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Editorial Board Members’ Collection Series: On Selected Areas in Distributed...
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Editorial Board Members’ Collection Series: On Selected Areas in Distributed Privacy and Security of Drones

A special issue of Drones (ISSN 2504-446X).

Deadline for manuscript submissions: closed (30 April 2023) | Viewed by 4564

Special Issue Editor

Dr. Shiva Raj Pokhrel

E-Mail Website
Guest Editor
School of Info Technology, Faculty of Science, Engineering and Built Environment, Deakin University, Melbourne Burwood Campus, Burwood, VIC 3217, Australia
Interests: autonomous vehicles; federated learning; blockchain modelling; optimization; recommender systems; cloud computing; dynamics control; Internet of Things; cyber-physical systems; manufacturing
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

We are pleased to announce a new collection entitled “Editorial Board Members’ Collection Series: On Selected Areas in Distributed Privacy and Security of Drones”, which will collect papers invited by the Editorial Board Members. 

The aim of this collection is to provide an opportunity to explore “novel theories of decentralized privacy, security and safety in relation to Drones and their automation and orchestration”. All papers will be published in an open access format following peer review.

Dr. Shiva Raj Pokhrel
Guest Editor

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Drones is an international peer-reviewed open access monthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2600 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Published Papers (2 papers)

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Research

17 pages, 2014 KiB  
Article
Optimizing Performance in Federated Person Re-Identification through Benchmark Evaluation for Blockchain-Integrated Smart UAV Delivery Systems
by Chengzu Dong, Jingwen Zhou, Qi An, Frank Jiang, Shiping Chen, Lei Pan and Xiao Liu
Drones 2023, 7(7), 413; https://doi.org/10.3390/drones7070413 - 22 Jun 2023
Cited by 6 | Viewed by 1523
Abstract
In recent years, edge-based intelligent UAV delivery systems have attracted significant interest from both the academic and industrial sectors. One key obstacle faced by these smart UAV delivery systems is data privacy, as they rely on vast amounts of data from users and [...] Read more.
In recent years, edge-based intelligent UAV delivery systems have attracted significant interest from both the academic and industrial sectors. One key obstacle faced by these smart UAV delivery systems is data privacy, as they rely on vast amounts of data from users and UAVs for training machine learning models for person re-identification (ReID) purposes. To tackle this issue, federated learning (FL) has been extensively adopted as a promising solution since it only involves sharing and updating model parameters with a central server, without transferring raw data. However, traditional FL still suffers from the problem of having a single point of failure. In this study, we present a performance optimization method for federated person re-identification using benchmark analysis in blockchain-powered edge-based smart UAV delivery systems. Our method integrates a decentralized FL mechanism enabled by blockchain, which eliminates the necessity for a central server and stores private data on a decentralized permissioned blockchain, thus preventing a single point of failure. We employ the person ReID application in intelligent UAV delivery systems as a representative example to drive our research and examine privacy concerns. Additionally, we introduce the Federated Re-identification Consensus (FRC) protocol to address the scalability issue of the blockchain in supporting UAV delivery systems. The efficiency of our proposed method is illustrated through experiments on energy efficiency, confirmation time, and throughput. We also explore the effects of the incentive mechanism and analyze the system’s resilience under various security attacks. This study offers valuable insights and potential solutions for addressing data privacy and security challenges in the fast-growing domain of smart UAV delivery systems. Full article
(This article belongs to the Special Issue Editorial Board Members’ Collection Series: On Selected Areas in Distributed Privacy and Security of Drones)
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18 pages, 1056 KiB  
Article
Preserving Privacy of Classified Authentic Satellite Lane Imagery Using Proxy Re-Encryption and UAV Technologies
by Yarajarla Nagasree, Chiramdasu Rupa, Ponugumati Akshitha, Gautam Srivastava, Thippa Reddy Gadekallu and Kuruva Lakshmanna
Drones 2023, 7(1), 53; https://doi.org/10.3390/drones7010053 - 12 Jan 2023
Cited by 19 | Viewed by 2512
Abstract
Privacy preservation of image data has been a top priority for many applications. The rapid growth of technology has increased the possibility of creating fake images using social media as a platform. However, many people, including researchers, rely on image data for various [...] Read more.
Privacy preservation of image data has been a top priority for many applications. The rapid growth of technology has increased the possibility of creating fake images using social media as a platform. However, many people, including researchers, rely on image data for various purposes. In rural areas, lane images have a high level of importance, as this data can be used for analyzing various lane conditions. However, this data is also being forged. To overcome this and to improve the privacy of lane image data, a real-time solution is proposed in this work. The proposed methodology assumes lane images as input, which are further classified as fake or bona fide images with the help of Error Level Analysis (ELA) and artificial neural network (ANN) algorithms. The U-Net model ensures lane detection for bona fide lane images, which helps in the easy identification of lanes in rural areas. The final images obtained are secured by using the proxy re-encryption technique which uses RSA and ECC algorithms. This helps in ensuring the privacy of lane images. The cipher images are maintained using fog computing and processed with integrity. The proposed methodology is necessary for protecting genuine satellite lane images in rural areas, which are further used by forecasters, and researchers for making interpretations and predictions on data. Full article
(This article belongs to the Special Issue Editorial Board Members’ Collection Series: On Selected Areas in Distributed Privacy and Security of Drones)
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