Tokenized Ecosystem of Personal Data - Exemplified on the Context of the Smart City

Data driven businesses, services, and even smart cities of tomorrow depend on access to data not only from machines, but also personal data of consumers, clients, citizens. Sustain-able utilization of such data must base on legal compliancy, ethical soundness, and consent. Data subjects nowadays largely lack empowerment over utilization and monetization of their personal data. To change this, we propose a tokenized ecosystem of personal data (TokPD), combining anonymization, referencing, encryption, decentralization, and functional layering to establish a privacy preserving solution for processing of personal data. This tokenized ecosys-tem is a more generalized variant of the smart city ecosystem described in the preceding publi-cation "Smart Cities of Self-Determined Data Subjects" (Frecè & Selzam 2017) with focus to-wards further options of decentralization. We use the example of a smart city to demonstrate, how TokPD ensures the data subjects’ privacy, grants the smart city access to a high number of new data sources, and simultaneously handles the user-consent to ensure compliance with mod-ern data protection regulation

Smart city technologies: new barriers to investment or a method for solving the economic problems of municipalities?

The purpose of the study is to determine the degree of readiness of urban municipal entities of the Russian Federation for the implementation of Smart City technology. The author proposes a methodology for determining the level of preparedness of cities for the introduction of Smart City technologies, selecting those municipal projects (Smart-projects) most relevant to the present level of readiness and identifying the main barriers to their implementation. The study used structural and graphical analysis methods, overall assessment and ratings as well as the group method of data handling (GMDH). The study yielded the following conclusions: The majority of cities comprising administrative centres of the Subjects of the Russian Federation are not ready for the implementation of Smart City technologies. The main problems hindering the implementation of Smart Technologies are the municipalities’ low energy efficiency and high dependence on borrowed capital. The methodology proposed by the author for assessing the readiness of municipalities for the implementation of Smart City technologies will quickly and optimally identify metropolitan areas suitable for the implementation of Smart-technologies. The field of application of the obtained results is sufficiently extensive. These results will be of interest to practitioners, representatives of state and local authorities, as well as for researchers in the fields of urban economics and urban studies. The main direction for future research consists in the provision of an underlying rationale for problem solving through launching Smart-projects in depressed and economically stagnating municipalities

Precision Targets: GPS and the Militarization of U.S. Consumer Identity

For most people in the United States, war is almost always elsewhere. Since the Civil War, declared wars have been engaged on terrains at a distance from the continental space of the nation. Until the attacks on the World Trade towers and the Pentagon in September 2001, many people in the United States perceived war to be conflicts between the standing armies of nation-states conducted at least a border—if not oceans and continents—away. Even the attacks of September 11 were localized in such a way as to feel as remote as they were immediate—watching cable news from elsewhere in the country, most U.S. residents were brought close to scenes of destruction and death by the media rather than by direct experience. Thus, in the United States, we could be said to be "consumers" of war, since our gaze is almost always fixed on representations of war that come from places perceived to be remote from the heartland

Review of Literature and Curricula in Smart Supply Chain & Transportation

This study provides a review of existing smart supply chain management (SCM) literature and current course offerings in order to identify unexplored implications of smart SCM. Specifically, the study focuses on curricula within the state of California to derive potential opportunities for the relevant practitioners in the Bay Area. In addition, the study further extends curriculum review to other well-recognized SCM programs around the U.S. By exploring current relevant course offerings from different academic institutions for higher education (i.e., universities), this research aims to deliver general ideas useful to knowledge practitioners in fields concerning SCM. Finally, the research illustrates a conceptual framework aimed at fostering familiarity with the necessary research topics for the evolving smart SCM

State-of-the-art on research and applications of machine learning in the building life cycle

