Article

People-centric urban sensing

Authors:

Andrew T. Campbell,

Shane B. Eisenman,

Nicholas D. Lane,

Emiliano Miluzzo,

Ronald A. PetersonAuthors Info & Claims

WICON '06: Proceedings of the 2nd annual international workshop on Wireless internet

Pages 18 - es

https://doi.org/10.1145/1234161.1234179

Published: 02 August 2006 Publication History

Abstract

The vast majority of advances in sensor network research over the last five years have focused on the development of a series of small-scale (100s of nodes) testbeds and specialized applications (e.g., environmental monitoring, etc.) that are built on low-powered sensor devices that self-organize to form application-specific multihop wireless networks. We believe that sensor networks have reached an important crossroads in their development. The question we address in this paper is how to propel sensor networks from their small-scale application-specific network origins, into the commercial mainstream of people's every day lives; the challenge being: how do we develop large-scale general-purpose sensor networks for the general public (e.g., consumers) capable of supporting a wide variety of applications in urban settings (e.g., enterprises, hospitals, recreational areas, towns, cities, and the metropolis). We propose MetroSense, a new people-centric paradigm for urban sensing at the edge of the Internet, at very large scale. We discuss a number of challenges, interactions and characteristics in urban sensing applications, and then present the MetroSense architecture which is based fundamentally on three design principles: network symbiosis, asymmetric design, and localized interaction. The ability of MetroSense to scale to very large areas is based on the use of an opportunistic sensor networking approach. Opportunistic sensor networking leverages mobility-enabled interactions and provides coordination between people-centric mobile sensors, static sensors and edge wireless access nodes in support of opportunistic sensing, opportunistic tasking, and opportunistic data collection. We discuss architectural challenges including providing sensing coverage with sparse mobile sensors, how to hand off roles and responsibilities between sensors, improving network performance and connectivity using adaptive multihop, and importantly, providing security and privacy for people-centric sensors and data.

References

[1]

Ubisense. http://www.ubisense.net.

[2]

A. Arora and et al. Exscal: Elements of an extreme scale wireless sensor network. In 11th IEEE International Conference on Embedded and Real-Time Computing Systems and Applications (RTCSA), 2005.

Digital Library

[3]

R. Govindan and et al. Tenet: An architecture for tiered embedded networks. In CENS Technical Report No. 53, Nov. 2005.

[4]

C.-Y. Wan, S. B. Eisenman, A. T. Campbell, and J. Crowcroft. Siphon: overload traffic management using multi-radio virtual sinks in sensor networks. In SenSys '05: Proc. of the 3rd int. conference on Embedded networked sensor systems, pages 116--129, San Diego, California, USA, 2005

Digital Library

[5]

C.-Y. Wan, A. T. Campbell, and L. Krishnamurthy. Pump slowly, fetch quickly (psfq): a reliable transport protocol for sensor networks. In IEEE Journal on Selected Areas in Communications, volume 23, pages 862--872, April 2005.

Digital Library

[6]

B. Hull, K. Jamieson, and H. Balakrishnan. Mitigating congestion in wireless sensor networks. In SenSys '04: Proc. of the 2nd int. conf. on Embedded networked sensor systems, pages 134--147, Baltimore, MD, USA, 2004.

Digital Library

[7]

Moteiv tmote invent: http://www.moteiv.com/products-tmoteinvent.php.

[8]

Sensorplanet. http://www.sensorplanet.org/.

[9]

D. Malan, T. Fulford-Jones, M. Wesh, and S. Moulton. Codeblue: An ad hoc sensor network infrastructure for emergency medical care. In Proc. MobiSys 2004 Workshop on Applications of Mobile Embedded Systems, 2004.

[10]

L. Krishnamurthy and et al. Design and deployment of industrial sensor networks: experiences from a semiconductor plant and the north sea. In SenSys '05: Proc. of the 3rd Intl. Conf. on Embedded Networked Sensor Systems, pages 64--75, San Diego, California, USA, 2005.

Digital Library

[11]

Victoria Transport Policy Institute. London congestion pricing. www.vtpi.org.

[12]

N. Xu, S. Rangwala, K. K. Chintalapudi, D. Ganesan, A. Broad, R. Govindan, and D. Estrin. A wireless sensor network for structural monitoring. In SenSys '04: Proc. of the 2nd int. conf. on Embedded networked sensor systems, pages 13--24, Baltimore, MD, USA, 2004.

