Big Data and the Internet of Things

Advances in sensing and computing capabilities are making it possible to embed increasing computing power in small devices. This has enabled the sensing devices not just to passively capture data at very high resolution but also to take sophisticated actions in response. Combined with advances in communication, this is resulting in an ecosystem of highly interconnected devices referred to as the Internet of Things - IoT. In conjunction, the advances in machine learning have allowed building models on this ever increasing amounts of data. Consequently, devices all the way from heavy assets such as aircraft engines to wearables such as health monitors can all now not only generate massive amounts of data but can draw back on aggregate analytics to "improve" their performance over time. Big data analytics has been identified as a key enabler for the IoT. In this chapter, we discuss various avenues of the IoT where big data analytics either is already making a significant impact or is on the cusp of doing so. We also discuss social implications and areas of concern.Comment: 33 pages. draft of upcoming book chapter in Japkowicz and Stefanowski (eds.) Big Data Analysis: New algorithms for a new society, Springer Series on Studies in Big Data, to appea

Achieving Small World Properties using Bio-Inspired Techniques in Wireless Networks

It is highly desirable and challenging for a wireless ad hoc network to have self-organization properties in order to achieve network wide characteristics. Studies have shown that Small World properties, primarily low average path length and high clustering coefficient, are desired properties for networks in general. However, due to the spatial nature of the wireless networks, achieving small world properties remains highly challenging. Studies also show that, wireless ad hoc networks with small world properties show a degree distribution that lies between geometric and power law. In this paper, we show that in a wireless ad hoc network with non-uniform node density with only local information, we can significantly reduce the average path length and retain the clustering coefficient. To achieve our goal, our algorithm first identifies logical regions using Lateral Inhibition technique, then identifies the nodes that beamform and finally the beam properties using Flocking. We use Lateral Inhibition and Flocking because they enable us to use local state information as opposed to other techniques. We support our work with simulation results and analysis, which show that a reduction of up to 40% can be achieved for a high-density network. We also show the effect of hopcount used to create regions on average path length, clustering coefficient and connectivity.Comment: Accepted for publication: Special Issue on Security and Performance of Networks and Clouds (The Computer Journal

Are smart innovation ecosystems really seeking to meet citizens’ needs? Insights from the stakeholders’ vision on smart city strategy implementation

The concept of a smart city is becoming the leading paradigm worldwide. Consequently, a creative mix of emerging technologies and open innovation is gradually becoming the defining element of smart city evolution, changing the ways in which city administrators are organizing their services and development globally. Thus, the smart city concept is becoming extremely relevant on the agendas of policy-makers as a development strategy for enhancing the quality of life of the citizen and improving the sustainability goals of their cities. Despite of the relevance of the topic, still few studies investigate how open innovation shapes the way cities become smarter or focus on the experiences of professionals to understand the concept of a smart city and its implementation. This paper fills this gap and analyzes the processes for building effective smart cities by integrating the different perspectives of smart innovations and using the core components of smart cities according to a conceptual framework developed in previous research. In so doing, it provides useful insights for smart city stakeholders in adopting social and technological innovation to improve the global competitiveness of their cities. The empirical dataset allows examining how “smart cities” are being implemented in Manchester (UK), and in Boston, Massachusetts, and San Diego City (United States of America (USA)), including archival data and in-depth interviews with core smart city stakeholders who are involved in smart city projects and programs across the cases. Results from empirical data suggest that the conceptualization of smart cities across the cases is similar with a strong emphasis on social and technological innovation aimed at addressing municipal challenges in the core sub-systems of the cities, which include mobility, environmental sustainability, entrepreneurial development, quality of life, and social cohesion. The results also reveal benefits and challenges relating to smart innovation ecosystems across the cases and the future directions of their diffusion

The genesis and development of mobile learning in Europe

In the past two decades, European researchers have conducted many significant mobile learning projects. The chapter explores how these projects have arisen and what each one has contributed, so as to show the driving forces and outcomes of European innovation in mobile learning. The authors identify context as a central construct in European researchers’ conceptualizations of mobile learning and examine theories of learning for the mobile world, based on physical, technological, conceptual, social and temporal mobility. The authors also examine the impacts of mobile learning research on educational practices and the implications for policy. Finally, they suggest future challenges for researchers, developers and policy makers in shaping the future of mobile learning

