A privacy-preserving and power-efficient bicycle tracking scheme for theft mitigation

© 2016 IEEE. Bicycle theft is a big problem in places such as university towns, where bicycles offer one of the most costefficient and quick ways for students to move around. For example, 1,200 bicycles are stolen yearly in Göttingen, with more than 300,000 being reported as stolen in the whole of Germany during 2014. We present a power efficient architecture to track the locations of stolen bicycles using opportunistic communication with collection nodes placed in high traffic spots, that can be used to find stolen and lost bicycles. At the same time, the scheme is designed to prevent a loss of privacy for the owners of bicycles that have not been marked as stolen, while also reducing power usage during times where bicycles are under the control of their proper owners.We also show the feasibility of our approach using a simplified implementation using IRIS nodes, with a university campus serving as a testbed

A Survey on the Applications of Frontier AI, Foundation Models, and Large Language Models to Intelligent Transportation Systems

This survey paper explores the transformative influence of frontier AI, foundation models, and Large Language Models (LLMs) in the realm of Intelligent Transportation Systems (ITS), emphasizing their integral role in advancing transportation intelligence, optimizing traffic management, and contributing to the realization of smart cities. Frontier AI refers to the forefront of AI technology, encompassing the latest advancements, innovations, and experimental techniques in the field, especially AI foundation models and LLMs. Foundation models, like GPT-4, are large, general-purpose AI models that provide a base for a wide range of applications. They are characterized by their versatility and scalability. LLMs are obtained from finetuning foundation models with a specific focus on processing and generating natural language. They excel in tasks like language understanding, text generation, translation, and summarization. By leveraging vast textual data, including traffic reports and social media interactions, LLMs extract critical insights, fostering the evolution of ITS. The survey navigates the dynamic synergy between LLMs and ITS, delving into applications in traffic management, integration into autonomous vehicles, and their role in shaping smart cities. It provides insights into ongoing research, innovations, and emerging trends, aiming to inspire collaboration at the intersection of language, intelligence, and mobility for safer, more efficient, and sustainable transportation systems. The paper further surveys interactions between LLMs and various aspects of ITS, exploring roles in traffic management, facilitating autonomous vehicles, and contributing to smart city development, while addressing challenges brought by frontier AI and foundation models. This paper offers valuable inspiration for future research and innovation in the transformative domain of intelligent transportation.Comment: This paper appears in International Conference on Computer and Applications (ICCA) 202

Advances on Smart Cities and Smart Buildings

Modern cities are facing the challenge of combining competitiveness at the global city scale and sustainable urban development to become smart cities. A smart city is a high-tech, intensive and advanced city that connects people, information, and city elements using new technologies in order to create a sustainable, greener city; competitive and innovative commerce; and an increased quality of life. This Special Issue collects the recent advancements in smart cities and covers different topics and aspects

Cybersecurity issues in software architectures for innovative services

The recent advances in data center development have been at the basis of the widespread success of the cloud computing paradigm, which is at the basis of models for software based applications and services, which is the "Everything as a Service" (XaaS) model. According to the XaaS model, service of any kind are deployed on demand as cloud based applications, with a great degree of flexibility and a limited need for investments in dedicated hardware and or software components. This approach opens up a lot of opportunities, for instance providing access to complex and widely distributed applications, whose cost and complexity represented in the past a significant entry barrier, also to small or emerging businesses. Unfortunately, networking is now embedded in every service and application, raising several cybersecurity issues related to corruption and leakage of data, unauthorized access, etc. However, new service-oriented architectures are emerging in this context, the so-called services enabler architecture. The aim of these architectures is not only to expose and give the resources to these types of services, but it is also to validate them. The validation includes numerous aspects, from the legal to the infrastructural ones e.g., but above all the cybersecurity threats. A solid threat analysis of the aforementioned architecture is therefore necessary, and this is the main goal of this thesis. This work investigate the security threats of the emerging service enabler architectures, providing proof of concepts for these issues and the solutions too, based on several use-cases implemented in real world scenarios

