Endre søk
Link to record
Permanent link

Direct link
BETA
Alternativa namn
Publikasjoner (10 av 87) Visa alla publikasjoner
Minovski, D., Åhlund, C., Mitra, K. & Johansson, P. (2019). Analysis and Estimation of Video QoE in Wireless Cellular Networks using Machine Learning. In: 2019 Eleventh International Conference on Quality of Multimedia Experience (QoMEX): . Paper presented at 2019 Eleventh International Conference on Quality of Multimedia Experience (QoMEX), 5-7 June 2019,Berlin, Germany. IEEE
Åpne denne publikasjonen i ny fane eller vindu >>Analysis and Estimation of Video QoE in Wireless Cellular Networks using Machine Learning
2019 (engelsk)Inngår i: 2019 Eleventh International Conference on Quality of Multimedia Experience (QoMEX), IEEE, 2019Konferansepaper, Publicerat paper (Fagfellevurdert)
Abstract [en]

The use of video streaming services are increasing in the cellular networks, inferring a need to monitor video quality to meet users' Quality of Experience (QoE). The so-called no-reference (NR) models for estimating video quality metrics mainly rely on packet-header and bitstream information. However, there are situations where the availability of such information is limited due to tighten security and encryption, which necessitates exploration of alternative parameters for conducting video QoE assessment. In this study we collect real-live in-smartphone measurements describing the radio link of the LTE connection while streaming reference videos in uplink. The radio measurements include metrics such as RSSI, RSRP, RSRQ, and CINR. We then use these radio metrics to train a Random Forrest machine learning model against calculated video quality metrics from the reference videos. The aim is to estimate the Mean Opinion Score (MOS), PSNR, Frame delay, Frame skips, and Blurriness. Our result show 94% classification accuracy, and 85% model accuracy (R 2 value) when predicting the MOS using regression. Correspondingly, we achieve 89%, 84%, 85%, and 82% classification accuracy when predicting PSNR, Frame delay, Frame Skips, and Blurriness respectively. Further, we achieve 81%, 77%, 79%, and 75% model accuracy (R 2 value) regarding the same parameters using regression.

sted, utgiver, år, opplag, sider
IEEE, 2019
Emneord
QoE, QoS, Video, MOS, PSNR, LTE
HSV kategori
Forskningsprogram
Distribuerade datorsystem; Distribuerade datorsystem
Identifikatorer
urn:nbn:se:ltu:diva-76619 (URN)10.1109/QoMEX.2019.8743281 (DOI)978-1-5386-8212-8 (ISBN)
Konferanse
2019 Eleventh International Conference on Quality of Multimedia Experience (QoMEX), 5-7 June 2019,Berlin, Germany
Tilgjengelig fra: 2019-11-05 Laget: 2019-11-05 Sist oppdatert: 2019-11-05bibliografisk kontrollert
Araujo, V., Mitra, K., Saguna, S. & Åhlund, C. (2019). Performance evaluation of FIWARE: A cloud-based IoT platform for smart cities. Journal of Parallel and Distributed Computing, 132, 250-261
Åpne denne publikasjonen i ny fane eller vindu >>Performance evaluation of FIWARE: A cloud-based IoT platform for smart cities
2019 (engelsk)Inngår i: Journal of Parallel and Distributed Computing, ISSN 0743-7315, E-ISSN 1096-0848, Vol. 132, s. 250-261Artikkel i tidsskrift (Fagfellevurdert) Published
Abstract [en]

As the Internet of Things (IoT) becomes a reality, millions of devices will be connected to IoT platforms in smart cities. These devices will cater to several areas within a smart city such as healthcare, logistics, and transportation. These devices are expected to generate significant amounts of data requests at high data rates, therefore, necessitating the performance benchmarking of IoT platforms to ascertain whether they can efficiently handle such devices. In this article, we present our results gathered from extensive performance evaluation of the cloud-based IoT platform, FIWARE. In particular, to study FIWARE’s performance, we developed a testbed and generated CoAP and MQTT data to emulate large-scale IoT deployments, crucial for future smart cities. We performed extensive tests and studied FIWARE’s performance regarding vertical and horizontal scalability. We present bottlenecks and limitations regarding FIWARE components and their cloud deployment. Finally, we discuss cost-efficient FIWARE deployment strategies that can be extremely beneficial to stakeholders aiming to deploy FIWARE as an IoT platform for smart cities.

