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On Maximizing Sensor Network Lifetime by Energy Balancing
KTH, School of Electrical Engineering and Computer Science (EECS), Network and Systems engineering.ORCID iD: 0000-0002-1934-9208
KTH, School of Electrical Engineering and Computer Science (EECS), Automatic Control.
KTH, School of Electrical Engineering and Computer Science (EECS), Network and Systems engineering.ORCID iD: 0000-0001-9810-3478
KTH, School of Electrical Engineering and Computer Science (EECS), Information Science and Engineering.ORCID iD: 0000-0002-5407-0835
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. Vol. 5, no 3
National Category
Electrical Engineering, Electronic Engineering, Information Engineering
Identifiers
URN: urn:nbn:se:kth:diva-185313DOI: 10.1109/TCNS.2017.2696363ISI: 000445357100035Scopus ID: 2-s2.0-85053762086OAI: oai:DiVA.org:kth-185313DiVA, id: diva2:920023
Note

QC 20160420

Available from: 2016-04-15 Created: 2016-04-15 Last updated: 2018-10-08Bibliographically 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. p. 21
Series
TRITA-EE, ISSN 1653-5146 ; 2016:60
Keywords
Integer Programming, Nonconvex Optimization, Network Lifetime, Dynamic
National Category
Electrical Engineering, Electronic Engineering, Information Engineering
Research subject
Electrical Engineering
Identifiers
urn:nbn:se:kth:diva-185453 (URN)978-91-7595-964-1 (ISBN)
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: 2016-04-20Bibliographically approved

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