Guest Editorial: Drones for smart cities
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Smart cities and unmanned aerial vehicles (UAVs) are two relatively recent concepts and also hot topics in research. The combination of these two technologies is expected to propel their capabilities even further for enabling revolutionary applications that will improve our quality of life. This Special Issue focuses on novel work done on the application of UAVs where state-of-the-art technologies in sensing, information dissemination, communications, and artificial intelligence (AI) are applied within the context of smart cities. Over the years, we have witnessed the joint efforts of academia and industry that have led to not only the introduction of novel applications but also the improvement of communications and the use of AI-based approaches intended to make use of UAVs in future smart cities. However, many issues remain unsolved. Further research efforts are required in the fields of drone networking, sensing, and autonomous driving; including information sharing and delivery, providing common understanding platforms, smart sensing, and also new communication paradigms for the advancement of drone systems within smart cities. This Special Issue aimed to investigate the above-mentioned open issues related to ‘Drones for Smart Cities’ and collected three (03) high-quality papers that were accepted after a rigorous review process. A review of some of the technical difficulties with aerial coordination and interaction that multirotor UAVs still encounter was presented by Fabra et al. [1]. In order to achieve collision-free flights and swarm-based missions, they highlighted recent advancements that have been published in the literature and presented some recent contributions. The study in this work allows the authors to offer insight into the issues that still need to be resolved in order to make it possible for UAV-based solutions to support sustainable aerial services. The study of Popescu et al. [2] examined the potential hovering locations based on each hovering location's unique constraints, such as flight time and coverage, in order to increase connection and ensure data rates in the 5G network. They presented analytical bounds on the connection expansion needs for fixed enhanced mobile broadband infrastructure serving vehicle networks, where both infrastructures and vehicular networks are analysed using stochastic and fractal geometry as a model for urban environments. Overall, the results presented a realistic stochastic communication model for investigating the growth of 5G in smart cities. The computation of precise bounds and the identification of specific behaviours served to highlight the appeal of such a creative framework (such as the characterisation of a threshold). It is also a start in the direction of creating a framework for ‘smart city modeling’ that may be used in different urban contexts. Finally, Rathee et al. [3] developed a trustworthy drone-based communication system for smart cities, in which intelligent devices conduct surveillance using drones. By classifying each device into legitimate and malicious ones, behaviour-based and local trust models are utilised to examine how each one communicates. Higher trust levels allow nodes to continue communicating and to be part of the network. The blockchain network, where each device's trust value is maintained as a block in the network, once again monitors and analyses the system. The suggested method has been tested and compared to the current scheme; it has outperformed it in terms of throughput, latency, accuracy, and block updating limit. Because there is reduced communication storage overhead and delay, the proposed approach performs better than the current methods. This special issue would not have been as successful without the remarkable effort and commitment of the reviewers and the dedication of authors to high quality contributions. We are grateful to all of them. We also extend our gratitude to the co-EiCs Prof. William Webb and Prof. Chai K. Toh and the editorial team at IET Smart Cities, for their support and dedication throughout the realisation of this special issue. Francisco J. Martinez ([email protected]) received the B.Sc. degree in computer science and the B.Sc. degree in documentation from the Technical University of Valencia in 1996 and 1999, respectively. He received the Ph.D. degree in Computer Engineering from the Technical University of Valencia, in 2010. He is currently a Full Professor with the Department of Computers and System Engineering, University of Zaragoza, Spain. He is also the Founder of the Intelligent Networks and Information Technologies (iNiT) research group. He has authored over 100 papers published in well-recognised conferences and journals. His current research interests include vehicular networks, intelligent transportation systems, electric vehicles, traffic safety, vehicle-to-everything (V2X) communications, and smart agriculture. He received the Extraordinary Doctorate Award. He serves as an associate editor, a reviewer, and a TPC member in different international journals and conferences. Chaker Abdelaziz Kerrache ([email protected]) is an Associate Professor at the department of Computer Science, University of Laghouat, Algeria. He is currently the head of the Informatics and Mathematics Laboratory (LIM) at the University of Laghouat. He received his MSc. degree in computer science in 2012, and his Ph.D. degree in computer science in 2017, both at the University of Laghouat, Algeria. His research activity is related to trust management, secure multi-hop communications, Named Data Networking (NDN), and UAVs. Abderrahmane Lakas ([email protected]) received his M.S. (1990) and Ph.D. (1996) in computer systems from the University of Paris 6, France. He is currently a Professor of Computer Engineering at the College of Information Technology, UAE University. Prior to joining UAE University in 2003, he had many years of industrial experience working in telecommunication companies such as Netrake (Plano, Texas/US, 2002), Nortel (Ottawa, Canada, 2000) and Newbridge (Ottawa, Canada, 1998). Dr. Lakas, has been conducting research in various areas of network design and performance, wireless networks, security and privacy, vehicular networks, Internet of Things, autonomous systems, unmanned aerial vehicles. He serves or served as associate editor in IEEE Access, IET Networks, the Journal of Communications (Actapress), and the Journal of Computer Systems, Networks, and Communications (Hindawi). He is a reviewer for many journals and an active participant in many of the international conferences in his field of research. Data sharing is not applicable to this article as no new data were created or analysed in this study.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it