Digital Library

[13]

Tune your run. http://www.apple.com/ipod/nike/.

[14]

M. Feldmeier and J. Paradiso. Giveaway wireless sensors for large-group interaction. In CHI '04: CHI '04 extended abstracts on Human factors in computing systems, pages 1291--1292, Vienna, Austria, 2004.

Digital Library

[15]

G. Bell and J. Gray. Digital immortality. In Communications of the ACM, volume 44(3), pages 28--31, 2001.

Digital Library

[16]

K. Fall. A delay tolerant network architecture for challenged internets. In Proc. ACM SIGCOMM, pages 27--34, Karlsruhe, Germany, Aug. 2003.

Digital Library

[17]

Ubifit project. http://dub.washington.edu/projects/ubifit/.

[18]

Virone and et al. An assisted living oriented information system based on a residential wireless sensor network. In Proc. of 1st Conf. on Distributed Diagnosis and Home Healthcare, pages 95--100, April 2--4 2006.

[19]

T. Ishida. Digital city kyoto. Commun. ACM, 45(7):76--81, 2002.

Digital Library

[20]

Silent london -- a map of the capital's quietest places. http://www.simonelvins.com/silent_london.html.

[21]

J. Polastre, J. Hui, P. Levis, J. Zhao, D. Culler, S. Shenker, and I. Stoica. A unifying link abstraction for wireless sensor networks. In SenSys '05: Proc. of the 3rd int. conf. on Embedded networked sensor systems, pages 76--89, San Diego, California, USA, 2005.

Digital Library

[22]

P. Levis and D. Culler. Mate: A tiny virtual machine for sensor networks. In Int. Conf. on Architectural Support for Programming Languages and Operating Systems, San Jose, CA, USA, Oct. 2002.

Digital Library

[23]

P. Levis, D. Gay, and D. Culler. Active sensor networks. In Proc. of the 2nd USENIX/ACM Symposium on Network Systems Design and Implementation (NSDI), May 2005.

Digital Library

[24]

D. Kotz and K. Essien. Analysis of a campus-wide wireless network. In MobiCom '02: Proceedings of the 8th annual international conference on Mobile computing and networking, pages 107--118, Atlanta, Georgia, USA, 2002.

Digital Library

[25]

Liu and et al. Mobility improves coverage of sensor networks. In Proc. of the 6th ACM Int'l Symp. on Mobile Ad Hoc Networking and Computing, pages 300--308, May 25--27 2005.

Digital Library

[26]

Openwrt. http://openwrt.org/.

[27]

A. Chaintreau, P. Hui, J. Crowcroft, C. Diot, R. Gass, and J. Scott. Impact of human mobility on the design of opportunistic forwarding algorithms. In Proc. of INFOCOM 2006, April 23--29, Barcelona, Spain.

[28]

E. Shi and A. Perrig. Designing secure sensor networks. Wireless Communication Magazine, 11(6):38--43, December 2004.

Digital Library

[29]

C. Karlof, N. Sastry, and D. Wagner. Tinysec: a link layer security architecture for wireless sensor networks. In SenSys '04: Proc. of the 2nd int. conf. on Embedded networked sensor systems, pages 162--175, Baltimore, MD, USA, 2004.

Digital Library

[30]

T. Stathopoulos, L. Girod, J. Heidemann, and D. Estrin. Mote herding for tiered wireless sensor networks. In CENS Technical Report 58, December 7, 2005.

[31]

M. Ho and K. Fall. Poster: Delay tolerant networking for sensor networks. In Proc. of IEEE Conference on Sensor and Ad Hoc Communications and Networks, 2004.

[32]

Y. Wang and H. Wu. Dft-msn: The delay fault tolerant mobile sensor network for pervasive information gathering. In Proc. of INFOCOM 2006, April 23--29, Barcelona, Spain.

[33]

A. Kansal, A. A. Somasundara, D. D. Jea, M. B. Srivastava, and D. Estrin. Intelligent fluid infrastructure for embedded networks. In Proc. of MobiSys '04, pages 111--124, Boston, MA, USA, 2004.

Digital Library

[34]

S. Nath, Y. Ke, P. B. Gibbons, B. Karp, and S. Seshan. Irisnet: An architecture for enabling sensor-enriched internet service. In Technical Report, Intel Research, number IRP-TR-03-04, Pittsburgh, June 2003.

