skip to main content
10.1145/2405688.2405691acmotherconferencesArticle/Chapter ViewAbstractPublication PagesmiddlewareConference Proceedingsconference-collections
research-article

A communication-optimizing middleware for efficient wireless communication in rural environments

Published: 03 December 2012 Publication History

Abstract

The realization of wireless communication in rural environments suffers from long distances, unknown node mobility and discontinuous communication paths. Combination of delay-tolerant mobile ad-hoc networks and infrastructure-based mobile communications results in an increased number of communication opportunities in many usage scenarios. However, efficient exploitation of communication opportunities remains a challenging task.
This article introduces a novel middleware approach specialized for consistent and efficient wireless communication in rural environments. An important part of communication optimization is realized by processing on-hand information of the current application scenario. The middleware autonomously links scenario information to network states and applies machine-learning-based analyses to derive meaningful communication predictions. Cooperation of infrastructure-based and ad-hoc communication is implemented by a gateway component. The gateway collects connectivity information of mobile nodes, enhances communication predictions and distributes predictions across the network. Evaluation of local communication layer and application layer information as well as sharing of communication predictions using an application level API ensure efficient use of communication opportunities.

References

[1]
Delay Tolerant Networks Research Group; http://www.dtnrg.org.
[2]
L. Atzori, A. Iera and G. Morabito. The Internet of Things: A Survey. Computer Networks, 54(15): 2787--2805, 2010.
[3]
S. Basagni, M. Conti, S. Giordano and I. Stojmenović. Mobile Ad Hoc Networking. Wiley, 2004.
[4]
L. Bergmans and M. Aksit. Composing Crosscutting Concerns using composition filters. Communications of the ACM, 44(10):51--57, Oct. 2001.
[5]
A. Boukerche. Algorithms and Protocols for Wireless and Mobile Ad Hoc Networks. Wiley, 2009.
[6]
J. Burgess, B. Gallagher, D. Jensen and B. Levine. Maxprop: Routing for vehicle-based disruption-tolerant Networks. In Proc. of th 25th IEEE Infocom, volume 6, pages 1--11. Citeseer, 2006.
[7]
I. Chakeres and C. Perkins. Dynamic MANET on-demand Routing (DYMO). IETF Draft, 2008.
[8]
M. Chaqfeh and N. Mohamed. Challenges in Middleware Solutions for the Internet of Things. In 2012 International Conference on Collaboration Technologies and Systems (CTS), pages 21--26. IEEE, 2012.
[9]
S. Chetan, J. Al-Muhtadi, R. Campbell and M. Mickunas. Mobile Gaia: A Middleware for ad-hoc pervasive computing. In Proc. of the 2nd IEEE Consumer Communications and Networking Conference (CCNC '05), pages 223--228. IEEE, 2005.
[10]
T. Clausen and P. Jacquet. Optimized Link State Routing Protocol (OLSR). IETF RFC 3626, 2003.
[11]
K. Fall. A delay-tolerant Network Architecture for challenged Internets. In Proc. of the 2003 Conference on Applications, Technologies, Architectures and Protocols for Computer Communications, pages 27--34. ACM, 2003.
[12]
K. Fall, W. Hong and S. Madden. Custody Transfer for reliable delivery in Delay Tolerant Networks. Technical report, IRB-TR-03-030, 2003.
[13]
K. Fall, K. Scott, S. Burleigh, L. Torgerson et al. Delay-tolerant Networking Architecture. IETF RFC 4838, 2007.
[14]
S. Hachem, T. Teixeira and V. Issarny. Ontologies for the Internet of Things. In Proc. of the 8th Middleware Doctoral Symposium, page 3. ACM, 2011.
[15]
S. Hadim and N. Mohamed. Middleware Challenges and Approaches for Wireless Sensor Networks. Distributed Systems Online, IEEE, 7(3):1--1, 2006.
[16]
M. Hall, E. Frank, G. Holmes, B. Pfahringer et al. The Weka Data Mining Software: An Update. ACM SIGKDD Explorations Newsletter, 11(1):10--18, 2009.
[17]
D. Johnson and D. Maltz. Dynamic Source Routing in Ad Hoc Wireless Networks. Mobile Computing, pages 153--181, 1996.
[18]
P. Levis, S. Madden, J. Polastre, R. Szewczyk et al. TinyOS: An Operating System for Sensor Networks. Ambient Intelligence, 35, 2005.
[19]
A. Lindgren, A. Doria and O. Schelen. Probabilistic Routing in intermittently connected Networks. Service Assurance with Partial and Intermittent Resources, pages 239--254, 2004.
[20]
C. Medaglia and A. Serbanati. An Overview of privacy and security issues in the Internet of Things. The Internet of Things, pages 389--395, 2010.
[21]
S. Misra, I. Woungang and S. Misra. Guide to Wireless Ad Hoc Networks. Springer, 2009.
[22]
L. Mottola and G. Picco. Middleware for Wireless Sensor Networks: An Outlook. Journal of Internet Services and Applications, pages 1--9, 2011.
[23]
A. Neumann, C. Aichele, M. Lindner and S. Wunderlich. Better Approach to Mobile Ad-Hoc Networking (B.A.T.M.A.N.). IETF Internet-Draft, 2008.
[24]
C. Perkins and P. Bhagwat. Highly dynamic destination-sequenced distance-vector Routing (DSDV) for mobile Computers. ACM SIGCOMM Computer Communication Review, 24(4):234--244, 1994.
[25]
C. Perkins, E. Royer and S. Das. Ad hoc on-demand distance vector Routing (AODV). IETF RFC 3561, 2003.
[26]
A. Petz, A. Bednarczyk, N. Paine, D. Stovall et al. Madman: A Middleware for delay-tolerant Mobile Ad-Hoc Networks. Technical report, TR-UTEDGE-2010-010, 2010.
[27]
J. R. Quinlan. C4. 5: Programs for Machine Learning. Morgan Kaufmann, 1993.
[28]
K. A. Saleh. Software Engineering. J. Ross Publishing, 2009.
[29]
K. Scott and S. Burleigh. Bundle Protocol Specification. IETF RFC 5050, 2007.
[30]
T. Spyropoulos, K. Psounis and C. Raghavendra. Spray and Wait: An efficient routing scheme for intermittently connected mobile Networks. In Proc. of the 2005 ACM SIGCOMM Workshop on delay-tolerant Networking, pages 252--259. ACM, 2005.
[31]
A. Vahdat, D. Becker, et al. Epidemic Routing for partially connected Ad Hoc Networks. Technical report, CS-200006, Duke University, 2000.
[32]
A. Vasilakos, Y. Zhang and T. Spyropoulos. Delay Tolerant Networks: Protocols and Applications. Wireless Networks and Mobile Communications Series. Taylor & Francis, 2011.
[33]
Z. Zhang. Routing in intermittently connected Mobile Ad Hoc Networks and Delay Tolerant Networks: Overview and Challenges. IEEE Communications Surveys & Tutorials, 8(1):24--37, 2006.
[34]
H. Zhuang, H. Ntareme, Z. Ou and B. Pehrson. A Service Adaptation Middleware for Delay Tolerant Networks based on HTTP Simple Queue Service. In Proc. of the 6th Workshop on Networked Systems for Developing Regions (NSDR '12). ACM, 2012.

