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Constraint Programming for Wireless Sensor Networks
Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Division of Computing Science. Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Computing Science. (ASTRA)
2015 (English)Doctoral thesis, comprehensive summary (Other academic)
Abstract [en]

In recent years, wireless sensor networks (WSNs) have grown rapidly and have had a substantial impact in many applications. A WSN is a network that consists of interconnected autonomous nodes that monitor physical and environmental conditions, such as temperature, humidity, pollution, etc. If required, nodes in a WSN can perform actions to affect the environment.

WSNs present an interesting and challenging field of research due to the distributed nature of the network and the limited resources of the nodes. It is necessary for a node in a WSN to be small to enable easy deployment in an environment and consume as little energy as possible to prolong its battery lifetime. There are many challenges in WSNs, such as programming a large number of nodes, designing communication protocols, achieving energy efficiency, respecting limited bandwidth, and operating with limited memory. WSNs are further constrained due to the deployment of the nodes in indoor and outdoor environments and obstacles in the environment.

In this dissertation, we study some of the fundamental optimisation problems related to the programming, coverage, mobility, data collection, and data loss of WSNs, modelled as standalone optimisation problems or as optimisation problems integrated with protocol design. Our proposed solution methods come from various fields of research including constraint programming, integer linear programming, heuristic-based algorithms, and data inference techniques.