[35]

P. Pietzuch, J. Shneidman, J. Ledlie, M. Welsh, M. Seltzer, and M. Roussopoulos. Hourglass: An infrastructure for connecting sensor networks and applications. In "Harvard University Technical Report TR-21-04", 2004.

[36]

J. Liu and F. Zhao. Towards semantic services for sensor-rich information systems. In The 2nd IEEE/CreateNet International Workshop on Broadband Advanced Sensor Networks (Basenets 2005), Boston, MA, Oct. 3, 2005., pages 44--51.

[37]

M. Romn, C. K. Hess, R. Cerqueira, A. Ranganathan, R. H. Campbell, and K. Nahrstedt. Gaia: A Middleware Infrastructure to Enable Active Spaces. IEEE Pervasive Computing, pages 74--83, Oct-Dec 2002.

Digital Library

[38]

J. Sousa and D. Garlan. Aura: An architectural framework for user mobility in ubiquitous computing environments. In Proceedings of 3rd IEEE/IFIP Conference on Software Architecture, Montreal, 2002.

Digital Library

[39]

Endeavour, http://endeavour.cs.berkeley.edu/.

[40]

Oxygen. http://www.oxygen.lcs.mit.edu/.

[41]

Sentient computing. http://www.uk.research.att.com/spirit/.

[42]

R. Want, A. Hopper, V. Falcao, and J. Gibbons. The active badge location system. ACM Trans. Inf. Syst., 10(1):91--102, 1992.

Digital Library

[43]

The cricket location-support system. http://cricket.csail.mit.edu/.

[44]

Quentin Jones and Sukeshini A. Grandhi. P3 systems: Putting the place back into social networks. IEEE Internet Computing, 9(5):38--46, 2005.

Digital Library

[45]

G. Simon et al. Sensor network-based countersniper system. In Proc. of the 2nd int. conf. on Embedded networked sensor systems, pages 1--12, Baltimore, MD, USA, 2004.

Digital Library

[46]

Urban Sensing Summit, CENS, UCLA, May 2006. http://bigriver.remap.ucla.edu/remap/index.php/Urban_Sensing_Summit.

Cited By

Luo CHui LShang ZWang CJin MWang XLi N(2024)Portable Arduino-Based Multi-Sensor Device (SBEDAD): Measuring the Built Environment in Street Cycling SpacesSensors10.3390/s2410309624:10(3096)Online publication date: 13-May-2024
https://doi.org/10.3390/s24103096
Kraft RReichert MPryss R(2024)Mobile Crowdsensing in Ecological Momentary Assessment mHealth Studies: A Systematic Review and AnalysisSensors10.3390/s2402047224:2(472)Online publication date: 12-Jan-2024
https://doi.org/10.3390/s24020472
Jiang YRezazadeh Baee MSimpson LGauravaram PPieprzyk JZia TZhao ZLe Z(2024)Pervasive User Data Collection from Cyberspace: Privacy Concerns and CountermeasuresCryptography10.3390/cryptography80100058:1(5)Online publication date: 31-Jan-2024
https://doi.org/10.3390/cryptography8010005
Show More Cited By

Index Terms

People-centric urban sensing
1. Hardware
  1. Communication hardware, interfaces and storage
2. Networks

Recommendations

Energy-efficient opportunistic coverage for people-centric urban sensing

Human-carried or vehicle-mounted sensors can be exploited to collect data ubiquitously for urban sensing. In this work, we study a new coverage problem, opportunistic coverage, to characterize the sensing quality of such people-centric sensing systems. ...
Designing Large-Scale Wireless Sensor Networks for Urban Environmental Sensing
UbiComp/ISWC '23 Adjunct: Adjunct Proceedings of the 2023 ACM International Joint Conference on Pervasive and Ubiquitous Computing & the 2023 ACM International Symposium on Wearable Computing

Hardware and software advances have paved the way for large-scale environmental urban sensor networks, but there is no guidance on where to place a finite number of nodes and how to assess the network design. In this work, I develop and test two quality ...
Urban Resolution: New Metric for Measuring the Quality of Urban Sensing
The rising popularity of smartphones and vehicles equipped with onboard sensors sheds lights on building a city-scale sensing system for urban surveillance. This paper proposes a novel metric, urban resolution, to measure the quality of urban sensing. ...

