The proliferation of electric vehicles (EVs) 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 EVs 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 (EAVs).

Increasing amount of data the organizations worldwide have at their disposal lead to the need to structure, organize and present the information obtained from it. That is because, in today's rapid-changing business environment, managers and executives need to be able to gain crucial insights about the ongoing project in as little time as possible. Recently, energy efficiency has become a greater field of research, and companies started concentrating on monitoring energy-related metrics. In addition, many of them have built their own internal tools (dashboards) to do just this. However, one of the major drawbacks of building specialized tools is the lack of adaptability. That is, they are often tailored to only one person (e.g. CEO), or a small group of them (e.g. board of directors, managers). Furthermore, the combination of metrics that is displayed to them does not change over time. This is a problem because most likely there exists a better metric combination that would allow users to get the crucial insights faster. To fill this gap, our ongoing research focuses on making the dashboards adaptable to multiple roles within the organization while optimizing for a certain goal. In some scenarios the dashboard's goal may be to detect defects, in others it may be to generate the most profit. As our primary research interest is to amplify energy efficiency, we have chosen that to be our dashboard's goal. Our previous work suggests that in order to handle compound metrics at scale it is needed to represent the dashboard as a complex system. This paper presents the design and the architecture of our proposed solution synergizing the notions from complexity theory, software architecture and user experience (UX) design.

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).

Developing features based solely on requirement documents and specifications has been a traditional way of building software. This paper provides a different approach by combining the notions from Artificial Intelligence (AI)-Evolutionary Algorithms (EA) and Complexity Theory. It represents the software to be build-a dashboard-as a Complex System, and metrics inside of them as agents that are interacting in a non-linear fashion. Furthermore, the paper suggests different visualization techniques required to represent such complex relationships.

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