sted, utgiver, år, opplag, sider
Elsevier, 2019
Emneord
Benchmarking, Cloud computing, Internet of things, Middleware, Quality of service, Smart cities
HSV kategori
Forskningsprogram
Distribuerade datorsystem
Identifikatorer
urn:nbn:se:ltu:diva-75060 (URN)10.1016/j.jpdc.2018.12.010 (DOI)000476580400021 ()2-s2.0-85066430784 (Scopus ID)
Merknad

Validerad;2019;Nivå 2;2019-07-08 (johcin)

Tilgjengelig fra: 2019-06-27 Laget: 2019-06-27 Sist oppdatert: 2019-10-03bibliografisk kontrollert
Bezerra, N. S., Åhlund, C., Saguna, S. & de Sousa Jr., V. A. (2019). Propagation Model Evaluation for LoRaWAN: Planning Tool Versus Real Case Scenario. In: IEEE (Ed.), 2019 IEEE 5th World Forum on Internet of Things (WF-IoT): . Paper presented at 2019 IEEE 5th World Forum on Internet of Things (WF-IoT) (pp. 1-6). https://ieeexplore.ieee.org/document/8767299
Åpne denne publikasjonen i ny fane eller vindu >>Propagation Model Evaluation for LoRaWAN: Planning Tool Versus Real Case Scenario
2019 (engelsk)Inngår i: 2019 IEEE 5th World Forum on Internet of Things (WF-IoT) / [ed] IEEE, https://ieeexplore.ieee.org/document/8767299, 2019, s. 1-6Konferansepaper, Publicerat paper (Fagfellevurdert)
Abstract [en]

LoRa has emerged as a prominent technology for the Internet of Things (IoT), with LoRa Wide Area Network (LoRaWAN) emerging as a suitable connection solution for smartthings. The choice of the best location for the installation of gateways, as well as a robust network server configuration, are key to the deployment of a LoRaWAN. In this paper, we present an evaluation of Received Signal Strength Indication (RSSI) values collected from the real-life LoRaWAN deployed in Skellefteå, Sweden, when compared with the values calculatedby a Radio Frequency (RF) planning tool for the Irregular Terrain Model (ITM), Irregular Terrain with Obstructions Model (ITWOM) and Okumura-Hata propagation models. Five sensors are configured and deployed along a wooden bridge, with different Spreading Factors (SFs), such as SF 7, 10 and 12. Our results show that the RSSI values calculated using the RF planning tool for ITWOM are closest to the values obtained from the real-life LoRaWAN. Moreover, we also show evidence that the choice of a propagation model in an RF planning tool has to be made with care, mainly due to the terrain conditions of the area where the network and the sensors are deployed.

sted, utgiver, år, opplag, sider
https://ieeexplore.ieee.org/document/8767299: , 2019
Emneord
LoRa, LoRaWAN, IoT, propagation, ITM, ITMWO, Okumura-Hata, smart city
HSV kategori
Forskningsprogram
Datalogi
Identifikatorer
urn:nbn:se:ltu:diva-75384 (URN)10.1109/WF-IoT.2019.8767299 (DOI)978-1-5386-4980-0 (ISBN)
Konferanse
2019 IEEE 5th World Forum on Internet of Things (WF-IoT)
Tilgjengelig fra: 2019-08-03 Laget: 2019-08-03 Sist oppdatert: 2019-10-22
Bezerra, N. S., Åhlund, C., Saguna, S. & de Sousa Jr., V. A. (2019). Temperature Impact in LoRaWAN: A Case Study in Northern Sweden. Sensors, 19(20), Article ID 4414.
Åpne denne publikasjonen i ny fane eller vindu >>Temperature Impact in LoRaWAN: A Case Study in Northern Sweden
2019 (engelsk)Inngår i: Sensors, ISSN 1424-8220, E-ISSN 1424-8220, Vol. 19, nr 20, artikkel-id 4414Artikkel i tidsskrift (Fagfellevurdert) Published
Abstract [en]

