An architecture for distributed ledger-based M2M auditing for Electric Autonomous Vehicles

Electric Autonomous Vehicles (EAVs) promise to be an effective way to solve transportation issues such as accidents, emissions and congestion, and aim at establishing the foundation of Machine-to-Machine (M2M) economy. For this to be possible, the market should be able to offer appropriate charging services without involving humans. The state-of-the-art mechanisms of charging and billing do not meet this requirement, and often impose service fees for value transactions that may also endanger users and their location privacy. This paper aims at filling this gap and envisions a new charging architecture and a billing framework for EAV which would enable M2M transactions via the use of Distributed Ledger Technology (DLT)

On M2M Micropayments : A Case Study of Electric Autonomous Vehicles

The proliferation of electric vehicles has spurred the research interest in technologies associated with it, for instance, batteries, and charging mechanisms. Moreover, the recent advancements in autonomous cars also encourage the enabling technologies to integrate and provide holistic applications. To this end, one key requirement for electric vehicles is to have an efficient, secure, and scalable infrastructure and framework for charging, billing, and auditing. However, the current manual charging systems for EVs may not be applicable to the autonomous cars that demand new, automatic, secure, efficient, and scalable billing and auditing mechanism. Owing to the distributed systems such as blockchain technology, in this paper, we propose a new charging and billing mechanism for electric vehicles that charge their batteries in a charging-on-the-move fashion. To meet the requirements of billing in electric vehicles, we leverage distributed ledger technology (DLT), a distributed peer-to-peer technology for micro-transactions. Our proof-of-concept implementation of the billing framework demonstrates the feasibility of such system in electric vehicles. It is also worth noting that the solution can easily be extended to the electric autonomous cars (EACs)

A Machine to Machine framework for the charging of Electric Autonomous Vehicles

Electric Autonomous Vehicles (EAVs) have gained increasing attention of industry, governments and scientific communities concerned about issues related to classic transportation including accidents and casualties, gas emissions and air pollution, intensive traffic and city viability. One of the aspects, however, that prevent a broader adoption of this technology is the need for human interference to charge EAVs, which is still mostly manual and time-consuming. This study approaches such a problem by introducing the Inno-EAV, an open-source charging framework for EAVs that employs machine-to-machine (M2M) distributed communication. The idea behind M2M is to have networked devices that can interact, exchange information and perform actions without any manual assistance of humans. The advantages of the Inno-EAV include the automation of charging processes and the collection of relevant data that can support better decision making in the spheres of energy distribution. In this paper, we present the software design of the framework, the development process, the emphasis on the distributed architecture and the networked communication, and we discuss the back-end database that is used to store information about car owners, cars, and charging stations

Distributed Space Traffic Management Solutions with Emerging New Space Industry

Day-to-day services, from weather forecast to logistics, rely on space-based infrastructures whose integrity is crucial to stakeholders and end-users worldwide. Current trends point towards congestion of the near-Earth space environment increasing at a rate greater than existing systems support, and thus demand novel cost-efficient approaches to traffic detection, characterization, tracking, and management to ensure space remains a safe, integral part of societies and economies worldwide. Whereas machine-learning (ML) and artificial intelligence (AI) have been extensively proposed to address congestion and alleviate big-data problems of the future, little has been done so far to tackle the need for transnational coordination and conflict-resolution in the context of space traffic management (STM). In STM, there is an ever-growing need for distributing information and coordinating actions (e.g., avoidance manoeuvres) to reduce the operational costs borne by individual entities and to decrease the latencies of actionable responses taken upon the detection of hazardous conditions by one-to-two orders of magnitude. However, these needs are not exclusive to STM, as evidenced by the widespread adoption of solutions to distributing, coordinating, and automating actions in other industries such as air traffic management (ATM), where a short-range airborne collision avoidance system (ACAS) automatically coordinates evasive manoeuvres whenever a conjunction is detected. Within this context, this paper aims at establishing a roadmap of promising technologies (e.g., blockchain), protocols and processes that could be adapted from different domains (railway, automotive, aerial, and maritime) to build an integrated traffic coordination and communication architecture to simplify and harmonise stakeholders’ satellite operations. This paper is organised into seven sections. First, Section 1 introduces the problem of STM, highlighting its complexity. Following this introduction, Section 2 discusses needs and requirements of various stakeholders such as commercial operators, space situational awareness (SSA) service providers, launch-service providers, satellite and constellation owners, governmental agencies, regulators, and insurance companies. Then, Section 3 addresses existing gaps and challenges in STM, focusing on globally coordinated approaches. Next, Section 4 reviews technologies for distributed, secure, and persistent communications, and proposed solutions to address some of these challenges from non-space sectors. Thereafter, Section 5 briefly covers the history of STM proposals and presents the state-of-the-art solution being proposed for modern STM. Following this review, Section 6 devises a step-by-step plan for exploiting and deploying some of the identified technologies within a five-to-ten-year timeline to close several existing gaps. Finally, Section 7 concludes the paper

Deployment of distributed ledger and decentralized technology for transition to smart industries

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