Internet Predictions

More than a dozen leading experts give their opinions on where the Internet is headed and where it will be in the next decade in terms of technology, policy, and applications. They cover topics ranging from the Internet of Things to climate change to the digital storage of the future. A summary of the articles is available in the Web extras section

Quality assessment technique for ubiquitous software and middleware

The new paradigm of computing or information systems is ubiquitous computing systems. The technology-oriented issues of ubiquitous computing systems have made researchers pay much attention to the feasibility study of the technologies rather than building quality assurance indices or guidelines. In this context, measuring quality is the key to developing high-quality ubiquitous computing products. For this reason, various quality models have been defined, adopted and enhanced over the years, for example, the need for one recognised standard quality model (ISO/IEC 9126) is the result of a consensus for a software quality model on three levels: characteristics, sub-characteristics, and metrics. However, it is very much unlikely that this scheme will be directly applicable to ubiquitous computing environments which are considerably different to conventional software, trailing a big concern which is being given to reformulate existing methods, and especially to elaborate new assessment techniques for ubiquitous computing environments. This paper selects appropriate quality characteristics for the ubiquitous computing environment, which can be used as the quality target for both ubiquitous computing product evaluation processes ad development processes. Further, each of the quality characteristics has been expanded with evaluation questions and metrics, in some cases with measures. In addition, this quality model has been applied to the industrial setting of the ubiquitous computing environment. These have revealed that while the approach was sound, there are some parts to be more developed in the future

A World That Counts: Mobilising The Data Revolution For Sustainable Development

This report sets out the main opportunities and risks presented by the data revolution for sustainable development. Seizing these opportunities and mitigating these risks requires active choices, especially by governments and international institutions. Without immediate action, gaps between developed and developing countries, between information-rich and information-poor people, and between the private and public sectors will widen, and risks of harm and abuses of human rights will grow

Cognitive Hyperconnected Digital Transformation

Cognitive Hyperconnected Digital Transformation provides an overview of the current Internet of Things (IoT) landscape, ranging from research, innovation and development priorities to enabling technologies in a global context. It is intended as a standalone book in a series that covers the Internet of Things activities of the IERC-Internet of Things European Research Cluster, including both research and technological innovation, validation and deployment. The book builds on the ideas put forward by the European Research Cluster, the IoT European Platform Initiative (IoT-EPI) and the IoT European Large-Scale Pilots Programme, presenting global views and state-of-the-art results regarding the challenges facing IoT research, innovation, development and deployment in the next years. Hyperconnected environments integrating industrial/business/consumer IoT technologies and applications require new IoT open systems architectures integrated with network architecture (a knowledge-centric network for IoT), IoT system design and open, horizontal and interoperable platforms managing things that are digital, automated and connected and that function in real-time with remote access and control based on Internet-enabled tools. The IoT is bridging the physical world with the virtual world by combining augmented reality (AR), virtual reality (VR), machine learning and artificial intelligence (AI) to support the physical-digital integrations in the Internet of mobile things based on sensors/actuators, communication, analytics technologies, cyber-physical systems, software, cognitive systems and IoT platforms with multiple functionalities. These IoT systems have the potential to understand, learn, predict, adapt and operate autonomously. They can change future behaviour, while the combination of extensive parallel processing power, advanced algorithms and data sets feed the cognitive algorithms that allow the IoT systems to develop new services and propose new solutions. IoT technologies are moving into the industrial space and enhancing traditional industrial platforms with solutions that break free of device-, operating system- and protocol-dependency. Secure edge computing solutions replace local networks, web services replace software, and devices with networked programmable logic controllers (NPLCs) based on Internet protocols replace devices that use proprietary protocols. Information captured by edge devices on the factory floor is secure and accessible from any location in real time, opening the communication gateway both vertically (connecting machines across the factory and enabling the instant availability of data to stakeholders within operational silos) and horizontally (with one framework for the entire supply chain, across departments, business units, global factory locations and other markets). End-to-end security and privacy solutions in IoT space require agile, context-aware and scalable components with mechanisms that are both fluid and adaptive. The convergence of IT (information technology) and OT (operational technology) makes security and privacy by default a new important element where security is addressed at the architecture level, across applications and domains, using multi-layered distributed security measures. Blockchain is transforming industry operating models by adding trust to untrusted environments, providing distributed security mechanisms and transparent access to the information in the chain. Digital technology platforms are evolving, with IoT platforms integrating complex information systems, customer experience, analytics and intelligence to enable new capabilities and business models for digital business