LoRaWAN has become popular as an IoT enabler. The low cost, ease of installation and the capacity of fine-tuning the parameters make this network a suitable candidate for the deployment of smart cities. In northern Sweden, in the smart region of Skellefteå, we have deployed a LoRaWAN to enable IoT applications to assist the lives of citizens. As Skellefteå has a subarctic climate, we investigate how the extreme changes in the weather happening during a year affect a real LoRaWAN deployment in terms of SNR, RSSI and the use of SF when ADR is enabled. Additionally, we evaluate two propagation models (Okumura-Hata and ITM) and verify if any of those models fit the measurements obtained from our real-life network. Our results regarding the weather impact show that cold weather improves the SNR while warm weather makes the sensors select lower SFs, to minimize the time-on-air. Regarding the tested propagation models, Okumura-Hata has the best fit to our data, while ITM tends to overestimate the RSSI values.

sted, utgiver, år, opplag, sider
MDPI, 2019
Emneord
ADR, IoT, LoRa, LoRaWAN, propagation model, smart city
HSV kategori
Forskningsprogram
Distribuerade datorsystem
Identifikatorer
urn:nbn:se:ltu:diva-76400 (URN)10.3390/s19204414 (DOI)000497864700062 ()31614808 (PubMedID)2-s2.0-85073456473 (Scopus ID)
Merknad

Validerad;2019;Nivå 2;2019-10-21 (johcin)

Tilgjengelig fra: 2019-10-15 Laget: 2019-10-15 Sist oppdatert: 2019-12-09bibliografisk kontrollert
Bezerra, N. S., Wang, M., Åhlund, C., Nordberg, M. & Schelén, O. (2018). RACH performance in massive machine-type communications access scenario. In: : . Paper presented at 2018 IEEE Wireless Communications and Networking Conference, WCNC 2018, Barcelona, Spain, 15-18 April 2018. New York: Institute of Electrical and Electronics Engineers (IEEE)
Åpne denne publikasjonen i ny fane eller vindu >>RACH performance in massive machine-type communications access scenario
Vise andre…
2018 (engelsk)Konferansepaper, Publicerat paper (Fagfellevurdert)
Abstract [en]

With the increasing number of devices performing Machine-Type Communications (MTC), mobile networks are expected to encounter a high load of burst transmissions. One bottleneck in such cases is the Random Access Channel (RACH) procedure, which is responsible for the attachment of devices, among other things. In this paper, we performed a rich-parameter based simulation on RACH to identify the procedure bottlenecks. A finding from the studied scenarios is that the Physical Downlink Control Channel (PDCCH) capacity for the grant allocation is the main limitation for the RACH capacity rather than the number of Physical Random Access Channel (PRACH) preambles. Guided by our simulation results, we proposed improvements to the RACH procedure and to PDCCH.

sted, utgiver, år, opplag, sider
New York: Institute of Electrical and Electronics Engineers (IEEE), 2018
Serie
IEEE Wireless Communications and Networking Conference, ISSN 1525-3511
HSV kategori
Forskningsprogram
Distribuerade datorsystem; Distribuerade datorsystem
Identifikatorer
urn:nbn:se:ltu:diva-70274 (URN)10.1109/WCNC.2018.8377151 (DOI)000435542401039 ()2-s2.0-85049181729 (Scopus ID)9781538617342 (ISBN)
Konferanse
2018 IEEE Wireless Communications and Networking Conference, WCNC 2018, Barcelona, Spain, 15-18 April 2018
Tilgjengelig fra: 2018-08-08 Laget: 2018-08-08 Sist oppdatert: 2019-10-15bibliografisk kontrollert
Schmidt, M. & Åhlund, C. (2018). Smart Buildings as Cyber-Physical Systems:Data-Driven Predictive Control Strategies for Energy Efficiency. Renewable & sustainable energy reviews, 90, 742-756
Åpne denne publikasjonen i ny fane eller vindu >>Smart Buildings as Cyber-Physical Systems:Data-Driven Predictive Control Strategies for Energy Efficiency
2018 (engelsk)Inngår i: Renewable & sustainable energy reviews, ISSN 1364-0321, E-ISSN 1879-0690, Vol. 90, s. 742-756Artikkel i tidsskrift (Fagfellevurdert) Published
Abstract [en]

Due to its significant contribution to global energy usage and the associated greenhouse gas emissions, existing buildingstock’s energy efficiency must improve. Predictive building control promises to contribute to that by increasing theefficiency of building operations. Predictive control complements other means to increase performance such as refurbishmentsas well as modernizations of systems. This survey reviews recent works and contextualizes these with thecurrent state of the art of interrelated topics in data handling, building automation, distributed control, and semantics.The comprehensive overview leads to seven research questions guiding future research directions.

