Title
Effectively Collecting Data for the Location-Based Authentication in Internet of Things
Abstract
The concept of Internet of things (IoT) has attracted attention as a key technology for realizing future industrial society. In the future society, numerous “things” with sensors are deployed and connected to networks, and data collected from these devices are used for a wide variety of industrial applications. In this paper, we focus on data collection for location-based authentication system as an application of industrial IoT. The authentication system uses ambient information, which is collected from the devices as unique information at a certain place and a certain time. However, since the ambient information changes continuously, it is required to collect it in real time from multipoint. Thus, we propose an efficient data collection method considering the requirements from the authentication system. The key point is to regulate the network performance for data collection by considering the application requirements. Since the location-based authentication system can be used in many situations and has large expansivity, the proposed work is considered to significantly contribute to the future industrial IoT society. In addition, we demonstrate how to optimize the operation of our proposal by using mathematical analysis. Moreover, the efficiency of our proposed method is validated through numerical results.
Year
DOI
Venue
2017
10.1109/JSYST.2015.2456878
IEEE Systems Journal
Keywords
Field
DocType
Ambient information,Carrier Sense Multiple Access/Collision Avoidance (CSMA/CA),Internet of things (IoT),authentication,data collection
Data collection,Authentication,Authentication system,Computer science,Internet of Things,Server,Computer network,Industrial society,Authentication protocol,Network performance
Journal
Volume
Issue
ISSN
PP
99
1932-8184
Citations 
PageRank 
References 
5
0.50
14
Authors
4
Name
Order
Citations
PageRank
Yuichi Kawamoto130526.42
Hiroki Nishiyama2128592.61
Nei Kato33982263.66
Shimizu, Y.450.50