Future Transportation

Greenhouse gas (GHG) emissions associated with transportation activities account for approximately 20 percent of all carbon dioxide (co2) emissions globally, making the transportation sector a major contributor to the current global warming. This book focuses on the latest advances in technologies aiming at the sustainable future transportation of people and goods. A reduction in burning fossil fuel and technological transitions are the main approaches toward sustainable future transportation. Particular attention is given to automobile technological transitions, bike sharing systems, supply chain digitalization, and transport performance monitoring and optimization, among others

Applications

Volume 3 describes how resource-aware machine learning methods and techniques are used to successfully solve real-world problems. The book provides numerous specific application examples: in health and medicine for risk modelling, diagnosis, and treatment selection for diseases in electronics, steel production and milling for quality control during manufacturing processes in traffic, logistics for smart cities and for mobile communications

Dictionary of privacy, data protection and information security

The Dictionary of Privacy, Data Protection and Information Security explains the complex technical terms, legal concepts, privacy management techniques, conceptual matters and vocabulary that inform public debate about privacy. The revolutionary and pervasive influence of digital technology affects numerous disciplines and sectors of society, and concerns about its potential threats to privacy are growing. With over a thousand terms meticulously set out, described and cross-referenced, this Dictionary enables productive discussion by covering the full range of fields accessibly and comprehensively. In the ever-evolving debate surrounding privacy, this Dictionary takes a longer view, transcending the details of today''s problems, technology, and the law to examine the wider principles that underlie privacy discourse. Interdisciplinary in scope, this Dictionary is invaluable to students, scholars and researchers in law, technology and computing, cybersecurity, sociology, public policy and administration, and regulation. It is also a vital reference for diverse practitioners including data scientists, lawyers, policymakers and regulators

Instrumentalization in the Public Smart Bikeshare Sector

This thesis is concerned with understanding how smart technologies are conceived, created and implemented, and explores the ways these processes are shaped by historical, geo-political, economic and technical contexts. At its core the thesis is concerned with understanding how technical citizenship and democracy can be preserved within the design process against a backdrop of increasing neoliberalism and technocracy. This is investigated by means of a comparative study of smart public bikeshare schemes in Dublin, Ireland and Hamilton, Canada. These schemes are configured and systemized using a variety of technical and ideological rationales and express the imaginaries of place in significantly different ways. Utilising a conceptual framework derived from Andrew Feenberg’s critical theory of technology, the thesis unpacks and problematizes the innovation process in order to understand how the outcomes of these schemes support the way of life of one or another influential social group. The philosophical orientation of the study is critical constructivism which combines a form of constructivism with more systematic and socially critical views of technology. The axis of comparison between the schemes is democratization and the manner in which the rationalizations and embedded cultural assumptions characterizing particular places operate to support or resist more egalitarian forms of participation. Methodologically, Feenberg’s critical framework is supported both by theory-driven thematic coding and critical hermeneutics which is an interpretative process that compliments the theoretical framework and positions issues of power and ideology within a wider, macro-level context. Data sources supporting the research comprise interviews, a variety of documentary sources and the architectures and technical specifications of both smart bikeshare systems. The findings from the research illustrate that despite the pervasiveness of a neoliberal orthodoxy conditioning technology production, citizen-centric design is still possible within a climate of consensus building and cooperation. As such, the thesis adds to the body of knowledge on philosophy of technology, critical urbanism, smart city development, democratic engagement and collaborative infrastructuring. In addition, the conceptual framework, developed in response to the empirical cases, represents an elaboration of Feenberg’s work and so the thesis also makes an important contribution to the analytic and methodological potential of critical theory of technology

Applications

Volume 3 describes how resource-aware machine learning methods and techniques are used to successfully solve real-world problems. The book provides numerous specific application examples: in health and medicine for risk modelling, diagnosis, and treatment selection for diseases in electronics, steel production and milling for quality control during manufacturing processes in traffic, logistics for smart cities and for mobile communications