Fueled by big data, powerful and affordable computing resources, and advanced algorithms, machine learning has been explored and applied to buildings research for the past decades and has demonstrated its potential to enhance building performance. This study systematically surveyed how machine learning has been applied at different stages of building life cycle. By conducting a literature search on the Web of Knowledge platform, we found 9579 papers in this field and selected 153 papers for an in-depth review. The number of published papers is increasing year by year, with a focus on building design, operation, and control. However, no study was found using machine learning in building commissioning. There are successful pilot studies on fault detection and diagnosis of HVAC equipment and systems, load prediction, energy baseline estimate, load shape clustering, occupancy prediction, and learning occupant behaviors and energy use patterns. None of the existing studies were adopted broadly by the building industry, due to common challenges including (1) lack of large scale labeled data to train and validate the model, (2) lack of model transferability, which limits a model trained with one data-rich building to be used in another building with limited data, (3) lack of strong justification of costs and benefits of deploying machine learning, and (4) the performance might not be reliable and robust for the stated goals, as the method might work for some buildings but could not be generalized to others. Findings from the study can inform future machine learning research to improve occupant comfort, energy efficiency, demand flexibility, and resilience of buildings, as well as to inspire young researchers in the field to explore multidisciplinary approaches that integrate building science, computing science, data science, and social science

The Phone Walkers: A study of human dependence on inactive mobile devices

The development of mobile phones has largely increased human interactions. Whilst the use of these devices for communication has received significant attention, there has been little analysis of more passive interactions. Through census data on casual social groups, this work suggests a clear pattern of mobile phones being carried in people's hands, without the person using it (that is, not looking at it). Moreover, this study suggests that when individuals join members of the opposite sex there is a clear tendency to stop holding mobile phones whilst walking. Although it is not clear why people hold their phones whilst walking in such large proportions (38% of solitary women, and 31% of solitary men), we highlight several possible explanation for holding the device, including the need to advertise status and affluence, to maintain immediate connection with friends and family, and to mitigate feelings related to anxiety and security.Comment: To appear in "Behaviour". 21 pages; 7 figures; Appendix in journal version onl

ICT, open government and civil society

Abstract This paper explores the rise of ICTs as instruments of government reform and the implication of their use from the vantage point of the relations between democratic governance, the aims of Buen Vivir, and the role of civil society. We discuss some of the contradictions inherent in the nature and organisation of ICTs, particularly in connection to such e-government projects as “smart cities” and participatory budgeting, and focus on the centrality of social relationships, political agency and the operations of social capital as elements that determine the success of these initiatives in the promotion of democratic practice. We also examine the relevance of social capital and user control to organisational structure and the ways in which structure relates to social innovation and the access, transfer and diffusion of knowledge as a common good. The paper concludes with a discussion of the significance of ICTs as instruments of civil empowerment and introduces the notion of “generative democracy” as a means of re-imagining and realigning the role and powers of the state and civil society for the social production of goods and services

Big data analytics:Computational intelligence techniques and application areas

Big Data has significant impact in developing functional smart cities and supporting modern societies. In this paper, we investigate the importance of Big Data in modern life and economy, and discuss challenges arising from Big Data utilization. Different computational intelligence techniques have been considered as tools for Big Data analytics. We also explore the powerful combination of Big Data and Computational Intelligence (CI) and identify a number of areas, where novel applications in real world smart city problems can be developed by utilizing these powerful tools and techniques. We present a case study for intelligent transportation in the context of a smart city, and a novel data modelling methodology based on a biologically inspired universal generative modelling approach called Hierarchical Spatial-Temporal State Machine (HSTSM). We further discuss various implications of policy, protection, valuation and commercialization related to Big Data, its applications and deployment

Big data and smart cities: a public sector organizational learning perspective

Public sector organizations (city authorities) have begun to explore ways to exploit big data to provide smarter solutions for cities. The way organizations learn to use new forms of technology has been widely researched. However, many public sector organisations have found themselves in new territory in trying to deploy and integrate this new form of technology (big data) to another fast moving and relatively new concept (smart city). This paper is a cross-sectional scoping study—from two UK smart city initiatives—on the learning processes experienced by elite (top management) stakeholders in the advent and adoption of these two novel concepts. The findings are an experiential narrative account on learning to exploit big data to address issues by developing solutions through smart city initiatives. The findings revealed a set of moves in relation to the exploration and exploitation of big data through smart city initiatives: (a) knowledge finding; (b) knowledge reframing; (c) inter-organization collaborations and (d) ex-post evaluations. Even though this is a time-sensitive scoping study it gives an account on a current state-of-play on the use of big data in public sector organizations for creating smarter cities. This study has implications for practitioners in the smart city domain and contributes to academia by operationalizing and adapting Crossan et al’s (Acad Manag Rev 24(3): 522–537, 1999) 4I model on organizational learning