Reliable Data Analysis through Blockchain based Crowdsourcing in Mobile Ad-hoc Cloud
Journal article
Rasool, S, Iqbal, M, Dagiuklas, T, Ul Qayyum, Z and Shancang, L (2019). Reliable Data Analysis through Blockchain based Crowdsourcing in Mobile Ad-hoc Cloud. Mobile Networks and Applications. 25, pp. 153-163. https://doi.org/10.1007/s11036-019-01221-x
Authors | Rasool, S, Iqbal, M, Dagiuklas, T, Ul Qayyum, Z and Shancang, L |
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Abstract | Mobile Ad-hoc Cloud (MAC) is the constellation of nearby mobile devices to serve the heavy computational needs of the resource constrained edge devices. One of the major challenges of MAC is to convince the mobile devices to offer their limited resources for the shared computational pool. Credit based rewarding system is considered as an effective way of incentivizing the arbitrary mobile devices for joining the MAC network and to earn the credits through computational crowdsourcing. The next challenge is to get the reliable computation as incentives attract the malicious devices to submit fake computational results for claiming their reward and we have used the blockchain based reputation system for identifying the malicious participants of MAC. This paper presents a malicious node identification algorithm integrated within the Iroha based permissioned blockchain. Iroha is a project of hyperledger which is focused on mobile devices and thus light-weight in nature. It is used for keeping the track of rewarding and reputation system driven by the malicious node detection algorithm. Experiments are conducted for evaluated the implemented test-bed and results show the effectiveness of algorithm in identifying the malicious devices and conducting the reliable data analysis through the blockchain based computational crowdsourcing in MAC. This is a post-peer-review, pre-copyedit version of an article published in Mobile Networks and Applications. The final authenticated version is available online at: https://link.springer.com/journal/11036 |
Year | 2019 |
Journal | Mobile Networks and Applications |
Journal citation | 25, pp. 153-163 |
Publisher | Springer |
ISSN | 1383-469X |
Digital Object Identifier (DOI) | https://doi.org/10.1007/s11036-019-01221-x |
Web address (URL) | https://link.springer.com/article/10.1007/s11036-019-01221-x |
Publication dates | |
Online | 01 Jun 2019 |
01 Feb 2020 | |
Publication process dates | |
Deposited | 01 Apr 2019 |
Accepted | 18 Jan 2019 |
Accepted author manuscript | License File Access Level Open |
https://openresearch.lsbu.ac.uk/item/867z1
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