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Wireless Sensor Networks in Smart Cities: The Monitoring of Water Distribution Networks Case
KTH, School of Electrical Engineering (EES), Automatic Control.ORCID iD: 0000-0002-1934-9208
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. , p. 21
Series
TRITA-EE, ISSN 1653-5146 ; 2016:60
Keywords [en]
Integer Programming, Nonconvex Optimization, Network Lifetime, Dynamic
National Category
Electrical Engineering, Electronic Engineering, Information Engineering
Research subject
Electrical Engineering
Identifiers
URN: urn:nbn:se:kth:diva-185453ISBN: 978-91-7595-964-1 (print)OAI: oai:DiVA.org:kth-185453DiVA, id: diva2:920737
Presentation
2016-05-10, Q2, OSQULDAS VÄG 10, Stockholm, 10:00 (English)
Opponent
Supervisors
Note

QC 20160419

Available from: 2016-04-20 Created: 2016-04-19 Last updated: 2020-01-07Bibliographically approved
List of papers
1. Energy Efficient Sensor Activation for Water Distribution Networks Based on Compressive Sensing
Open this publication in new window or tab >>Energy Efficient Sensor Activation for Water Distribution Networks Based on Compressive Sensing
2015 (English)In: IEEE Journal on Selected Areas in Communications, ISSN 0733-8716, E-ISSN 1558-0008, Vol. 33, no 12, p. 2997-3010Article 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
National Category
Communication Systems
Identifiers
urn:nbn:se:kth:diva-178154 (URN)10.1109/JSAC.2015.2481199 (DOI)000365223600040 ()2-s2.0-84960131576 (Scopus ID)
Funder
Wireless@kth
Note

QC 20151215

Available from: 2015-12-07 Created: 2015-12-07 Last updated: 2017-12-01Bibliographically approved
2. On Maximizing Sensor Network Lifetime by Energy Balancing
Open this publication in new window or tab >>On Maximizing Sensor Network Lifetime by Energy Balancing
2018 (English)In: IEEE Transactions on Control of Network Systems, ISSN 2325-5870, Vol. 5, no 3Article in journal (Refereed) Published
Abstract [en]

Many physical systems, such as water/electricity distribution networks, are monitored by battery-powered wireless-sensor networks (WSNs). Since battery replacement of sensor nodes is generally difficult, long-term monitoring can be only achieved if the operation of the WSN nodes contributes to long WSN lifetime. Two prominent techniques to long WSN lifetime are 1) optimal sensor activation and 2) efficient data gathering and forwarding based on compressive sensing. These techniques are feasible only if the activated sensor nodes establish a connected communication network (connectivity constraint), and satisfy a compressive sensing decoding constraint (cardinality constraint). These two constraints make the problem of maximizing network lifetime via sensor node activation and compressive sensing NP-hard. To overcome this difficulty, an alternative approach that iteratively solves energy balancing problems is proposed. However, understanding whether maximizing network lifetime and energy balancing problems are aligned objectives is a fundamental open issue. The analysis reveals that the two optimization problems give different solutions, but the difference between the lifetime achieved by the energy balancing approach and the maximum lifetime is small when the initial energy at sensor nodes is significantly larger than the energy consumed for a single transmission. The lifetime achieved by energy balancing is asymptotically optimal, and that the achievable network lifetime is at least 50% of the optimum. Analysis and numerical simulations quantify the efficiency of the proposed energy balancing approach.

Place, publisher, year, edition, pages
Institute of Electrical and Electronics Engineers (IEEE), 2018
National Category
Electrical Engineering, Electronic Engineering, Information Engineering
Identifiers
urn:nbn:se:kth:diva-185313 (URN)10.1109/TCNS.2017.2696363 (DOI)000445357100035 ()2-s2.0-85053762086 (Scopus ID)
Note

QC 20160420

Available from: 2016-04-15 Created: 2016-04-15 Last updated: 2018-10-08Bibliographically approved
3. Flowing with the water: On optimal monitoring of water distribution networks by mobile sensors
Open this publication in new window or tab >>Flowing with the water: On optimal monitoring of water distribution networks by mobile sensors
2016 (English)Conference paper, Published paper (Refereed)
National Category
Electrical Engineering, Electronic Engineering, Information Engineering
Identifiers
urn:nbn:se:kth:diva-185312 (URN)000390154400151 ()2-s2.0-84983247016 (Scopus ID)
Conference
International Conference on Computer Communications, 10-15 April 2016,San Fransisco, USA
Note

QC 20160420

Available from: 2016-04-15 Created: 2016-04-15 Last updated: 2017-01-16Bibliographically approved

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