Avoiding Data Corruption in Drop Computing Mobile Networks
Journal
IEEE Access
ISSN
2169-3536
Date Issued
2019
Author(s)
Ciobanu, Radu-Ioan
Tăbușcă, Vlăduţ Constantin
Dobre, Ciprian
Băjenaru, Lidia
Mavromoustakis, Constandinos
Mastorakis, George
DOI
10.1109/ACCESS.2019.2903018
Abstract
Drop computing is a network paradigm that aims to address the issues of the mobile cloud
computing model, which has started to show limitations especially since the advent of the Internet of Things
and the increase in the number of connected devices. In drop computing, nodes are able to offload data
and computations to the cloud, to edge devices, or to the social-based opportunistic network composed of
other nodes located nearby. In this paper, we focus on the lowest layer of drop computing, where mobile
nodes offload tasks and data to and from each other through close-range protocols, based on their social
connections. In such a scenario, where the data can circulate in the mobile network on multiple paths
(and through multiple other devices), consistency issues may appear due to data corruption or malicious
intent. Since there is no central entity that can control the way information is spread and its correctness,
alternative methods need to be employed. In this paper, we propose several mechanisms for ensuring data
consistency in drop computing, ranging from a rating system to careful analysis of the data received. Through
thorough experimentation, we show that our proposed solution is able to maximize the amount of correct
(i.e., uncorrupted) data exchanged in the network, with percentages as high as 100%.
computing model, which has started to show limitations especially since the advent of the Internet of Things
and the increase in the number of connected devices. In drop computing, nodes are able to offload data
and computations to the cloud, to edge devices, or to the social-based opportunistic network composed of
other nodes located nearby. In this paper, we focus on the lowest layer of drop computing, where mobile
nodes offload tasks and data to and from each other through close-range protocols, based on their social
connections. In such a scenario, where the data can circulate in the mobile network on multiple paths
(and through multiple other devices), consistency issues may appear due to data corruption or malicious
intent. Since there is no central entity that can control the way information is spread and its correctness,
alternative methods need to be employed. In this paper, we propose several mechanisms for ensuring data
consistency in drop computing, ranging from a rating system to careful analysis of the data received. Through
thorough experimentation, we show that our proposed solution is able to maximize the amount of correct
(i.e., uncorrupted) data exchanged in the network, with percentages as high as 100%.