UAV & Drones

This project focuses on solving various issues related to networking in UAV's including security, charge scheduling etc.

Team Members

Vinay Chamola (Senior Member, IEEE)
Vikas Hassija
Tejasvi Alladi

Collaborators

Mohsen Guizani, Qatar University (Fellow, IEEE)
Dusit Niyato, Nanyang Technological University, Singapore (Fellow, IEEE)
Richard Yu, Carleton University, Canada (Fellow, IEEE)

Our publications in UAV and drones

V. Hassija, V. Chamola, N. Dara and M. Guizani, "A Distributed Framework for Charge Trading Between UAVs and Charging Stations", IEEE Transactions on Vehicular Technology (Impact Factor: 5.3)

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Abstract

Use of Unmanned Aerial Vehicles (UAVs) is rapidly increasing in various domains such as disaster management, delivery of goods, surveillance, military, etc. Significant issues in the expansion of UAV-based applications are the security of (IoT to UAV) communication, and the limited flight time of the UAVs and IoT devices considering the limited battery power. Standalone UAVs are not capable of accomplishing several tasks, and therefore swarm of UAVs is being explored. Security issues in the swarm of UAVs do not allow the applications to leverage the full benefits that one can offer. Several recent studies have proposed the use of a distributed network of UAVs to upgrade the level of security in the swarm of UAVs. In this paper, a framework for secure and reliable energy trading among UAVs and charging stations is presented. Advanced blockchain, based on the tangle data structure is used to create a distributed network of UAVs and charging stations. The proposed model allows the UAVs to buy energy from the charging station in exchange for tokens. If the UAV does not have sufficient tokens to buy the energy, then the model allows the UAV to borrow tokens from the charging station. The borrowed tokens can be repaid back to the charging station with interest or late fees. A game-theoretic model is used for deciding the buying strategy of energy for UAVs. Numerical analysis shows that the proposed model helps in providing increased utility for the swarm of UAVs and charging stations in a secure and cost-optimal way as compared to the conventional schemes. The results can eventually be applied to IoT devices that constantly need energy to perform under ideal conditions.

V. Chamola, V. Hassija, V. Garg, M. Guizani, “A Comprehensive Review of the COVID-19 Pandemic and the Role of IoT, Drones, AI, Blockchain, and 5G in Managing Its Impact”, IEEE Access, April 2020 (Impact Factor: 4)

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Abstract

The unprecedented outbreak of the 2019 novel coronavirus, termed as COVID-19 by the World Health Organization (WHO), has placed numerous governments around the world in a precarious position. The impact of the COVID-19 outbreak, earlier witnessed by the citizens of China alone, has now become a matter of grave concern for virtually every country in the world. The scarcity of resources to endure the COVID-19 outbreak combined with the fear of overburdened healthcare systems has forced a majority of these countries into a state of partial or complete lockdown. The number of laboratory-confirmed coronavirus cases has been increasing at an alarming rate throughout the world, with reportedly more than 3 million confirmed cases as of 30 April 2020. Adding to these woes, numerous false reports, misinformation, and unsolicited fears in regards to coronavirus, are being circulated regularly since the outbreak of the COVID-19. In response to such acts, we draw on various reliable sources to present a detailed review of all the major aspects associated with the COVID-19 pandemic. In addition to the direct health implications associated with the outbreak of COVID-19, this study highlights its impact on the global economy. In drawing things to a close, we explore the use of technologies such as the Internet of Things (IoT), Unmanned Aerial Vehicles (UAVs), blockchain, Artificial Intelligence (AI), and 5G, among others, to help mitigate the impact of COVID-19 outbreak.

T. Alladi, V. Chamola, N. Sahoo and M. Guizani, “Application of Blockchain in Unmanned Aerial Vehicles: A Review”, Vehicular Communications, Elsevier, Jan. 2020 (Impact Factor: 3.5)

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Abstract

The recent advancement in Unmanned Aerial Vehicles (UAVs) in terms of manufacturing processes, and communication and networking technology has led to a rise in their usage in civilian and commercial applications. The regulations of the Federal Aviation Administration (FAA) in the US had earlier limited the usage of UAVs to military applications. However more recently, the FAA has outlined new enforcement that will also expand the usage of UAVs in civilian and commercial applications. Due to being deployed in open atmosphere, UAVs are vulnerable to being lost, destroyed or physically hijacked. With the UAV technology becoming ubiquitous, various issues in UAV networks such as intra-UAV communication, UAV security, air data security, data storage and management, etc. need to be addressed. Blockchain being a distributed ledger protects the shared data using cryptography techniques such as hash functions and public key encryption. It can also be used for assuring the truthfulness of the information stored and for improving the security and transparency of the UAVs. In this paper, we review various applications of blockchain in UAV networks such as network security, decentralized storage, inventory management, surveillance, etc., and discuss some broader perspectives in this regard. We also discuss various challenges to be addressed in the integration of blockchain and UAVs and suggest some future research directions. Index Terms-Unmanned Aerial Vehicle (UAV) network, security and privacy, blockchain technology, Internet of Things (IoT).

