Neural-Blockchain-Based Ultrareliable Caching for Edge-Enabled UAV Networks

Vishal Sharma, Ilsun You, Dushantha Nalin K. Jayakody, Daniel Gutierrez Reina, Kim Kwang Raymond Choo

Research output: Contribution to journalArticle

2 Citations (Scopus)

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.

Original languageEnglish
Article number8734799
Pages (from-to)5723-5736
Number of pages14
JournalIEEE Transactions on Industrial Informatics
Volume15
Issue number10
DOIs
Publication statusPublished - 1 Oct 2019

Fingerprint

Unmanned aerial vehicles (UAV)
Communication
Servers
Jitter
Energy utilization
Throughput
Availability
Drones

Keywords

  • Blockchain
  • caching
  • drones
  • edge-computing
  • neural networks
  • ultrareliability
  • unmanned aerial vehicles (UAVs)

ASJC Scopus subject areas

  • Control and Systems Engineering
  • Information Systems
  • Computer Science Applications
  • Electrical and Electronic Engineering

Cite this

Neural-Blockchain-Based Ultrareliable Caching for Edge-Enabled UAV Networks. / Sharma, Vishal; You, Ilsun; Jayakody, Dushantha Nalin K.; Reina, Daniel Gutierrez; Choo, Kim Kwang Raymond.

In: IEEE Transactions on Industrial Informatics, Vol. 15, No. 10, 8734799, 01.10.2019, p. 5723-5736.

Research output: Contribution to journalArticle

Sharma, Vishal ; You, Ilsun ; Jayakody, Dushantha Nalin K. ; Reina, Daniel Gutierrez ; Choo, Kim Kwang Raymond. / Neural-Blockchain-Based Ultrareliable Caching for Edge-Enabled UAV Networks. In: IEEE Transactions on Industrial Informatics. 2019 ; Vol. 15, No. 10. pp. 5723-5736.
@article{e4f055771a3e45468a59a725b81383a8,
title = "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.",
keywords = "Blockchain, caching, drones, edge-computing, neural networks, ultrareliability, unmanned aerial vehicles (UAVs)",
author = "Vishal Sharma and Ilsun You and Jayakody, {Dushantha Nalin K.} and Reina, {Daniel Gutierrez} and Choo, {Kim Kwang Raymond}",
year = "2019",
month = "10",
day = "1",
doi = "10.1109/TII.2019.2922039",
language = "English",
volume = "15",
pages = "5723--5736",
journal = "IEEE Transactions on Industrial Informatics",
issn = "1551-3203",
publisher = "IEEE Computer Society",
number = "10",

}

TY - JOUR

T1 - Neural-Blockchain-Based Ultrareliable Caching for Edge-Enabled UAV Networks

AU - Sharma, Vishal

AU - You, Ilsun

AU - Jayakody, Dushantha Nalin K.

AU - Reina, Daniel Gutierrez

AU - Choo, Kim Kwang Raymond

PY - 2019/10/1

Y1 - 2019/10/1

N2 - 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.

AB - 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.

KW - Blockchain

KW - caching

KW - drones

KW - edge-computing

KW - neural networks

KW - ultrareliability

KW - unmanned aerial vehicles (UAVs)

UR - http://www.scopus.com/inward/record.url?scp=85073036972&partnerID=8YFLogxK

UR - http://www.scopus.com/inward/citedby.url?scp=85073036972&partnerID=8YFLogxK

U2 - 10.1109/TII.2019.2922039

DO - 10.1109/TII.2019.2922039

M3 - Article

AN - SCOPUS:85073036972

VL - 15

SP - 5723

EP - 5736

JO - IEEE Transactions on Industrial Informatics

JF - IEEE Transactions on Industrial Informatics

SN - 1551-3203

IS - 10

M1 - 8734799

ER -