Comments

Information & Contributors

Information

Published In

cover image ACM Other conferences

WICON '06: Proceedings of the 2nd annual international workshop on Wireless internet

August 2006

247 pages

ISBN:159593510X

DOI:10.1145/1234161

Copyright © 2006 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: 02 August 2006

Permissions

Request permissions for this article.

Request Permissions

Check for updates

Author Tags

Qualifiers

Article

Contributors

Other Metrics

View Article Metrics

Bibliometrics & Citations

Bibliometrics

Article Metrics

336
Total Citations
View Citations
2,453
Total Downloads

Downloads (Last 12 months)57
Downloads (Last 6 weeks)3

Reflects downloads up to 13 Jan 2025

Other Metrics

View Author Metrics

Citations

Cited By

Luo CHui LShang ZWang CJin MWang XLi N(2024)Portable Arduino-Based Multi-Sensor Device (SBEDAD): Measuring the Built Environment in Street Cycling SpacesSensors10.3390/s2410309624:10(3096)Online publication date: 13-May-2024
https://doi.org/10.3390/s24103096
Kraft RReichert MPryss R(2024)Mobile Crowdsensing in Ecological Momentary Assessment mHealth Studies: A Systematic Review and AnalysisSensors10.3390/s2402047224:2(472)Online publication date: 12-Jan-2024
https://doi.org/10.3390/s24020472
Jiang YRezazadeh Baee MSimpson LGauravaram PPieprzyk JZia TZhao ZLe Z(2024)Pervasive User Data Collection from Cyberspace: Privacy Concerns and CountermeasuresCryptography10.3390/cryptography80100058:1(5)Online publication date: 31-Jan-2024
https://doi.org/10.3390/cryptography8010005
Busygina T(2024)Libraries and Citizen Science Projects: Foreign PracticeBibliosphere10.20913/1815-3186-2024-2-100-108(100-108)Online publication date: 10-Jun-2024
https://doi.org/10.20913/1815-3186-2024-2-100-108
KANAI KKANEMITSU HYAMAZAKI TMORI SMINE AMIYATA SIMAMURA HNAKAZATO H(2024)D2EcoSys: Decentralized Digital Twin EcoSystem Empower Co-Creation City-Level Digital TwinsIEICE Transactions on Communications10.1587/transcom.2023WWI0001E107.B:1(50-62)Online publication date: 1-Jan-2024
https://doi.org/10.1587/transcom.2023WWI0001
Jin HZhang PDong HBouguettaya AZomaya A(2024)Swift and Accurate Mobility-Aware QoS Forecasting for Mobile Edge EnvironmentsIEEE Transactions on Services Computing10.1109/TSC.2024.3417339(1-14)Online publication date: 2024
https://doi.org/10.1109/TSC.2024.3417339
Wang JHao YHu LFortino GAlqahtani SChen M(2024)Urban Sensing of Virtual Internet of Things for MetaverseIEEE Sensors Journal10.1109/JSEN.2023.334742324:5(5675-5686)Online publication date: 1-Mar-2024
https://doi.org/10.1109/JSEN.2023.3347423
Wang STang SRuan SLong CLiang YLi QYuan ZBao JZheng Y(2024)Urban Sensing for Multi-Destination Workers via Deep Reinforcement Learning2024 IEEE 40th International Conference on Data Engineering (ICDE)10.1109/ICDE60146.2024.00318(4167-4179)Online publication date: 13-May-2024
https://doi.org/10.1109/ICDE60146.2024.00318
Li HWang HWang KQu TRen XMa J(2024)An energy efficient clustering algorithm based on density and fitness for mobile crowd-sensing networkJournal of Engineering Research10.1016/j.jer.2024.07.011Online publication date: Jul-2024
https://doi.org/10.1016/j.jer.2024.07.011
Kanai KMine AKanemitsu HNakazato H(2024)Self-expanding City-Level Digital Twin Using Blockchain and IPFSAdvances in Intelligent Networking and Collaborative Systems10.1007/978-3-031-72322-3_43(434-443)Online publication date: 15-Sep-2024
https://doi.org/10.1007/978-3-031-72322-3_43
Show More Cited By

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 Publication

View options

PDF

View or Download as a PDF file.

eReader

View online with eReader.

Media

Figures

Other

Tables

View Table of Contents