Neural-blockchain-based ultrareliable caching for edge-enabled UAV networks
Abstract
Mobile edge computing (MEC) reduces the computational distance between the source and the servers by fortifying near-user site evaluations of data for expedited communications, using caching. Caching provides ephemeral storage of data on designated servers for low-latency transmissions. However, with the network following a hierarchical layout, even the near-user site evaluations can be impacted by the overheads associated with maintaining a perpetual connection and other factors (e.g., those relating to the reliability of the underpinning network). Prior solutions study reliability as a factor of throughput, delays, jitters, or delivery ratio. However, with modern networks supporting high data rates, a current research trend is in ultrareliability. The latter is defined in terms of availability, connectivity, and survivability. Thus, in this paper, we focus on the ultrareliable communication in MEC. Specifically, in our setting, we use drones as on-demand nodes for efficient caching. While some existing solutions use cache-enabled drones, they generally focus only on the positioning problem rather than factors relating to ultrareliable communications. We present a novel neural-blockchain-based drone-caching approach, designed to ensure ultrareliability and provide a flat architecture (via blockchain). This neural-model fortifies an efficient transport mechanism, since blockchain maintains high reliability amongst the peers involved in the communications. The findings from the evaluation demonstrate that the proposed approach scores well in the following metrics: the probability of connectivity reaches 0.99; energy consumption is decreased by 60.34%; the maximum failure rate is affected by 13.0%; survivability is greater than 0.90; reliability reaches 1.0 even for a large set of users.
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From Blockchain for 5G and beyond networks: A state of the art survey (2020) · cites this paper as [225]
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- Authors
- Sharma, V., You, I., Jayakody, D. N. K., Reina, D. G., & Choo, K.-K. R.
- Affiliations
- Soonchunhyang University (KR)
- Tomsk Polytechnic University (RU)
- The University of Texas at San Antonio (US)
- Venue
- IEEE Transactions on Industrial Informatics
- Published
- 2019-10
- Volume
- 15
- Issue
- 10
- Pages
- 5723-5736
- ISSN
- 1551-3203 (ISSN-L)
- 1941-0050
- OpenAlex
- W2952342730
- Cited by
- 108
- References
- 34
- Citations by year
- 2026: 1 · 2025: 6 · 2024: 10 · 2023: 10 · 2022: 22 · 2021: 31 · 2020: 25 · 2019: 3
- FWCI
- 11.09
- Type
- journal article
- Language
- en
- Open access
- closed
- Licence
- Locations
- IEEE Transactions on Industrial Informatics (publishedVersion)
- Funders
- Institute for Information and communications Technology Promotion
- South Korea Government (2017-0-00664)
- Soonchunhyang University Research Fund
- Topics
- Physical Sciences › Computer Science › Computer Networks and Communications › IoT and Edge/Fog Computing
- Physical Sciences › Computer Science › Computer Networks and Communications › Caching and Content Delivery
- Physical Sciences › Engineering › Aerospace Engineering › UAV Applications and Optimization
- Keywords
- mobile edge computing, UAV networks, blockchain, artificial neural networks, edge caching, network survivability, connectivity probability, energy consumption, failure rate, flat network architecture
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