research-article

IoT Data Prefetching in Indoor Navigation SOAs

Authors:

Andreas Konstantinidis,

Panagiotis Irakleous,

Zacharias Georgiou,

Demetrios Zeinalipour-Yazti,

Panos K. ChrysanthisAuthors Info & Claims

ACM Transactions on Internet Technology (TOIT), Volume 19, Issue 1

Article No.: 10, Pages 1 - 21

https://doi.org/10.1145/3177777

Published: 30 November 2018 Publication History

Abstract

Internet-based Indoor Navigation Service-Oriented Architectures (IIN-SOA) organize signals collected by IoT-based devices to enable a wide range of novel applications indoors, where people spend 80--90% of their time. In this article, we study the problem of prefetching (or hoarding) the most important IoT data from an IIN-SOA to a mobile device, without knowing its user’s destination during navigation. Our proposed Grap (Graph Prefetching) framework structurally analyzes building topologies to identify important areas that become virtual targets to an online heuristic search algorithm we developed. We tested Grap with datasets from a real IIN-SOA and found it to be impressively accurate.

References

[1]

A. Al-Fuqaha, M. Guizani, M. Mohammadi, M. Aledhari, and M. Ayyash. 2015. Internet of things: A survey on enabling technologies, protocols, and applications. IEEE Comm. Surv. Tutor. 17, 4 (2015), 2347--2376.

Digital Library

[2]

L. Atzori, A. Iera, and G. Morabito. 2010. The internet of things: A survey. Comput. Netw. 54, 15 (2010), 2787--2805.

Digital Library

[3]

C. Becker and F. Dürr. 2005. On location models for ubiquitous computing. Personal Ubiquitous Comput. 9, 1 (2005), 20--31.

Digital Library

[4]

C. Bouras, A. Konidaris, and D. Kostoulas. 2004. Predictive prefetching on the web and its potential impact in the wide area. World Wide Web 7, 2 (2004), 143--179.

Digital Library

[5]

S. Brin and L. Page. 1998. The anatomy of a large-scale hypertextual web search engine. In Proceedings of the Seventh International Conference on World Wide Web 7 (WWW7). Elsevier Science Publishers B. V., 107--117.

Digital Library

[6]

G. Chatzimiloudis, A. Konstantinidis, C. Laoudias, and D. Zeinalipour-Yazti. 2012. Crowdsourcing with Smartphones. IEEE Internet Comput. 16, 5 (2012), 36--44.

Digital Library

[7]

M. Dong and L. Zhong. 2011. Self-constructive high-rate system energy modeling for battery-powered mobile systems. In Proceedings of the 9th International Conference on Mobile Systems, Applications, and Services (MobiSys'11). ACM, 335--348.

Digital Library

[8]

L. Ghouti, T. Sheltami, and K. Alutaibi. 2013. Mobility prediction in mobile ad hoc networks using extreme learning machines. Procedia Computer Science 19 (2013), 305--312.

[9]

B. D. Higgins, J. Flinn, T. J. Giuli, B. Noble, C. Peplin, and D. Watson. 2012. Informed mobile prefetching. In Proceedings of the 10th International Conference on Mobile Systems, Applications, and Services (MobiSys'12). ACM, 155--168.

Digital Library

[10]

I.-Y. Ko, H.-G. Ko, A.-J. Molina, and J.-H. Kwon. 2016. SoIoT: Toward A user-centric IoT-based service framework. ACM Trans. Internet Technol. 16, 2 (2016), 8:1--8:21.

Digital Library

[11]

A. Konstantinidis, G. Chatzimilioudis, D. Zeinalipour-Yazti, P. Mpeis, N. Pelekis, and Y. Theodoridis. 2015. Privacy-preserving indoor localization on smartphones. IEEE Trans. Knowl. Data Eng. 27, 11 (2015), 3042--3055.

Digital Library

[12]

A. Konstantinidis, G. Nikolaides, G. Chatzimilioudis, G. Evagorou, D. Zeinalipour-Yazti, and P. K. Chrysanthis. 2015. Radiomap prefetching for indoor navigation in intermittently connected Wi-Fi networks. In Proceedings of the 16th IEEE International Conference on Mobile Data Management 1 (2015), 34--43.