V. Hassija, V. Saxena and V. Chamola, “Scheduling Drone Charging for Multi-Drone Network based on Consensus Time-stamp and Game Theory”, Computer Communications, Elsevier, vol. 149, pp. 51-61, Jan 2020 (Impact Factor: 3)

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Abstract

Drones or Unmanned Aerial Vehicles (UAVs) can be highly efficient in various applications like hidden area exploration, delivery, or surveillance and can enhance the quality of experience (QoE) for end-users. However, the number of drone-based applications are not very high due to the constrained flight time. The weights of the drones need to be kept less, and intuitively they cannot be loaded with big batteries. Frequent recharging and battery replacement processes limit the appropriate use of drones in most applications. A peer-to-peer distributed network of drones and charging stations is a highly promising solution to empower drones to be used in multiple applications by increasing their flight time. The charging stations are limited, and therefore, an adequate, fair, and cost-optimal scheduling algorithm is required to serve the most needed drone first. The proposed model allows the drones to enter into the network and request for a charging time slot from the station. The stations are also the part of the same network, this work proposes a scheduling algorithm for drones who compete for charging slots with constraints of optimizing criticality and task deadline. A game-theoretic approach is used to model the energy trading between the drones and charging station in a cost-optimal manner. Numerical results based on simulations show that the proposed model provides a better price for the drones to get charged and better revenue for the charging stations simultaneously.

T. Alladi, Naren, V. Chamola and N. Kumar, “PARTH: A two-stage lightweight mutual authentication protocol for UAV surveillance networks,” Computer Communications, Elsevier, Mar. 2020 (Impact Factor: 3)

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Abstract

Internet of things (IoT) is the next era of communication. Using IoT, physical objects can be empowered to create, receive and exchange data in a seamless manner. Various IoT applications focus on automating different tasks and are trying to empower the inanimate physical objects to act without any human intervention. The existing and upcoming IoT applications are highly promising to increase the level of comfort, efficiency, and automation for the users. To be able to implement such a world in an ever growing fashion requires high security, privacy, authentication, and recovery from attacks. In this regard, it is imperative to make the required changes in the architecture of IoT applications for achieving end-to-end secure IoT environments. In this paper, a detailed review of the security-related challenges and sources of threat in IoT applications is presented. After discussing the security issues, various emerging and existing technologies focused on achieving a high degree of trust in IoT applications are discussed. Four different technologies: Blockchain, fog computing, edge computing, and machine learning to increase the level of security in IoT are discussed.

P. Garg, A. Srinivasan, M. Mandal, P. Narang, V. Chamola and M. Guizani, "ISDNet: AI-enabled Instance Segmentation of Aerial Scenes for Smart Cities", ACM Transactions on Internet Technology, Accepted July 2020 (Impact factor: 3.75)

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Abstract

Aerial scenes captured by UAVs have immense potential in IoT applications related to urban surveillance, road and building segmentation, land cover classification, etc. which are necessary for the evolution of smart cities. The advancements in deep learning have greatly enhanced visual understanding, but the domain of aerial vision remains largely unexplored. Aerial images pose many unique challenges for performing proper scene parsing such as high-resolution data, small-scaled objects, a large number of objects in the camera view, dense clustering of objects, background clutter, etc., which greatly hinder the performance of the existing deep learning methods. In this work, we propose ISDNet (Instance Segmentation and Detection Network), a novel network to perform instance segmentation and object detection on visual data captured by UAVs. This work enables aerial image analytics for various needs in a smart city. In particular, we use dilated convolutions to generate improved spatial context, leading to better discrimination between foreground and background features. The proposed network efficiently reuses the segment-mask features by propagating them from early stages using residual connections. Furthermore, ISDNet makes use of effective anchors to accommodate varying object scales and sizes. The proposed method obtains state-of-the-art results in the aerial context.Aerial scenes captured by UAVs have immense potential in IoT applications related to urban surveillance, road and building segmentation, land cover classification, etc. which are necessary for the evolution of smart cities. The advancements in deep learning have greatly enhanced visual understanding, but the domain of aerial vision remains largely unexplored. Aerial images pose many unique challenges for performing proper scene parsing such as high-resolution data, small-scaled objects, a large number of objects in the camera view, dense clustering of objects, background clutter, etc., which greatly hinder the performance of the existing deep learning methods. In this work, we propose ISDNet (Instance Segmentation and Detection Network), a novel network to perform instance segmentation and object detection on visual data captured by UAVs. This work enables aerial image analytics for various needs in a smart city. In particular, we use dilated convolutions to generate improved spatial context, leading to better discrimination between foreground and background features. The proposed network efficiently reuses the segment-mask features by propagating them from early stages using residual connections. Furthermore, ISDNet makes use of effective anchors to accommodate varying object scales and sizes. The proposed method obtains state-of-the-art results in the aerial context.