A gap analysis of Internet-of-Things platforms

We are experiencing an abundance of Internet-of-Things (IoT) middleware solutions that provide connectivity for sensors and actuators to the Internet. To gain a widespread adoption, these middleware solutions, referred to as platforms, have to meet the expectations of different players in the IoT ecosystem, including device providers, application developers, and end-users, among others. In this article, we evaluate a representative sample of these platforms, both proprietary and open-source, on the basis of their ability to meet the expectations of different IoT users. The evaluation is thus more focused on how ready and usable these platforms are for IoT ecosystem players, rather than on the peculiarities of the underlying technological layers. The evaluation is carried out as a gap analysis of the current IoT landscape with respect to (i) the support for heterogeneous sensing and actuating technologies, (ii) the data ownership and its implications for security and privacy, (iii) data processing and data sharing capabilities, (iv) the support offered to application developers, (v) the completeness of an IoT ecosystem, and (vi) the availability of dedicated IoT marketplaces. The gap analysis aims to highlight the deficiencies of today's solutions to improve their integration to tomorrow's ecosystems. In order to strengthen the finding of our analysis, we conducted a survey among the partners of the Finnish IoT program, counting over 350 experts, to evaluate the most critical issues for the development of future IoT platforms. Based on the results of our analysis and our survey, we conclude this article with a list of recommendations for extending these IoT platforms in order to fill in the gaps.Comment: 15 pages, 4 figures, 3 tables, Accepted for publication in Computer Communications, special issue on the Internet of Things: Research challenges and solution

Exploring Data Security and Privacy Issues in Internet of Things Based on Five-Layer Architecture

Data Security and privacy is one of the serious issues in internet-based computing like cloud computing, mobile computing and Internet of Things (IoT). This security and privacy become manifolded in IoT because of diversified technologies and the interaction of Cyber Physical Systems (CPS) used in IoT. IoTs are being adapted in academics and in many organizations without fully protecting their assets and also without realizing that the traditional security solutions cannot be applied to IoT environment. This paper explores a comprehensive survey of IoT architectures, communication technologies and the security and privacy issues of them for a new researcher in IoT. This paper also suggests methods to thwart the security and privacy issues in the different layers of IoT architecture

A Paradigm for Safe Adaptation of Collaborating Robots

The dynamic forces that transit back and forth traditional boundaries of system development have led to the emergence of digital ecosystems. Within these, business gains are achieved through the development of intelligent control that requires a continuous design and runtime co-engineering process endangered by malicious attacks. The possibility of inserting specially crafted faults capable to exploit the nature of unknown evolving intelligent behavior raises the necessity of malicious behavior detection at runtime.Adjusting to the needs and opportunities of fast AI development within digital ecosystems, in this paper, we envision a novel method and framework for runtime predictive evaluation of intelligent robots' behavior for assuring a cooperative safe adjustment

Threat Landscape and Good Practice Guide for Software Defined Networks/5G

5G represents the next major phase of mobile telecommunication systems and network architectures beyond the current 4G standards, aiming at extreme broadband and ultra-robust, low latency connectivity, to enable the programmable connectivity for the Internet of Everything2. Despite the significant debate on the technical specifications and the technological maturity of 5G, which are under discussion in various fora3, 5G is expected to affect positively and significantly several industry sectors ranging from ICT to industry sectors such as car and other manufacturing, health and agriculture in the period up to and beyond 2020. 5G will be driven by the influence of software on network functions, known as Software Defined Networking (SDN) and Network Function Virtualization (NFV). The key concept that underpins SDN is the logical centralization of network control functions by decoupling the control and packet forwarding functionality of the network. NFV complements this vision through the virtualization of these functionalities based on recent advances in general server and enterprise IT virtualization. Considering the technological maturity of the technologies that 5G can leverage on, SDN is the one that is moving faster from development to production. To realize the business potential of SDN/5G, a number of technical issues related to the design and operation of Software Defined Networks need to be addressed. Amongst them, SDN/5G security is one of the key issues, that needs to be addressed comprehensively in order to avoid missing the business opportunities arising from SDN/5G. In this report, we review threats and potential compromises related to the security of SDN/5G networks. More specifically, this report contains a review of the emerging threat landscape of 5G networks with particular focus on Software Defined Networking. It also considers security of NFV and radio network access. To provide a comprehensive account of the emerging threat SDN/5G landscape, this report has identified related network assets and the security threats, challenges and risks arising for these assets. Driven by the identified threats and risks, this report has also reviewed and identified existing security mechanisms and good practices for SDN/5G/NFV, and based on these it has analysed gaps and provided technical, policy and organizational recommendations for proactively enhancing the security of SDN/5G

Attacks on the Android Platform

The focus of this research revolves around Android platform security, specifically Android malware attacks and defensive techniques. Android is a mobile operating system developed by Google, based on the Linux kernel and designed primarily for touchscreen mobile devices such as smartphones and tablets. With the rise of device mobility in our data-driven world, Android constitutes most of the operating systems on these mobile devices playing a dominant role in today’s world. Hence, this paper analyzes attacks and the various defensive mechanisms that have been proposed to prevent those attacks

Towards AI Standards Whitepaper: Thought-leadership in AI legal, ethical and safety specifications through experimentation

With the rapid adoption of algorithms in business and society there is a growing concern to safeguard the public interest. Researchers, policy-makers and industry sharing this view convened to collectively identify future areas of focus in order to advance AI standards - in particular the acute need to ensure standard suggestions are practical and empirically informed. This discussion occurred in the context of the creation of a lab at UCL with these concerns in mind (currently dubbed as UCL The Algorithms Standards and Technology Lab). Via a series of panels, with the main stakeholders, three themes emerged, namely (i) Building public trust, (ii) Accountability and Operationalisation, and (iii) Experimentation. In order to forward the themes, lab activities will fall under three streams - experimentation, community building and communication. The Lab’s mission is to provide thought-leadership in AI standards through experimentation

CONTEXTUALIZING DISCRIMINATION IN AI: MORAL IMAGINATION AND VALUE SENSITIVE DESIGN AS A FRAMEWORK TO STUDY AI DEVELOPMENT IN THE EU

AI will continue to play a role in service provision by both public and private sector providers. These services sometimes border on fundamental rights such as the right not to be discriminated against. Commonly, most people hold the prevailing belief that data knows best and that algorithms ensure equality and fairness. However, algorithms do discriminate and sometimes they perpetuate inequality. The paper is built on the premise that the primary source of discrimination in AI is human input and not the underlying AI technology. Moral imagination, or more accurately, the lack of it, may be responsible for non-technical bias in AI decision-making. Prohibition of discrimination is recognised as a fundamental value of the EU and it follows that AI systems must comply with EU regulations in their decision-making to prevent discrimination and in the process protect human dignity. As concerns human dignity, algorithmic bias continues to be the main problem regarding automated decision-making. This bias, more often than not, is as a result of reinforcing some institutional and societal discrimination into AI systems in the development phase. This has the effect of continuing to perpetuate bias in the wider society when AI systems are used. This paper takes a dogmatic approach in analyzing the EU value of prohibition of discrimination as it is interpreted in the design process of AI systems by using moral imagination and value sensitive design as a framework of investigation