Change search
ReferencesLink to record
Permanent link

Direct link
Energy Efficient Sensor Activation for Water Distribution Networks Based on Compressive Sensing
KTH, School of Electrical Engineering (EES), Automatic Control.ORCID iD: 0000-0002-1934-9208
KTH, School of Electrical Engineering (EES), Automatic Control.
KTH, School of Electrical Engineering (EES), Automatic Control.ORCID iD: 0000-0001-9810-3478
KTH, School of Electrical Engineering (EES), Communication Theory.ORCID iD: 0000-0002-5407-0835
2015 (English)In: IEEE Journal on Selected Areas in Communications, ISSN 0733-8716, E-ISSN 1558-0008, Vol. 33, no 12, 2997-3010 p.Article in journal (Refereed) Published
Abstract [en]

The recent development of low cost wireless sensors enables novel internet-of-things (IoT) applications, such as the monitoring of water distribution networks. In such scenarios, the lifetime of the wireless sensor network (WSN) is a major concern, given that sensor node replacement is generally inconvenient and costly. In this paper, a compressive sensing-based scheduling scheme is proposed that conserves energy by activating only a small subset of sensor nodes in each timeslot to sense and transmit. Compressive sensing introduces a cardinality constraint that makes the scheduling optimization problem particularly challenging. Taking advantage of the network topology imposed by the IoT water monitoring scenario, the scheduling problem is decomposed into simpler subproblems, and a dynamic-programming-based solution method is proposed. Based on the proposed method, a solution algorithm is derived, whose complexity and energy-wise performance are investigated. The complexity of the proposed algorithm is characterized and its performance is evaluated numerically via an IoT emulator of water distribution networks. The analytical and numerical results show that the proposed algorithm outperforms state-of-the-art approaches in terms of energy consumption, network lifetime, and robustness to sensor node failures. It is argued that the derived solution approach is general and it can be potentially applied to more IoT scenarios such as WSN scheduling in smart cities and intelligent transport systems.

Place, publisher, year, edition, pages
IEEE , 2015. Vol. 33, no 12, 2997-3010 p.
National Category
Communication Systems
URN: urn:nbn:se:kth:diva-178154DOI: 10.1109/JSAC.2015.2481199ISI: 000365223600040ScopusID: 2-s2.0-84960131576OAI: diva2:877656

QC 20151215

Available from: 2015-12-07 Created: 2015-12-07 Last updated: 2016-04-20Bibliographically approved
In thesis
1. Wireless Sensor Networks in Smart Cities: The Monitoring of Water Distribution Networks Case
Open this publication in new window or tab >>Wireless Sensor Networks in Smart Cities: The Monitoring of Water Distribution Networks Case
2016 (English)Licentiate thesis, comprehensive summary (Other academic)
Abstract [en]

The development of wireless sensor networks (WSNs) is making it possible to monitor our cities. Due to the small size of the sensor nodes, and their capabilities of transmitting data remotely, they can be deployed at locations that are not easy or impossible to access, such as the pipelines of water distribution networks (WDNs), which plays an important role in protecting environment and securing public health.

  The design of WSNs for WDNs faces major challenges. Generally, WSNs are resource-limited because most of the sensor nodes are battery powered. Thus, their resource allocation has to be carefully controlled. The thesis considers two prominent problems that occur when designing WSNs for WDNs: scheduling the sensing of the nodes of static WSNs, and sensor placement for mobile WSNs. These studies are reported in the thesis from three published or submitted papers. In the first paper, the scheduling of sleep/sensing for each sensor node is considered to maximize the whole WSNs lifetime while guaranteeing a monitoring performance constraint. The problem is transformed into an energy balancing problem, and solved by a dynamic programming based algorithm. It is proved that this algorithm finds one of the optimal solutions for the energy balancing problem. In the second paper, the question of how the energy balancing problem approximates the original scheduling problem is addressed. It is shown that even though these two problems are not equivalent, the gap of them is small enough. Thus, the proposed algorithm for the energy balancing problem can find a good approximation solution for the original scheduling problem. The second part of the thesis considers the use of mobile sensor nodes. Here, the limited resource is the number of available such mobile nodes. To maximize the monitoring coverage in terms of population, an optimization problem for determining the releasing locations for the mobile sensor nodes is formulated. An approximate solution algorithm based on submodular maximization is proposed and its performance is investigated. Beside WDNs, WSN applications for smart cities share a common characteristic: the area to monitor usually has a network structure. Therefore, the studies of this thesis can be potentially generalized for several IoT scenarios.

Place, publisher, year, edition, pages
KTH Royal Institute of Technology, 2016. 21 p.
TRITA-EE, ISSN 1653-5146 ; 2016:60
Integer Programming, Nonconvex Optimization, Network Lifetime, Dynamic
National Category
Electrical Engineering, Electronic Engineering, Information Engineering
Research subject
Electrical Engineering
urn:nbn:se:kth:diva-185453 (URN)978-91-7595-964-1 (ISBN)
2016-05-10, Q2, OSQULDAS VÄG 10, Stockholm, 10:00 (English)

QC 20160419

Available from: 2016-04-20 Created: 2016-04-19 Last updated: 2016-04-20Bibliographically approved

Open Access in DiVA

No full text

Other links

Publisher's full textScopus

Search in DiVA

By author/editor
Du, RongGkatzikis, LazarosFischione, CarloXiag, Ming
By organisation
Automatic ControlCommunication Theory
In the same journal
IEEE Journal on Selected Areas in Communications
Communication Systems

Search outside of DiVA

GoogleGoogle ScholarTotal: 1 downloads
The number of downloads is the sum of all downloads of full texts. It may include eg previous versions that are now no longer available

Altmetric score

Total: 164 hits
ReferencesLink to record
Permanent link

Direct link