Federated Robust Embedded Systems: Concepts and Challenges

The development within the area of embedded systems (ESs) is moving rapidly, not least due to falling costs of computation and communication equipment. It is believed that increased communication opportunities will lead to the future ESs no longer being parts of isolated products, but rather parts of larger communities or federations of ESs, within which information is exchanged for the benefit of all participants. This vision is asserted by a number of interrelated research topics, such as the internet of things, cyber-physical systems, systems of systems, and multi-agent systems. In this work, the focus is primarily on ESs, with their specific real-time and safety requirements. While the vision of interconnected ESs is quite promising, it also brings great challenges to the development of future systems in an efficient, safe, and reliable way. In this work, a pre-study has been carried out in order to gain a better understanding about common concepts and challenges that naturally arise in federations of ESs. The work was organized around a series of workshops, with contributions from both academic participants and industrial partners with a strong experience in ES development. During the workshops, a portfolio of possible ES federation scenarios was collected, and a number of application examples were discussed more thoroughly on different abstraction levels, starting from screening the nature of interactions on the federation level and proceeding down to the implementation details within each ES. These discussions led to a better understanding of what can be expected in the future federated ESs. In this report, the discussed applications are summarized, together with their characteristics, challenges, and necessary solution elements, providing a ground for the future research within the area of communicating ESs

Deep learning for internet of underwater things and ocean data analytics

The Internet of Underwater Things (IoUT) is an emerging technological ecosystem developed for connecting objects in maritime and underwater environments. IoUT technologies are empowered by an extreme number of deployed sensors and actuators. In this thesis, multiple IoUT sensory data are augmented with machine intelligence for forecasting purposes

New Trends in the Use of Artificial Intelligence for the Industry 4.0

Industry 4.0 is based on the cyber-physical transformation of processes, systems and methods applied in the manufacturing sector, and on its autonomous and decentralized operation. Industry 4.0 reflects that the industrial world is at the beginning of the so-called Fourth Industrial Revolution, characterized by a massive interconnection of assets and the integration of human operators with the manufacturing environment. In this regard, data analytics and, specifically, the artificial intelligence is the vehicular technology towards the next generation of smart factories.Chapters in this book cover a diversity of current and new developments in the use of artificial intelligence on the industrial sector seen from the fourth industrial revolution point of view, namely, cyber-physical applications, artificial intelligence technologies and tools, Industrial Internet of Things and data analytics. This book contains high-quality chapters containing original research results and literature review of exceptional merit. Thus, it is in the aim of the book to contribute to the literature of the topic in this regard and let the readers know current and new trends in the use of artificial intelligence for the Industry 4.0

Traffic-Driven Energy Efficient Operational Mechanisms in Cellular Access Networks

Recent explosive growth in mobile data traffic is increasing energy consumption in cellular networks at an incredible rate. Moreover, as a direct result of the conventional static network provisioning approach, a significant amount of electrical energy is being wasted in the existing networks. Therefore, in recent time, the issue of designing energy efficient cellular networks has drawn significant attention, which is also the foremost motivation behind this research. The proposed research is particularly focused on the design of self-organizing type traffic-sensitive dynamic network reconfiguring mechanisms for energy efficiency in cellular systems. Under the proposed techniques, radio access networks (RANs) are adaptively reconfigured using less equipment leading to reduced energy utilization. Several energy efficient cellular network frameworks by employing inter-base station (BS) cooperation in RANs are proposed. Under these frameworks, based on the instantaneous traffic demand, BSs are dynamically switched between active and sleep modes by redistributing traffic among them and thus, energy savings is achieved. The focus is then extended to exploiting the availability of multiple cellular networks for extracting energy savings through inter-RAN cooperation. Mathematical models for both of these single-RAN and multi-RAN cooperation mechanisms are also formulated. An alternative energy saving technique using dynamic sectorization (DS) under which some of the sectors in the underutilized BSs are turned into sleep mode is also proposed. Algorithms for both the distributed and the centralized implementations are developed. Finally, a two-dimensional energy efficient network provisioning mechanism is proposed by jointly applying both the DS and the dynamic BS switching. Extensive simulations are carried out, which demonstrate the capability of the proposed mechanisms in substantially enhancing the energy efficiency of cellular networks

Securing teleoperated robot: Classifying human operator identity and emotion through motion-controlled robotic behaviors

Teleoperated robotic systems allow human operators to control robots from a distance, which mitigates the constraints of physical distance between the operators and offers invaluable applications in the real world. However, the security of these systems is a critical concern. System attacks and the potential impact of operators’ inappropriate emotions can result in misbehavior of the remote robots, which poses risks to the remote environment. These concerns become particularly serious when performing mission-critical tasks, such as nuclear cleaning. This thesis explored innovative security methods for the teleoperated robotic system. Common methods of security that can be used for teleoperated robots include encryption, robot misbehavior detection and user authentication. However, they have limitations for teleoperated robot systems. Encryption adds communication overheads to the systems. Robot misbehavior detection can only detect unusual signals on robot devices. The user authentication method secured the system primarily at the access point. To address this, we built motioncontrolled robot platforms that allow for robot teleoperation and proposed methods of performing user classification directly on remote-controlled robotic behavioral data to enhance security integrity throughout the operation. We discussed in Chapter 3 and conducted 4 experiments. Experiments 1 and 2 demonstrated the effectiveness of our approach, achieving user classification accuracy of 95% and 93% on two platforms respectively, using motion-controlled robotic end-effector trajectories. The results in experiment 3 further indicated that control system performance directly impacts user classification efficacy. Additionally, we deployed an AI agent to protect user biometric identities, ensuring the robot’s actions do not compromise user privacy in the remote environment in experiment 4. This chapter provided a foundation of methodology and experiment design for the next work. Additionally, Operators’ emotions could pose a security threat to the robot system. A remote robot operator’s emotions can significantly impact the resulting robot’s motions leading to unexpected consequences, even when the user follows protocol and performs permitted tasks. The recognition of a user operator’s emotions in remote robot control scenarios is, however, under-explored. Emotion signals mainly are physiological signals, semantic information, facial expressions and bodily movements. However, most physiological signals are electrical signals and are vulnerable to motion artifacts, which can not acquire the accurate signal and is not suitable for teleoperated robot systems. Semantic information and facial expressions are sometimes not accessible and involve high privacy issues and add additional sensors to the teleoperated systems. We proposed the methods of emotion recognition through the motion-controlled robotic behaviors in Chapter 4. This work demonstrated for the first time that the motioncontrolled robotic arm can inherit human operators’ emotions and emotions can be classified through robotic end-effector trajectories, achieving an 83.3% accuracy. We developed two emotion recognition algorithms using Dynamic Time Warping (DTW) and Convolutional Neural Network (CNN), deriving unique emotional features from the avatar’s end-effector motions and joint spatial-temporal characteristics. Additionally, we demonstrated through direct comparison that our approach is more appropriate for motion-based telerobotic applications than traditional ECG-based methods. Furthermore, we discussed the implications of this system on prominent current and future remote robot operations and emotional robotic contexts. By integrating user classification and emotion recognition into teleoperated robotic systems, this thesis lays the groundwork for a new security paradigm that enhances both the safety of remote operations. Recognizing users and their emotions allows for more contextually appropriate robot responses, potentially preventing harm and improving the overall quality of teleoperated interactions. These advancements contribute significantly to the development of more adaptive, intuitive, and human-centered HRI applications, setting a precedent for future research in the field