineering educators have recognized this demand for certified sUAV professionals. This dissertation aims to address this growing sUAV-market need by evaluating two active learning-based instructional approaches designed for undergraduate sUAV education. The two approaches leverages the interactive-constructive-active-passive (ICAP) framework of engagement and explores the use of Competition based Learning (CBL) and Project based Learning (PBL). The CBL approach is implemented through a drone building and piloting competition that featured 97 students from undergraduate and graduate programs at NJIT. The competition focused on 1) drone assembly, testing, and validation using commercial off-the-shelf (COTS) parts, 2) simulation of drone flight missions, and 3) manual and semi-autonomous drone piloting were implemented. The effective student learning experience from this competition served as the basis of a new undergraduate course on drone science fundamentals at NJIT. This undergraduate course focused on the three foundational pillars of drone careers: 1) drone programming using Python, 2) designing and fabricating drones using Computer-Aided Design (CAD) and rapid prototyping, and 3) the US Federal Aviation Administration (FAA) Part 107 Commercial small Unmanned Aerial Vehicles (sUAVs) pilot test. Multiple assessment methods are applied to examine the students’ gains in sUAV skills and knowledge and student attitudes towards an active learning-based approach for sUAV education. The use of active learning techniques to address these challenges lead to meaningful student engagement and positive gains in the learning outcomes as indicated by quantitative and qualitative assessments