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Abbas, Muhammad TahirORCID iD iconorcid.org/0000-0001-5495-4318
Publications (2 of 2) Show all publications
Abbas, M. T., Eklund, J., Grinnemo, K.-J. & Brunström, A. (2019). Impact of Tunable Parameters in NB-IoT Stack onthe Energy Consumption. In: Proceedings of Fifteenth Swedish National Computer Networking Workshop (SNCNW): . Paper presented at 15th Swedish National Computer Networking Workshop (SNCNW 2019). Luleå, June 4-5, 2019..
Open this publication in new window or tab >>Impact of Tunable Parameters in NB-IoT Stack onthe Energy Consumption
2019 (English)In: Proceedings of Fifteenth Swedish National Computer Networking Workshop (SNCNW), 2019Conference paper, Published paper (Refereed)
Abstract [en]

This paper studies the impact of tunable parametersin the NB-IoT stack on the energy consumption of a user equipment(UE), e.g., a wireless sensor. NB-IoT is designed to enablemassive machine-type communications for UE while providing abattery lifetime of up to 10 years. To save battery power, most oftime the UE is in dormant state and unreachable. Still, duringthe CONNECTED and IDLE state, correct tuning of criticalparameters, like Discontinuous reception (DRX), and extendedDiscontinuous reception (eDRX), respectively, are essential to savebattery power. Moreover, the DRX and eDRX actions relate tovarious parameters which are needed to be tuned in order toachieve a required UE battery lifetime. The objective of thispaper is to observe the influence of an appropriate tuning ofthese parameters to reduce the risk of an early battery drainage

Keywords
NB-IoT, energy consumption, DRX, eDRX.
National Category
Telecommunications
Research subject
Computer Science
Identifiers
urn:nbn:se:kau:diva-72440 (URN)
Conference
15th Swedish National Computer Networking Workshop (SNCNW 2019). Luleå, June 4-5, 2019.
Projects
HITS; 4707
Funder
Knowledge Foundation
Available from: 2019-06-12 Created: 2019-06-12 Last updated: 2019-07-03Bibliographically approved
Abbas, M. T., Jibran, M. A., Afaq, M. & Song, W.-C. An adaptive approach to vehicle trajectory prediction using multimodel Kalman filter. European transactions on telecommunications, Article ID e3734.
Open this publication in new window or tab >>An adaptive approach to vehicle trajectory prediction using multimodel Kalman filter
(English)In: European transactions on telecommunications, ISSN 1124-318X, E-ISSN 2161-3915, article id e3734Article in journal (Refereed) Epub ahead of print
Abstract [en]

With the aim to improve road safety services in critical situations, vehicle trajectory and future location prediction are important tasks. An infinite set of possible future trajectories can exit depending on the current state of vehicle motion. In this paper, we present a multimodel-based Extended Kalman Filter (EKF), which is able to predict a set of possible scenarios for vehicle future location. Five different EKF models are proposed in which the current state of a vehicle exists, particularly, a vehicle at intersection or on a curve path. EKF with Interacting Multiple Model framework is explored combinedly for mathematical model creation and probability calculation for that model to be selected for prediction. Three different parameters are considered to create a state vector matrix, which includes vehicle position, velocity, and distance of the vehicle from the intersection. Future location of a vehicle is then used by the software-defined networking controller to further enhance the safety and packet delivery services by the process of flow rule installation intelligently to that specific area only. This way of flow rule installation keeps the controller away from irrelevant areas to install rules, hence, reduces the network overhead exponentially. Proposed models are created and tested in MATLAB with real-time global positioning system logs from Jeju, South Korea.

Place, publisher, year, edition, pages
Wiley-Blackwell
National Category
Computer Sciences
Research subject
Computer Science
Identifiers
urn:nbn:se:kau:diva-75113 (URN)10.1002/ett.3734 (DOI)000485969000001 ()
Available from: 2019-10-10 Created: 2019-10-10 Last updated: 2019-10-17Bibliographically approved
Organisations
Identifiers
ORCID iD: ORCID iD iconorcid.org/0000-0001-5495-4318

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