In this chapter, several kinds of MI-based applications are introduced. Specifically, the MI-based localization system is one of the most widely used and mature applications of the MI-based techniques. Thus, this chapter first describes several typical MI localization applications, such as the motion capture system, pipeline position systems, and fusion localization with other techniques (such as inertial measurement correction). Second, we summarize some MI-based communication applications for IoT, such as radio frequency identification, through-the-earth communication and underwater communication.

Innovation is about change: the introduction of novelty into an economic system. Managing any type of economic or organizational change is challenging because its effects are usually uncertain and affect participants unevenly. Managing technological change requires a heady cocktail of creativity, flexibility, and perseverance in the face of novelty and turmoil. This chapter explores the specia features of digital innovation of new businesses. Digital business innovation deals with improved technology-based business models for information and communication – core elements of all economic activity. Furthermore, we look at the long-term patterns of technological change and notice how digital technologies arise from the combination of electronics and instruments and lead to new kinds of technologies that accelerate invention activity itself.

Times are changing as our global ecosystem for commercializing innovation helps bring new technologies to market, networks grow, and interconnections and transactions become more complex around standards, all to enable vast opportunities to improve the human condition, to further competition, and to improve broad access. The policies that governments use to structure their legal systems for intellectual property, especially patents, as well as for competition—or antitrust—continue to have myriad powerful impacts and raise intense debates over challenging questions. This chapter explores a representative set of debates about policy approaches to patents, to elucidate particular ideas to bear in mind about how adopting a private law, property rights-based approach to patents enables them to better operate as tools for facilitating the commercialization of new technologies in ways that best promote the goals of increasing access while fostering competition and security for a diverse and inclusive society.

The content of this book is rather controversial. It paints a rather bleak picture, that the current EU legal economic system being developed for the data-driven economy is both outdated and – to some extent – a policy at war with itself. It promotes dominant platforms to detriment of others. Moreover, the fundamentals for creating rules are also missing. A liberal economic system needs to be based on aspects of a rights system, otherwise, we risk losing innovation, the establishment of new markets, and the creation of wealth, while we will see increasing market failures. Without a legal system for rights to data, we will lose out of a just system for the distribution of wealth. Indeed, it is time that the data-driven economy and the internet economy are granted their ‘property’ rights, reflecting the new paradigm of the data-driven industrial revolution. Moreover, such a regime fits well with the European economic constitution now being established.

Generally, the political consensus at the beginning of the internet era was that platforms should have only limited liability under intellectual property law for content that users uploaded on these platforms.1 Now, however, the platforms are the center of gravity for the Internet, drawing in (for technical reasons and owing to network effects) all data streams in the respective ecosystems, for the benefit of the system leader controlling the platform. Furthermore, the contracts that system leaders conclude with business users, and which control their business relationship, not only normally grant the exclusive right to data generated on the platform to the platform provider but also generally neutralize any intellectual property rights held by the business user. Indeed, the system leaders are regulators of their respective ecosystems and use their system of contracts to control the ecosystems and exclude the use and importance of intellectual property rights. The platform or cloud provider contractually secures the right from the platform or cloud user, not only to store the data but also to analyze it and make use of it for the provider’s own benefit, and for the benefit of others in the ecosystem, on all connected markets. The platform providers thus become the masters of their respective data ecosystems; they do indeed hoard the data and generally do not trade or share the data.

Data is vital to the internet-based economy and will become even more important in the old economy as the Internet of Things (IoT) gains ground. The competitiveness of firms will increasingly depend on timely access to relevant data and the ability to use that data to develop new, innovative applications and products. In consumer-oriented businesses, the relevant data is often personal information; although this data is becoming increasingly collectable, only a few firms have access to larger amounts of it.1

How to build an ecosystem of trust in digital health? The availability of large amounts of personal data, combined with AI and ML capacities, Internet of Things and strong computational platforms, has the potential to transform healthcare systems in a disruptive way. The emergence of personalised medicine offers opportunities and raises new legal, ethical and societal challenges. A silent shift towards data-driven preventive and personalised medicine may improve diagnosis and therapies while reducing public health costs. In order to build trust, risks such as data breaches, privacy issues, discrimination and eugenics must be addressed. This chapter presents the disruptive nature of AI and ML technologies in healthcare, and makes specific recommendations to build a trustworthy digital health system. Special attention is given to governance by international institutions as well as key principles like transparency, accountability and decision-making processes in a medical context. We first identify the key parameters to advance the field of digital health in a responsible way. Second, we propose possible solutions to shape a sound policy in digital health taking into account a rights-based governance framework. The last part of the chapter is dedicated to the accountability scheme.