Cited By

View all
  • (2018)Evaluating Utilization of Cloud Computing for IoT Big Data SystemsInternational Journal of Distributed Artificial Intelligence10.4018/IJDAI.201801010310:1(34-42)Online publication date: 1-Jan-2018
  • (2018)HOAR: A hybrid-opportunistic architecture for robust agricultural networking2018 IoT Vertical and Topical Summit on Agriculture - Tuscany (IOT Tuscany)10.1109/IOT-TUSCANY.2018.8373020(1-6)Online publication date: May-2018
  • (2017)Measuring and Adapting MQTT in Cellular Networks for Collaborative Smart Farming2017 IEEE 42nd Conference on Local Computer Networks (LCN)10.1109/LCN.2017.81(294-302)Online publication date: Oct-2017
  • Show More Cited By

Index Terms

  1. A communication-optimizing middleware for efficient wireless communication in rural environments

      Recommendations

      Comments

      Information & Contributors

      Information

      Published In

      cover image ACM Other conferences
      MIDDLEWARE '12: Proceedings of the 9th Middleware Doctoral Symposium of the 13th ACM/IFIP/USENIX International Middleware Conference
      December 2012
      52 pages
      ISBN:9781450316118
      DOI:10.1145/2405688
      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]

      Sponsors

      • Professional
      • USENIX Assoc: USENIX Assoc
      • IFIP

      Publisher

      Association for Computing Machinery

      New York, NY, United States

      Publication History

      Published: 03 December 2012

      Permissions

      Request permissions for this article.

      Check for updates

      Author Tags

      1. DTNs
      2. MANETs
      3. cellular networks
      4. communication predictions
      5. communication-optimizing middleware
      6. machine learning
      7. rural environments

      Qualifiers

      • Research-article

      Conference

      Middleware '12
      Sponsor:
      • USENIX Assoc
      Middleware '12: 13th International Middleware Conference
      December 3, 2012
      Quebec, Montreal, Canada

      Acceptance Rates

      Overall Acceptance Rate 203 of 948 submissions, 21%

      Contributors

      Other Metrics

      Bibliometrics & Citations

      Bibliometrics

      Article Metrics

      • Downloads (Last 12 months)3
      • Downloads (Last 6 weeks)0
      Reflects downloads up to 25 Feb 2025

      Other Metrics

      Citations

      Cited By

      View all
      • (2018)Evaluating Utilization of Cloud Computing for IoT Big Data SystemsInternational Journal of Distributed Artificial Intelligence10.4018/IJDAI.201801010310:1(34-42)Online publication date: 1-Jan-2018
      • (2018)HOAR: A hybrid-opportunistic architecture for robust agricultural networking2018 IoT Vertical and Topical Summit on Agriculture - Tuscany (IOT Tuscany)10.1109/IOT-TUSCANY.2018.8373020(1-6)Online publication date: May-2018
      • (2017)Measuring and Adapting MQTT in Cellular Networks for Collaborative Smart Farming2017 IEEE 42nd Conference on Local Computer Networks (LCN)10.1109/LCN.2017.81(294-302)Online publication date: Oct-2017
      • (2016)IoT-based Big Data Storage Systems in Cloud Computing: Perspectives and ChallengesIEEE Internet of Things Journal10.1109/JIOT.2016.2619369(1-1)Online publication date: 2016

      View Options

      Login options

      View options

      PDF

      View or Download as a PDF file.

      PDF

      eReader

      View online with eReader.

      eReader

      Figures

      Tables

      Media

      Share

      Share

      Share this Publication link

      Share on social media