Following the awareness of the need to optimize the sustainability of public transportation resources, we conduct a systematic literature review of the current Internet of Things-based approaches and technologies improving sustainable public transportation in smart cities. Using internationally peer-reviewed literature, we analyze the current state of research and identify research gaps. Major findings include the flexibility of various sensors for different use cases, data collection hot spots in public transportation, and the quality of service enhancements for passengers. Our study aims to provide practitioners and researchers with guidance on applying Internet of Things-based approaches to develop smart and sustainable transportation solutions in smart cities.

As the identification of the energy consumption represents a crucial part of the smart grid, smart meters are considered one of the most important devices in the evolution of the electrical grid. Following the recent developments which have given rise to deep learning, this paper systematically reviews the literature on deep learning approaches in smart meter data analytics. To systematically structure and analyze the current state of research, we propose a framework for deep learning-based smart meter data analytics, which investigates relevant internationally peer-reviewed literature in the field against the background of the main future challenges of smart meter data analytics. Our research aims to foster the understanding and adaption of modern deep learning methods to solve existing challenges regarding the energy supply and identify future research needs.

This study systematically reviews international peer-reviewed literature to show existing scientific approaches on how machine learning and deep learning methods can improve the detection of events and disturbances in smart grid systems. Smart grids can adapt and react flexibly to different situations. Different approaches can be exploited to protect the whole system more efficiently. Using an extended smart control center framework, we systematically structure our literature analysis, allowing us to identify unaddressed research gaps, guiding future research on contributing to ensuring and improving security in smart grids.

Growing data acquisition in smart grid technology leads to considerable privacy concerns, requiring standardized and sophisticated solutions that ensure consumers’ privacy. In this paper, we systematically review international peer-reviewed publications, targeting to improve and ensure data privacy in the context of smart meter data analysis. Our research shows that a remarkable trend towards privacy-preserving aggregation schemes has been established in recent years, advancing future research and providing data privacy for smart grid participants.