sted, utgiver, år, opplag, sider
Elsevier, 2018
Emneord
Energy Efficiency, Predictive Control, Cyber-Physical System, Existing Buildings
HSV kategori
Forskningsprogram
Distribuerade datorsystem
Identifikatorer
urn:nbn:se:ltu:diva-67706 (URN)10.1016/j.rser.2018.04.013 (DOI)000434917700050 ()2-s2.0-85045248317 (Scopus ID)
Merknad

Validerad;2018;Nivå 2;2018-04-26 (andbra)

Tilgjengelig fra: 2018-02-20 Laget: 2018-02-20 Sist oppdatert: 2018-07-19bibliografisk kontrollert
Palm, E., Mitra, K., Saguna, S. & Åhlund, C. (2017). A Bayesian system for cloud performance diagnosis and prediction. In: Proceedings of the International Conference on Cloud Computing Technology and Science, CloudCom: . Paper presented at 8th IEEE International Conference on Cloud Computing Technology and Science, CloudCom 2016, Luxembourg, 12-15 December 2016 (pp. 371-374). Piscataway, NJ: IEEE Computer Society, Article ID 7830706.
Åpne denne publikasjonen i ny fane eller vindu >>A Bayesian system for cloud performance diagnosis and prediction
2017 (engelsk)Inngår i: Proceedings of the International Conference on Cloud Computing Technology and Science, CloudCom, Piscataway, NJ: IEEE Computer Society, 2017, s. 371-374, artikkel-id 7830706Konferansepaper, Publicerat paper (Fagfellevurdert)
Abstract [en]

The stochastic nature of the cloud systems makes cloud quality of service (QoS) performance diagnosis and prediction a challenging task. A plethora of factors including virtual machine types, data centre regions, CPU types, time-of-the-day, and day-of-the-week contribute to the variability of the cloud QoS. The state-of-the-art methods for cloud performance diagnosis do not capture and model complex and uncertain inter-dependencies between these factors for efficient cloud QoS diagnosis and prediction. This paper presents ALPINE, a proof-of-concept system based on Bayesian networks. Using a real-life dataset, we demonstrate that ALPINE can be utilised for efficient cloud QoS diagnosis and prediction under stochastic cloud conditions

sted, utgiver, år, opplag, sider
Piscataway, NJ: IEEE Computer Society, 2017
Serie
International Conference on Cloud Computing Technology and Science, ISSN 2330-2194
HSV kategori
Forskningsprogram
Distribuerade datorsystem
Identifikatorer
urn:nbn:se:ltu:diva-62202 (URN)10.1109/CloudCom.2016.0065 (DOI)000398536300049 ()2-s2.0-85013025438 (Scopus ID)9781509014453 (ISBN)
Konferanse
8th IEEE International Conference on Cloud Computing Technology and Science, CloudCom 2016, Luxembourg, 12-15 December 2016
Tilgjengelig fra: 2017-02-28 Laget: 2017-02-28 Sist oppdatert: 2019-04-03bibliografisk kontrollert
Mitra, K., Saguna, S., Åhlund, C. & Ranjan, R. (2017). ALPINE: A Bayesian System For Cloud Performance Diagnosis And Prediction. In: 2017 IEEE International Conference on Services Computing (SCC): . Paper presented at 14th IEEE International Conference on Services Computing (SCC), Honolulu, HI, USA, 25-30 June 2017 (pp. 281-288). Piscataway, NJ: IEEE, Article ID 8034996.
Åpne denne publikasjonen i ny fane eller vindu >>ALPINE: A Bayesian System For Cloud Performance Diagnosis And Prediction
2017 (engelsk)Inngår i: 2017 IEEE International Conference on Services Computing (SCC), Piscataway, NJ: IEEE, 2017, s. 281-288, artikkel-id 8034996Konferansepaper, Publicerat paper (Fagfellevurdert)
Abstract [en]

