A Roadmap for Interdisciplinary Research on the Internet of Things

In mid-2011, the Technology Strategy Board started an integrated programme of work focused on the Internet of Things (IoT), which included strategic investment and the establishment of a Special Interest Group aimed at building and engaging a UK community of innovators and researchers in the IoT. As the portfolio of activities with businesses, academics and other stakeholders progressed, it became apparent to us that the community had a keen interest in taking a more concerted and deeper look at the fundamental research issues in the IoT and that a more interdisciplinary approach was needed.Responding to this level of interest, the Technology Strategy Board joined forces with the Arts and Humanities Research Council, the Economic and Social Research Council, the Engineering and Physical Sciences Research Council and the Research Councils UK Digital Economy Programme and agreed to collaborate on an interdisciplinary R&D roadmapping activity, arguably the first of its kind in the UK. The activity, led by Professors Rahim Tafazolli, Hamid Aghvami, Rachel Cooper, William Dutton and Dr Colin Upstill brought together insight from a wide group of leaders and culminated in a two-day ‘meeting of minds’ in Loughborough on 11 and 12 July 2012. This report summarises the outcomes of the activity and makes important wide-ranging recommendations

The Internet of Musical Things Ontology

The Internet of Musical Things (IoMusT) is an emerging research area consisting of the extension of the Internet of Things paradigm to the music domain. Interoperability represents a central issue within this domain, where heterogeneous objects dedicated to the production and/or reception of musical content (Musical Things) are envisioned to communicate between each other. This paper proposes an ontology for the representation of the knowledge related to IoMusT ecosystems to facilitate interoperability between Musical Things. There was no previous comprehensive data model for the IoMusT domain, however the new ontology relates to existing ontologies, including the SOSA Ontology for the representation of sensors and actuators and the Music Ontology focusing on the production and consumption of music. This paper documents the design of the ontology and its evaluation with respect to specific requirements gathered from an extensive literature review, which was based on scenarios involving IoMusT stakeholders, such as performers and audience members. The IoMusT Ontology can be accessed at: https://w3id.org/iomust#

GAN Base feedback analysis system for industrial IOT networks

The internet, like automated tools, has grown to better our daily lives. Interacting IoT products and cyber-physical systems. Generative Adversarial Network’s (GANs’) generator and discriminator may have different inputs, allowing feedback in supervised models. AI systems use neural networks, and adversarial networks analyse neural network feedback. Cyber-physical production systems (CPPS) herald intelligent manufacturing . CPPS may launch cross-domain attacks since the virtual and real worlds are interwoven. This project addresses enhanced Cyber-Physical System(CPS) feedback structure for Denial-of-Service (DoS) defence . Comparing sensor-controller and controller-to-actuator DoS attack channels shows a swapping system modelling solution for the CPS’s complex response feedback. Because of the differential in bandwidth between the two channels and the suspects’ limited energy, one person can only launch so many DoS assaults. DoS attacks are old and widespread. Create a layered switching paradigm that employs packet-based transfer techniques to prevent assaults. The discriminator’s probability may be used to assess whether feedback samples came from real or fictional data. Cognitive feedback can assess GA feedback data

Intelligent Embedded Software: New Perspectives and Challenges

Intelligent embedded systems (IES) represent a novel and promising generation of embedded systems (ES). IES have the capacity of reasoning about their external environments and adapt their behavior accordingly. Such systems are situated in the intersection of two different branches that are the embedded computing and the intelligent computing. On the other hand, intelligent embedded software (IESo) is becoming a large part of the engineering cost of intelligent embedded systems. IESo can include some artificial intelligence (AI)-based systems such as expert systems, neural networks and other sophisticated artificial intelligence (AI) models to guarantee some important characteristics such as self-learning, self-optimizing and self-repairing. Despite the widespread of such systems, some design challenging issues are arising. Designing a resource-constrained software and at the same time intelligent is not a trivial task especially in a real-time context. To deal with this dilemma, embedded system researchers have profited from the progress in semiconductor technology to develop specific hardware to support well AI models and render the integration of AI with the embedded world a reality