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Register a new userInternet of Autonomous Vehicles: Architecture, Features, and Socio-Technological Challenges
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Added by
Furqan Jameel
Publication date
2019
fields
Informatics Engineering
and research's language is
English
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Mobility is the backbone of urban life and a vital economic factor in the development of the world. Rapid urbanization and the growth of mega-cities is bringing dramatic changes in the capabilities of vehicles. Innovative solutions like autonomy, electrification, and connectivity are on the horizon. How, then, we can provide ubiquitous connectivity to the legacy and autonomous vehicles? This paper seeks to answer this question by combining recent leaps of innovation in network virtualization with remarkable feats of wireless communications. To do so, this paper proposes a novel paradigm called the Internet of autonomous vehicles (IoAV). We begin painting the picture of IoAV by discussing the salient features, and applications of IoAV which is followed by a detailed discussion on the key enabling technologies. Next, we describe the proposed layered architecture of IoAV and uncover some critical functions of each layer. This is followed by the performance evaluation of IoAV which shows the significant advantage of the proposed architecture in terms of transmission time and energy consumption. Finally, to best capture the benefits of IoAV, we enumerate some social and technological challenges and explain how some unresolved issues can disrupt the widespread use of autonomous vehicles in the future.
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Advancements in digital technologies have a bootstrapping effect. The past fifty years of technological innovations from the computer architecture community have brought innovations and orders-of-magnitude efficiency improvements that engender use cases that were not previously possible -- stimulating novel application domains and increasing uses and deployments at an ever-faster pace. Consequently, computing technologies have fueled significant economic growth, creating education opportunities, enabling access to a wider and more diverse spectrum of information, and, at the same time, connecting people of differing needs in the world together. Technology must be offered that is inclusive of the worlds physical, cultural, and economic diversity, and which is manufactured, used, and recycled with environmental sustainability at the forefront. For the next decades to come, we envision significant cross-disciplinary efforts to build a circular development cycle by placing pervasive connectivity, sustainability, and demographic inclusion at the design forefront in order to sustain and expand the benefits of a technologically rich society. We hope this work will inspire our computing community to take broader and more holistic approaches when developing technological solutions to serve people from different parts of the world.
This paper describes a new architecture for transient mobile networks destined to merge existing and future network architectures, communication implementations and protocol operations by introducing a new paradigm to data delivery and identification. The main goal of our research is to enable seamless end-to-end communication between mobile and stationary devices across multiple networks and through multiple communication environments. The architecture establishes a set of infrastructure components and protocols that set the ground for a Persistent Identification Network (PIN). The basis for the operation of PIN is an identification space consisting of unique location independent identifiers similar to the ones implemented in the Handle system. Persistent Identifiers are used to identify and locate Digital Entities which can include devices, services, users and even traffic. The architecture establishes a primary connection independent logical structure that can operate over conventional networks or more advanced peer-to-peer aggregation networks. Communication is based on routing pools and novel protocols for routing data across several abstraction levels of the network, regardless of the end-points current association and state...
The recent history has witnessed disruptive advances in disciplines related to information and communication technologies that have laid a rich technological ecosystem for the growth and maturity of latent paradigms in this domain. Among them, sensor networks have evolved from the originally conceived set-up where hundreds of nodes with sensing and actuating functionalities were deployed to capture information from their environment and act accordingly (coining the so-called wireless sensor network concept) to the provision of such functionalities embedded in quotidian objects that communicate and work together to collaboratively accomplish complex tasks based on the information they acquire by sensing the environment. This is nowadays a reality, embracing the original idea of an Internet of things (IoT) forged in the late twentieth century, yet featuring unprecedented scales, capabilities and applications ignited by new radio interfaces, communication protocols and intelligent data-based models. This chapter examines the latest findings reported in the literature around these topics, with a clear focus on IoT communications, protocols and platforms, towards ultimately identifying opportunities and trends that will be at the forefront of IoT-related research in the near future.
Federated learning (FL) can empower Internet-of-Vehicles (IoV) networks by leveraging smart vehicles (SVs) to participate in the learning process with minimum data exchanges and privacy disclosure. The collected data and learned knowledge can help the vehicular service provider (VSP) improve the global model accuracy, e.g., for road safety as well as better profits for both VSP and participating SVs. Nonetheless, there exist major challenges when implementing the FL in IoV networks, such as dynamic activities and diverse quality-of-information (QoI) from a large number of SVs, VSPs limited payment budget, and profit competition among SVs. In this paper, we propose a novel dynamic FL-based economic framework for an IoV network to address these challenges. Specifically, the VSP first implements an SV selection method to determine a set of the best SVs for the FL process according to the significance of their current locations and information history at each learning round. Then, each selected SV can collect on-road information and offer a payment contract to the VSP based on its collected QoI. For that, we develop a multi-principal one-agent contract-based policy to maximize the profits of the VSP and learning SVs under the VSPs limited payment budget and asymmetric information between the VSP and SVs. Through experimental results using real-world on-road datasets, we show that our framework can converge 57% faster (even with only 10% of active SVs in the network) and obtain much higher social welfare of the network (up to 27.2 times) compared with those of other baseline FL methods.
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