The fourth chapter provides the attributes that DLT has that can make it unique when solving climate issues. It considers the overlap between the traditional legal structures in a system and the modern crypto-legal proposals and provides solutions to the security issues arising under the EU ETS.

Internet of Thing (IoT)s. It focuses in particular on the question of liability in circumstances where an IoT system has not performed as expected and where this has resulted in loss or damage. The authors argue that the combination of AI and the IoT raises several novel aspects concerning the basis for assessing responsibility and of allocating liability for loss or damage, and that this will necessitate the development of a more creative approach to liability than generally followed in many legal systems. Linear liability based on contractual relationships and fault-based or strict liability of a wrongdoer in tort law are no longer sufficient to deal with the complex issues associated with the interaction of AI and the IoT. According to the authors, the values underpinning established liability systems, particularly in the field of consumer protection law, should be maintained in the context of new digital technology applications. The adoption of new digital technology applications cannot be a basis for imposing a lower threshold of liability than the level of liability established in other contexts.

Student-centred learning is an emerging terminology questioning the relevance of traditional terminologies such as teacher-centred and institution-centred learning. Teacher-centred and institution-centred learning align more towards teachers and institutions making the students passive recipients of knowledge. These traditional paradigms of teaching have been questioned in recent years and they have been replaced by student-centred learning which focuses on placing the students at the forefront and taking responsibility for their learning. Internet technology has offered tremendous support in the process of students playing a key role in student-centred learning. This chapter presents a summary of emerging technologies that have played a key role in enhancing the quality of student-centred learning in higher education. Five key technology trends such as Learning Management Systems, Virtual Reality, Internet of Things, MOOCs and Social Media are critically analysed to explore their role in the development of a student-centric learning and teaching program. The chapter identifies the strengths and weaknesses of these technologies and how they can be successfully applied to enhance the quality of student-centric learning and teaching program.

Teaching formats are constantly evolving over the years to adapt to newer methods of student learning. In the ancient times, the practice of ‘Gurukul System’ where students would go and live with a teacher and learn all that teacher would know and practice by listening and observing. Here the method adopted was more tacit-to-tacit knowledge transfer between the teacher and the students. The learning here used to be a function of student’s ability to absorb skill and knowledge, and the evaluation used to be a function of real demonstration of student’s ability. This method has changed in the last hundred years where a more formal schooling system has evolved to get both teachers and students in one place, and the knowledge is transferred explicitly in the form of a prescribed curriculum. The learning in a ‘classroom’ environment is more formatted and the evaluation is based on formal assessments. In the recent years, lot of interest has been developed to innovate the teaching formats so that a ‘student centric learning’ can be practiced in a classroom environment. As described by Kaplan (2021) in this book’s first chapter, changes triggered by the COVID 19 pandemic have impacted the Education Sector extensively giving birth to a digital mode of instruction as the new normal. The digital platform and the transformation it can bring into the practice has created new interest among the teaching community to explore better teaching formats.

Although lay participation has long been a feature of scientific research, the past decades have seen an explosion in the number of citizen science projects. Simultaneously, the number of low-cost network connected devices collectively known as Internet of Things devices has proliferated. The increased use of Internet of Things devices in citizen science exists has coincided with a reconsideration of the right to science under international law. Specifically, the Universal Declaration of Human Rights and the International Covenant on Economic Social and Cultural Rights both recognise a right to benefit and participate in the scientific process. Whilst it is unclear whether this right protects participation by citizen scientists, it provides a useful framework to help chart the ethical issues raised by citizen science. In this chapter, we first describe the origins and boundaries of the right to science, as well as its relevance to citizen science. We then use the findings of a scoping review to examine three main ethical and legal issues for using Internet of Things devices in citizen science.