Digital Library

[13]

G. Larkou, C. Costa, P. G. Andreou, A. Konstantinidis, and D. Zeinalipour-Yazti. 2013. Managing smartphone testbeds with smartlab. In Proceedings of the 27th USENIX Conference on Large Installation System Administration (LISA'13). USENIX Association, 115--132.

Digital Library

[14]

B. Li, J. Salter, A. G. Dempster, and C. Rizos. 2006. Indoor positioning techniques based on wireless lan. 1st International Conference on Wireless Broadband and Ultra Wideband Communications, 13--16.

[15]

S. Li, L.-D. Xu, and S. Zhao. 2015. The internet of things: A survey. Inf. Syst. Front. 17, 2 (2015), 243--259.

Digital Library

[16]

T. M. Lim, C. K. Yeo, F. Lee, and Q. V. Le. 2009. Tmsp: Terminal mobility support protocol. IEEE Trans. Mobile Comput. 8, 6 (2009), 849--863.

Digital Library

[17]

H. Lu, C. Guo, B. Yang, and C. S. Jensen. 2016. Finding frequently visited indoor POIs using symbolic indoor tracking data. In Proceedings of the 19th International Conference on Extending Database Technology (EDBT'16). 449--460.

[18]

Q. Lv, P. Cao, E. Cohen, K. Li, and S. Shenker. 2002. Search and replication in unstructured peer-to-peer networks. In Proceedings of the 16th International Conference on Supercomputing (ICS'02). ACM, 84--95.

Digital Library

[19]

D. Lymberopoulos, J. Liu, X. Yang, R. R. Choudhury, V. Handziski, and S. Sen. 2015. A realistic evaluation and comparison of indoor location technologies: Experiences and lessons learned. In Proceedings of the 14th International Conference on Information Processing in Sensor Networks (IPSN'15). ACM, 178--189.

Digital Library

[20]

S. Papastavrou, G. Samaras, P. Evripidou, and P. K. Chrysanthis. 2006. A decade of dynamic web content: A structured survey on past and present practices and future trends. IEEE Commun. Surv. Tutor. 8, 2 (2006), 52--60.

Digital Library

[21]

P. Prasithsangaree, P. Krishnamurthy, and P. Chrysanthis. 2002. On indoor position location with wireless LANs. In Proceedings of the 13th IEEE International Symposium on Personal, Indoor and Mobile Radio Communications 2 (2002), 720--724.

[22]

M. Satyanarayanan, J. J. Kistler, P. Kumar, M. E. Okasaki, E. H. Siegel, D. Steere, and C. Steere. 1990. Coda: A highly available file system for a distributed workstation environment. IEEE Trans. Comput. 39 (1990), 447--459.

Digital Library

[23]

E. Shriver, C. Small, and K. A. Smith. 1999. Why does file system prefetching work? In Proceedings of the Annual Conference on USENIX Annual Technical Conference (ATEC'99). USENIX Association, 6--6.

Digital Library

[24]

N. Thiagarajan, G. Aggarwal, A. Nicoara, D. Boneh, and J. P. Singh. 2012. Who killed my battery?: Analyzing mobile browser energy consumption. In Proceedings of the 21st International Conference on World Wide Web (WWW'12). ACM, 41--50.

Digital Library

[25]

O. Trullols-Cruces, M. Fiore, and J. Barcelo-Ordinas. 2012. Cooperative download in vehicular environments. IEEE Trans. Mobile Comput. 11, 4 (2012), 663--678.

Digital Library

[26]

M. Vögler, J. Schleicher, C. Inzinger, and S. Dustdar. 2016. A scalable framework for provisioning large-scale IoT deployments. ACM Trans. Internet Technol. 16, 2 (2016).

Digital Library

[27]

Y. Xia and C. K. Yeo. 2014. Mobile internet access over intermittent network connectivity. J. Netw. Comput. Appl. 40 (2014), 126--138.

Digital Library

[28]

J. Xiao, Z. Zhou, Y. Yi, and L. M. Ni. 2016. A survey on wireless indoor localization from the device perspective. ACM Comput. Surv. 49, 2 (2016), 25:1--25:31.

Digital Library

[29]

L.-D. Xu. 2011. Enterprise systems: State-of-the-art and future trends. IEEE Trans. Indu. Inf. 7, 4 (2011), 1551--3203.

