Blockchain-based IoT platform for autonomous drone operations management

Dawaliby, Samir; Aberkane, Arezki; Bradai, Abbas

doi:10.1145/3414045.3415939

Cited by 18 publications

(14 citation statements)

References 13 publications

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“…Vehicles authentication and privacy protection in 6G networks have seriously restricted the development of IoV, especially when being dependent on a centralized trusted authority to distribute identity information [165]. This problem can be solved with blockchain due to its many potential uses in IoV when including the distributed ledger to secure the network and enable autonomous communications [166], radio access network decentralization [167] and mobile service authorization [168]. The integration of both AI and blockchain is highly trending because it provides security, intelligent architecture, and flexible resource sharing [169] for IoV as well as Industrial IoT networks [170].…”

Section: Security Enhancement With Blockchainmentioning

confidence: 99%

Deep reinforcement learning techniques for vehicular networks: Recent advances and future trends towards 6G

Mekrache

Bradai

Moulay

et al. 2022

Vehicular Communications

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Employing machine learning into 6G vehicular networks to support vehicular application services is being widely studied and a hot topic for the latest research works in the literature. This article provides a comprehensive review of research works that integrated reinforcement and deep reinforcement learning algorithms for vehicular networks management with an emphasis on vehicular telecommunications issues. Vehicular networks have become an important research area due to their specific features and applications such as standardization, efficient traffic management, road safety, and infotainment. In such networks, network entities need to make decisions to maximize network performance under uncertainty. To achieve this goal, Reinforcement Learning (RL) can effectively solve decision-making problems. However, the state and action spaces are massive and complex in large-scale wireless networks. Hence, RL may not be able to find the best strategy in a reasonable time. Therefore, Deep Reinforcement Learning (DRL) has been developed to combine RL with Deep Learning (DL) to overcome this issue. In this survey, we first present vehicular networks and give a brief overview of RL and DRL concepts. Then we review RL and especially DRL approaches to address emerging issues in 6G vehicular networks. We finally discuss and highlight some unresolved challenges for further study.

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Section: Security Enhancement With Blockchainmentioning

confidence: 99%

Deep reinforcement learning techniques for vehicular networks: Recent advances and future trends towards 6G

Mekrache

Bradai

Moulay

et al. 2022

Vehicular Communications

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“…Authors in [22] explored the use of blockchain for authentication and data sharing in a 5G-based drone network. In another work in [38] the authors have proposed a blockchain-based IoT platform for the management of autonomous operations of drones. A detailed system model for the deployment of service is presented along with various operations of the drones in the proposed ecosystem.…”

Section: Legacy Blockchain For Drone Securitymentioning

confidence: 99%

Derived blockchain architecture for security-conscious data dissemination in edge-envisioned Internet of Drones ecosystem

2022

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Internet of Drones (IoD) facilitates the autonomous operations of drones into every application (warfare, surveillance, photography, etc) across the world. The transmission of data (to and fro) related to these applications occur between the drones and the other infrastructure over wireless channels that must abide to the stringent latency restrictions. However, relaying this data to the core cloud infrastructure may lead to a higher round trip delay. Thus, we utilize the cloud close to the ground, i.e., edge computing to realize an edge-envisioned IoD ecosystem. However, as this data is relayed over an open communication channel, it is often prone to different types of attacks due to it wider attack surface. Thus, we need to find a robust solution that can maintain the confidentiality, integrity, and authenticity of the data while providing desired services. Blockchain technology is capable to handle these challenges owing to the distributed ledger that store the data immutably. However, the conventional block architecture pose several challenges because of limited computational capabilities of drones. As the size of blockchain increases, the data flow also increases and so does the associated challenges. Hence, to overcome these challenges, in this work, we have proposed a derived blockchain architecture that decouples the data part (or block ledger) from the block header and shifts it to off-chain storage. In our approach, the registration of a new drone is performed to enable legitimate access control thus ensuring identity management and traceability. Further, the interactions happen in the form of transactions of the blockchain. We propose a lightweight consensus mechanism based on the stochastic selection followed by a transaction signing process to ensure that each drone is in control of its block. The proposed scheme also handles the expanding storage requirements with the help of data compression using a shrinking block mechanism. Lastly, the problem of additional delay anticipated due to drone mobility is handled using a multi-level caching mechanism. The proposed work has been validated in a simulated Gazebo environment and the results are promising in terms of different metrics. We have also provided numerical validations in context of complexity, communication overheads and computation costs.

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“…Dawaliby et al [ 71 ] designed an architecture-based blockchain to handle IoT drone operations while retaining and security. The results of the introduced architecture obtained in the case of practical agricultural usage illustrate the usefulness in reducing signaling and operating time, improving the percentage of effective maintenance operations, and providing trust and protection in autonomous drone management.…”

Section: Recent Advancementsmentioning

confidence: 99%

The Internet of Drones: Requirements, Taxonomy, Recent Advances, and Challenges of Research Trends

Abdelmaboud

2021

Sensors

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The use of unmanned aerial vehicles or drones are a valuable technique in coping with issues related to life in the general public’s daily routines. Given the growing number of drones in low-altitude airspace, linking drones to form the Internet of drones (IoD) is a highly desirable trend to improve the safety as well as the quality of flight. However, there remain security, privacy, and communication issues related to IoD. In this paper, we discuss the key requirements of security, privacy, and communication and we present a taxonomy of IoD based on the most relevant considerations. Furthermore, we present the most commonly used commercial case studies and address the latest advancements and solutions proposed for the IoD environments. Lastly, we discuss the challenges and future research directions of IoD.

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Blockchain-based IoT platform for autonomous drone operations management

Cited by 18 publications

References 13 publications

Deep reinforcement learning techniques for vehicular networks: Recent advances and future trends towards 6G

Deep reinforcement learning techniques for vehicular networks: Recent advances and future trends towards 6G

Derived blockchain architecture for security-conscious data dissemination in edge-envisioned Internet of Drones ecosystem

The Internet of Drones: Requirements, Taxonomy, Recent Advances, and Challenges of Research Trends

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