Drones' Edge Intelligence Over Smart Environments in B5G: Blockchain and Federated Learning Synergy

Edge Intelligence is an emerging technology which has attracted significant attention. It applies Artificial Intelligence (AI) closer to the network edge for supporting Beyond fifth Generation (B5G) needs. On the other hand, drones can be used as relay station (mobile drone edge intelligence) to gat...

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Veröffentlicht in:IEEE transactions on green communications and networking 2022-03, Vol.6 (1), p.295-312
Hauptverfasser: Alsamhi, Saeed Hamood, Almalki, Faris A., Afghah, Fatemeh, Hawbani, Ammar, Shvetsov, Alexey V., Lee, Brian, Song, Houbing
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Sprache:eng
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Zusammenfassung:Edge Intelligence is an emerging technology which has attracted significant attention. It applies Artificial Intelligence (AI) closer to the network edge for supporting Beyond fifth Generation (B5G) needs. On the other hand, drones can be used as relay station (mobile drone edge intelligence) to gather data from smart environments. Federated Learning (FL) enables the drones to perform decentralized collaborative learning by developing local models, sharing the model parameters with neighbors and the centralized unit to improve global model accuracy in smart environments. However, drone edge intelligence faces challenges such as security and decentralization management, limiting its functions to support green smart environments. Blockchain is a promising technology that enables privacy-preserving data sharing in a distributed manner. There are several challenges that still need to be addressed in blockchain-based applications, such as scalability, energy efficiency, and transaction capacity. Motivated by the significance of FL and blockchain, this survey focuses on the synergy of FL and blockchain to enable drone edge intelligence for green sustainable environments. Moreover, we discuss the combination of FL and blockchain technological aspects, motivation, and framework for green smart environments. Finally, we discuss the challenges and opportunities, and future trends in this domain.
ISSN:2473-2400
2473-2400
DOI:10.1109/TGCN.2021.3132561