[30]

L. Yao, Q. Z. Sheng, and S. Dustdar. 2015. Web-based management of the internet of things. IEEE Internet Comput. 19, 4 (2015), 60--67.

Digital Library

[31]

D. Zeinalipour-Yazti, and C. Laoudias. 2017. The anatomy of the anyplace indoor navigation service. In ACM SIGSPATIAL Special, Vol. 9, ACM Press, 3--10.

Digital Library

[32]

D. Zeinalipour-Yazti, C. Laoudias, K. Georgiou, and G. Chatzimiloudis. 2017. Internet-based indoor navigation services. IEEE Internet Comput. 21, 4 (2017), 54--63.

Digital Library

[33]

Z. Zhang. 2006. Routing in intermittently connected mobile ad hoc networks and delay tolerant networks: overview and challenges. IEEE Commun. Surv. Tutor. 8, 1 (2006), 24--37.

Digital Library

[34]

Z. Zheng, P. Sinha, and S. Kumar. 2009. Alpha coverage: Bounding the interconnection gap for vehicular internet access. In Proceedings of the IEEE INFOCOM. 2831--2835.

Cited By

Symvoulidis CMarinos GKiourtis AMavrogiorgou AKyriazis D(2022)HealthFetch: An Influence-Based, Context-Aware Prefetch Scheme in Citizen-Centered Health Storage CloudsFuture Internet10.3390/fi1404011214:4(112)Online publication date: 1-Apr-2022
https://doi.org/10.3390/fi14040112
Constantinou SKonstantinidis AChrysanthis PZeinalipour-Yazti D(2022)Green Planning of IoT Home Automation Workflows in Smart BuildingsACM Transactions on Internet of Things10.1145/35495493:4(1-30)Online publication date: 6-Sep-2022
https://dl.acm.org/doi/10.1145/3549549
Laoudias CNikitin AKarras PYoussef MZeinalipour-Yazti D(2021)Indoor Quality-of-position Visual Assessment Using Crowdsourced Fingerprint MapsACM Transactions on Spatial Algorithms and Systems10.1145/34330267:2(1-32)Online publication date: 30-Jan-2021
https://dl.acm.org/doi/10.1145/3433026
Show More Cited By

Index Terms

IoT Data Prefetching in Indoor Navigation SOAs
1. Human-centered computing
  1. Ubiquitous and mobile computing
    1. Ubiquitous and mobile computing systems and tools
2. Information systems
  1. Data management systems
  2. Information systems applications
    1. Mobile information processing systems

Recommendations

Seamless outdoor/indoor navigation with WIFI/GPS aided low cost Inertial Navigation System

This paper describes an integrated navigation system that can be used for pedestrian navigation in both outdoor and indoor environments. With the aid of Global Positioning System (GPS) positioning solutions, an Inertial Navigation System (INS) can ...
An indoor navigation platform for seeking Internet of Things devices in large indoor environment
GoodTechs '19: Proceedings of the 5th EAI International Conference on Smart Objects and Technologies for Social Good

With the proliferation of mobile Internet of Things (IoT) devices, finding those devices for maintenance or for a reattribution can be a long and difficult process, particularly in large institutions such as hospitals with hundreds of devices. With the ...
Indoor Tracking and Navigation Using Received Signal Strength and Compressive Sensing on a Mobile Device

An indoor tracking and navigation system based on measurements of received signal strength (RSS) in wireless local area network (WLAN) is proposed. In the system, the location determination problem is solved by first applying a proximity constraint to ...

Comments

Information & Contributors

Information

Published In

cover image ACM Transactions on Internet Technology

ACM Transactions on Internet Technology Volume 19, Issue 1

Regular Papers, Special Issue on Service Management for IOT and Special Issue on Knowledge-Driven BPM

February 2019

321 pages

ISSN:1533-5399

EISSN:1557-6051

DOI:10.1145/3283809

Editor:
Ling Liu
Georgia Institute of Technology, USA

Issue’s Table of Contents

Copyright © 2018 ACM.

Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

Publisher

Association for Computing Machinery

New York, NY, United States

Publication History

Published: 30 November 2018

Accepted: 01 December 2017

Revised: 01 November 2017

Received: 01 May 2017

Published in TOIT Volume 19, Issue 1

Permissions

Request permissions for this article.

