In the last decade, electric vehicles (EVs) have emerged as a sustainable transportation alternative to traditional internal combustion engine (ICE) cars, with automotive software as the key driver behind the advancements. A well-defined information and communication technology (ICT) architecture, comprised of electronics and software, can increase an EV’s energy and cost efficiency, safety and comfort. With connected and autonomous electric vehicles (CAEVs) fast becoming a reality, the importance of software in vehicles increases tenfold. This paper serves as an introduction into the field of electromobility and automotive software. It provides an overview of the software and ICT architecture found in CAEVs and identifies future trends and challenges in automotive software development.

Communication is a prerequisite for any form of social activity, including social networking. Nowadays, communication is not reserved only for humans, but machines can also communicate. This paper reviews the state-of-the-art technology in the area of Machine-to-Machine (M2M) communication by comparing the M2M concept with other related research paradigms such as Wireless Sensor Networks, Cyber-Physical Systems, Internet of Things, and Human-Agent Collectives. Furthermore, the paper analyses trends in the interconnecting of machines and identifies an evolutionary path in which future (smart) machines will form mostly or completely autonomous communities bonded through social connections. Such communities—machine social networks—will be formed dynamically, just like human connections, and based on the needs of machines, their context, and state of their environment. Finally, the paper outlines the current evolutionary stage and identifies key research challenges of machine social networking.

Current trends are showing that the popularity of electric vehicles (EVs) has significantly increased over the last few years, causing changes not only in the transportation industry but generally in business and society. This paper covers one possible angle to the (r) evolution instigated by EVs, i.e., it provides the data science perspective review of the interdisciplinary area at the intersection of green transportation, energy informatics, and economics. Namely, the review summarizes data-driven research in EVs by identifying two main research streams: (i) socio–economic, and (ii) socio–technical. The socio–economic stream includes research in: (i) acceptance of green transportation in countries and among different populations, (ii) current trends in the EV market, and (iii) forecasting future sales for the green transportation. The socio–technical stream includes research in: (i) electric vehicle battery price and capacity and (ii) charging station management. This kind of study is especially important now when the question is no longer whether the transition from internal-combustion engine vehicles to clean-fuel vehicles is going to happen but how fast it will happen and what are going to be implications for society, governmental policies, and industry. Based on the presented literature review, the paper also outlines the most significant open questions and challenges that are yet to be solved: (i) scarcity of trustworthy (open) data, and (ii) designing a generalized methodology for charging station deployment.

This paper presents the SmartSocial Dataset which describes 1,826 Facebook users with lots of details, including their connections and their interactions (e.g., posts, likes, and comments). We first present a detailed analysis of descriptive and network characteristics of the SmartSocial Dataset to provide evidence for its representativeness. Afterward, we analyze the relationship between social and behavioral characteristics of SmartSocial Dataset users and Benford's Law as well as Dunbar's Number, to test whether Facebook has the power to change natural (Benford) and anthropological (Dunbar) laws. We find that Facebook's features are aligned with the Benford's Law but redefine the way how Dunbar's Number is calculated. Finally, we demonstrate how those findings could help researchers and business practitioners who collect Facebook data sets in a way to indicate whether there is serious sampling problem with the data set they collected.