Christos Cholevas

Non-fungible tokens (NFTs) are unique digital assets powered by blockchain technology, enabling secure and decentralized ownership and monetization across diverse industries. However, their energy consumption is primarily linked to energy-intensive consensus mechanisms and has raised significant environmental concerns. This survey provides a comprehensive analysis of the evolution and use of token standards and blockchain workflows in developing green NFT technologies, emphasizing the transition to energy-efficient consensus mechanisms. By seamlessly blending technological acumen with a discerning gaze, the current analysis suggests that, apart from the blockchain consensus mechanisms, the environmental impact of NFTs should also be investigated and linked to certain blockchain factors such as interoperability, scalability, sustainability, and Layer-2 scaling solutions. As such, the current endeavor offers a perspective on the symbiotic relationship between blockchain and NFTs by identifying pathways to balance innovation with environmental stewardship. Finally, this paper offers valuable insights into the role of green NFTs in fostering sustainable digital economies by exploring under-represented applications in various economic and industrial domains.

In decentralized systems, the quest for heightened security and integrity within blockchain networks becomes an issue. This survey investigates anomaly detection techniques in blockchain ecosystems through the lens of unsupervised learning, delving into the intricacies and going through the complex tapestry of abnormal behaviors by examining avant-garde algorithms to discern deviations from normal patterns. By seamlessly blending technological acumen with a discerning gaze, this survey offers a perspective on the symbiotic relationship between unsupervised learning and anomaly detection by reviewing this problem with a categorization of algorithms that are applied to a variety of problems in this field. We propose that the use of unsupervised algorithms in blockchain anomaly detection should be viewed not only as an implementation procedure but also as an integration procedure, where the merits of these algorithms can effectively be combined in ways determined by the problem at hand. In that sense, the main contribution of this paper is a thorough study of the interplay between various unsupervised learning algorithms and how this can be used in facing malicious activities and behaviors within public and private blockchain networks. The result is the definition of three categories, the characteristics of which are recognized in terms of the way the respective integration takes place. When implementing unsupervised learning, the structure of the data plays a pivotal role. Therefore, this paper also provides an in-depth presentation of the data structures commonly used in unsupervised learning-based blockchain anomaly detection. The above analysis is encircled by a presentation of the typical anomalies that have occurred so far along with a description of the general machine learning frameworks developed to deal with them. Finally, the paper spotlights challenges and directions that can serve as a comprehensive compendium for future research efforts.