Request Permissions

Check for updates

Author Tags

Qualifiers

Research-article
Research
Refereed

Contributors

Other Metrics

View Article Metrics

Bibliometrics & Citations

Bibliometrics

Article Metrics

9
Total Citations
View Citations
284
Total Downloads

Downloads (Last 12 months)10
Downloads (Last 6 weeks)0

Reflects downloads up to 08 Mar 2025

Other Metrics

View Author Metrics

Citations

Cited By

Symvoulidis CMarinos GKiourtis AMavrogiorgou AKyriazis D(2022)HealthFetch: An Influence-Based, Context-Aware Prefetch Scheme in Citizen-Centered Health Storage CloudsFuture Internet10.3390/fi1404011214:4(112)Online publication date: 1-Apr-2022
https://doi.org/10.3390/fi14040112
Constantinou SKonstantinidis AChrysanthis PZeinalipour-Yazti D(2022)Green Planning of IoT Home Automation Workflows in Smart BuildingsACM Transactions on Internet of Things10.1145/35495493:4(1-30)Online publication date: 6-Sep-2022
https://dl.acm.org/doi/10.1145/3549549
Laoudias CNikitin AKarras PYoussef MZeinalipour-Yazti D(2021)Indoor Quality-of-position Visual Assessment Using Crowdsourced Fingerprint MapsACM Transactions on Spatial Algorithms and Systems10.1145/34330267:2(1-32)Online publication date: 30-Jan-2021
https://dl.acm.org/doi/10.1145/3433026
Constantinou SKonstantinidis AZeinalipour-Yazti DChrysanthis P(2021)The IoT Meta-Control Firewall2021 IEEE 37th International Conference on Data Engineering (ICDE)10.1109/ICDE51399.2021.00284(2523-2534)Online publication date: Apr-2021
https://doi.org/10.1109/ICDE51399.2021.00284
Mpeis PRoussel TKumar MCosta CLaoudiasDenis CCapot-Ray DZeinalipour-Yazti D(2020)The Anyplace 4.0 IoT Localization Architecture2020 21st IEEE International Conference on Mobile Data Management (MDM)10.1109/MDM48529.2020.00045(218-225)Online publication date: Jun-2020
https://doi.org/10.1109/MDM48529.2020.00045
Alexandrou RPapadopoulos HKonstantinidis A(2020)Smartphone Indoor Localization Using Bio-inspired ModelingNature-Inspired Computation in Navigation and Routing Problems10.1007/978-981-15-1842-3_7(149-167)Online publication date: 20-Feb-2020
https://doi.org/10.1007/978-981-15-1842-3_7
Konstantinidis ADemetriades APericleous SHung CPapadopoulos G(2019)A multi-objective indoor localization service for smartphonesProceedings of the 34th ACM/SIGAPP Symposium on Applied Computing10.1145/3297280.3297395(1174-1181)Online publication date: 8-Apr-2019
https://dl.acm.org/doi/10.1145/3297280.3297395
Ramdani NPanayides AKaramousadakis MMellado MLopez RChristophorou CRebiai MBlouin MVellidou EKoutsouris D(2019)A Safe, Efficient and Integrated Indoor Robotic Fleet for Logistic Applications in Healthcare and Commercial Spaces: The ENDORSE Concept2019 20th IEEE International Conference on Mobile Data Management (MDM)10.1109/MDM.2019.000-8(425-430)Online publication date: Jun-2019
https://doi.org/10.1109/MDM.2019.000-8
Furfari FCrivello ABarsocchi PPalumbo FPotorti F(2019)What is next for Indoor Localisation? Taxonomy, protocols, and patterns for advanced Location Based Services2019 International Conference on Indoor Positioning and Indoor Navigation (IPIN)10.1109/IPIN.2019.8911759(1-8)Online publication date: Sep-2019
https://doi.org/10.1109/IPIN.2019.8911759

View Options

Login options

Check if you have access through your login credentials or your institution to get full access on this article.

Full Access

Get this Article

View options

PDF

View or Download as a PDF file.

eReader

View online with eReader.

Figures

Tables

Media

View Issue’s Table of Contents