Place, publisher, year, edition, pages
Uppsala: Acta Universitatis Upsaliensis, 2015. , 80 p.
Series
Digital Comprehensive Summaries of Uppsala Dissertations from the Faculty of Science and Technology, ISSN 1651-6214 ; 1219
Keyword [en]
Constraint programming, wireless sensor networks, optimisation, macroprogramming, task mapping
National Category
Computer Science
Research subject
Computer Science with specialization in Computer Communication
Identifiers
URN: urn:nbn:se:uu:diva-241378ISBN: 978-91-554-9144-4 (print)OAI: oai:DiVA.org:uu-241378DiVA: diva2:778823
Public defence
2015-03-13, Room 2446, Polacksbacken, Lägerhyddsvägen 2, Uppsala, 13:00 (English)
Opponent
Supervisors
Projects
ProFuN
Available from: 2015-02-06 Created: 2015-01-12 Last updated: 2015-03-19Bibliographically approved
List of papers
1. Energy-efficient task mapping for data-driven sensor network macroprogramming using constraint programming
Open this publication in new window or tab >>Energy-efficient task mapping for data-driven sensor network macroprogramming using constraint programming
2011 (English)In: Operations Research, Computing, and Homeland Defense, Hanover, MD: Institute for Operations Research and the Management Sciences , 2011, 199-209 p.Conference paper, Published paper (Refereed)
Place, publisher, year, edition, pages
Hanover, MD: Institute for Operations Research and the Management Sciences, 2011
National Category
Computer Science
Identifiers
urn:nbn:se:uu:diva-136365 (URN)10.1287/ics.2011.0016 (DOI)978-0-9843378-1-1 (ISBN)
Conference
12th INFORMS Computing Society Conference
Projects
ProFuN
Funder
Swedish Foundation for Strategic Research , RIT08-0065
Available from: 2011-01-11 Created: 2010-12-12 Last updated: 2015-03-09Bibliographically approved
2. An optimisation-based approach for wireless sensor deployment in mobile sensing environments
Open this publication in new window or tab >>An optimisation-based approach for wireless sensor deployment in mobile sensing environments
2012 (English)In: Proc. Wireless Communications and Networking Conference 2012, IEEE Communications Society, 2012, 2108-2112 p.Conference paper, Published paper (Refereed)
Place, publisher, year, edition, pages
IEEE Communications Society, 2012
National Category
Computer Science
Identifiers
urn:nbn:se:uu:diva-171536 (URN)10.1109/WCNC.2012.6214140 (DOI)000324580702038 ()978-1-4673-0436-8 (ISBN)
Conference
WCNC 2012
Projects
ProFuN
Funder
Swedish Foundation for Strategic Research , RIT08-0065
Available from: 2012-06-11 Created: 2012-03-20 Last updated: 2015-03-09Bibliographically approved
3. Optimising quality of information in data collection for mobile sensor networks
Open this publication in new window or tab >>Optimising quality of information in data collection for mobile sensor networks
2013 (English)In: Proc. 21st International Symposium on Quality of Service, IEEE Communications Society, 2013, 163-172 p.Conference paper, Published paper (Refereed)
Place, publisher, year, edition, pages
IEEE Communications Society, 2013
National Category
Computer Science
Identifiers
urn:nbn:se:uu:diva-208996 (URN)10.1109/IWQoS.2013.6550277 (DOI)000325614100019 ()978-1-4799-0589-8 (ISBN)
Conference
IEEE/ACM 21st International Symposium on Quality of Service (IWQoS), 3-4 June, 2013, Montreal, QC
Projects
ProFuN
Funder
Swedish Foundation for Strategic Research , RIT08-0065
Available from: 2013-10-13 Created: 2013-10-13 Last updated: 2015-03-09Bibliographically approved
4. A constraint programming approach for managing end-to-end requirements in sensor network macroprogramming
Open this publication in new window or tab >>A constraint programming approach for managing end-to-end requirements in sensor network macroprogramming
Show others...
2014 (English)In: Proc. 3rd International Conference on Sensor Networks / [ed] Postolache, Octavian; van Sinderen, Marten; Ali, Falah; Benavente-Peces, César, Setúbal, Portugal: SciTePress, 2014, 28-40 p.Conference paper, Published paper (Refereed)
Place, publisher, year, edition, pages
Setúbal, Portugal: SciTePress, 2014
National Category
Computer Science
Identifiers
urn:nbn:se:uu:diva-210431 (URN)10.5220/0004715200280040 (DOI)978-989-758-001-7 (ISBN)
Conference
SENSORNETS 2014
Projects
ProFuN
Funder
Swedish Foundation for Strategic Research , RIT08-0065
Available from: 2014-01-09 Created: 2013-11-08 Last updated: 2015-03-09Bibliographically approved
5. Energy-efficient sensor selection for data quality and load balancing in wireless sensor networks
Open this publication in new window or tab >>Energy-efficient sensor selection for data quality and load balancing in wireless sensor networks
2014 (English)In: Proc. 22nd International Symposium on Quality of Service, IEEE Communications Society, 2014, 338-343 p.Conference paper, Published paper (Refereed)
Place, publisher, year, edition, pages
IEEE Communications Society, 2014
National Category
Computer Science
Identifiers
urn:nbn:se:uu:diva-229594 (URN)10.1109/IWQoS.2014.6914338 (DOI)000355927000044 ()978-1-4799-4852-9 (ISBN)
Conference
IWQoS 2014, May 26–27, Hong Kong, China
Projects
ProFuN
Funder
Swedish Foundation for Strategic Research , RIT08-0065
Available from: 2014-05-27 Created: 2014-08-11 Last updated: 2015-08-26Bibliographically approved
6. Cloud-Assisted Data Fusion and Sensor Selection for Internet-of-Things
Open this publication in new window or tab >>Cloud-Assisted Data Fusion and Sensor Selection for Internet-of-Things
(English)Manuscript (preprint) (Other academic)
Abstract [en]

The Internet of Things (IoT) is connecting people and smart devices on a scale that once was unimaginable. One major challenge for the IoT is to handle vast amount of sensing data generated from the smart devices that are resource-limited and subject to missing data due to link or node failures. By exploring cloud computing with the IoT, we present a cloud-based solution that takes into account the link quality and spatio-temporal correlation of data to minimise energy consumption by selecting sensors for sampling and relaying data. We propose a multi-phase adaptive sensing algorithm with belief propagation protocol (ASBP), which can provide high data quality and reduce energy consumption by turning on only a small number of nodes in the network. We formulate the sensor selection problem and solve it using constraint programming (CP) and greedy search. We then use our message passing algorithm (belief propagation) for performing inference to reconstruct the missing sensing data. ASBP is evaluated based on the data collected from real sensors. The results show that while maintaining a satisfactory level of data quality and prediction accuracy, ASBP can provide load balancing among sensors successfully and preserves 80\% more energy compared with the case where all sensor nodes are actively involved.

National Category
Computer Science
Research subject
Computer Science
Identifiers
urn:nbn:se:uu:diva-241377 (URN)
Projects
ProFuN
Available from: 2015-01-12 Created: 2015-01-12 Last updated: 2015-03-09

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Hassani Bijarbooneh, Farshid

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