Cloud performance diagnosis and prediction is a challenging problem due to the stochastic nature of the cloud systems. Cloud performance is affected by a large set of factors such as virtual machine types, regions, workloads, wide area network delay and bandwidth. Therefore, necessitating the determination of complex relationships between these factors. The current research in this area does not address the challenge of modeling the uncertain and complex relationships between these factors. Further, the challenge of cloud performance prediction under uncertainty has not garnered sufficient attention. This paper proposes, develops and validates ALPINE, a Bayesian system for cloud performance diagnosis and prediction. ALPINE incorporates Bayesian networks to model uncertain and complex relationships between several factors mentioned above. It handles missing, scarce and sparse data to diagnose and predict stochastic cloud performance efficiently. We validate our proposed system using extensive real data and show that it predicts cloud performance with high accuracy of 91.93%.

sted, utgiver, år, opplag, sider
Piscataway, NJ: IEEE, 2017
Emneord
Bayesian Network, Cloud Computing, Diagnosis, Quality of Service, Prediction
HSV kategori
Forskningsprogram
Distribuerade datorsystem
Identifikatorer
urn:nbn:se:ltu:diva-64458 (URN)10.1109/SCC.2017.43 (DOI)000425931600036 ()2-s2.0-85032332116 (Scopus ID)978-1-5386-2005-2 (ISBN)
Konferanse
14th IEEE International Conference on Services Computing (SCC), Honolulu, HI, USA, 25-30 June 2017
Tilgjengelig fra: 2017-06-25 Laget: 2017-06-25 Sist oppdatert: 2018-06-28bibliografisk kontrollert
Idowu, S., Saguna, S., Åhlund, C. & Schelén, O. (2016). Applied Machine Learning: Forecasting Heat Load in District Heating System. Energy and Buildings, 133, 478-488
Åpne denne publikasjonen i ny fane eller vindu >>Applied Machine Learning: Forecasting Heat Load in District Heating System
2016 (engelsk)Inngår i: Energy and Buildings, ISSN 0378-7788, E-ISSN 1872-6178, Vol. 133, s. 478-488Artikkel i tidsskrift (Fagfellevurdert) Published
Abstract [en]

Forecasting energy consumption in buildings is a key step towards the realization of optimized energy production, distribution and consumption. This paper presents a data driven approach for analysis and forecast of aggregate space and water thermal load in buildings. The analysis and the forecast models are built using district heating data unobtrusively collected from ten residential and commercial buildings located in Skellefteå, Sweden. The load forecast models are generated using supervised machine learning techniques, namely, support vector machine, regression tree, feed forward neural network, and multiple linear regression. The model takes the outdoor temperature, historical values of heat load, time factor variables and physical parameters of district heating substations as its input. A performance comparison among the machine learning methods and identification of the importance of models input variables is carried out. The models are evaluated with varying forecast horizons of every hour from 1 up to 48 hours. Our results show that support vector machine, feed forward neural network and multiple linear regression are more suitable machine learning methods with lower performance errors than the regression tree. Support vector machine has the least normalized root mean square error of 0.07 for a forecast horizon of 24 hour.

HSV kategori
Forskningsprogram
Distribuerade datorsystem
Identifikatorer
urn:nbn:se:ltu:diva-59596 (URN)10.1016/j.enbuild.2016.09.068 (DOI)000389087300045 ()2-s2.0-84992362157 (Scopus ID)
Merknad

Validerad; 2016; Nivå 2; 2016-11-08 (andbra)

Tilgjengelig fra: 2016-10-10 Laget: 2016-10-10 Sist oppdatert: 2018-07-10bibliografisk kontrollert
Nanda, R., Saguna, S., Mitra, K. & Åhlund, C. (2016). BayesForSG: A Bayesian Model for Forecasting Thermal Load in Smart Grids (ed.). In: (Ed.), SAC '16: Proceedings of the 31st Annual ACM Symposium on Applied Computing. Paper presented at ACM Symposium on Applied Computing : 04/04/2016 - 07/04/2016 (pp. 2135-2141). New York: ACM Digital Library
Åpne denne publikasjonen i ny fane eller vindu >>BayesForSG: A Bayesian Model for Forecasting Thermal Load in Smart Grids
2016 (engelsk)Inngår i: SAC '16: Proceedings of the 31st Annual ACM Symposium on Applied Computing, New York: ACM Digital Library, 2016, s. 2135-2141Konferansepaper, Publicerat paper (Fagfellevurdert)
Abstract [en]

Forecasting the thermal load demand for residential buildings assists in optimizing energy production and developing demand response strategies in a smart grid system. However, the presence of a large number of factors such as outdoor temperature, district heating operational parameters, building characteristics and occupant behavior, make thermal load forecasting a challenging task. This paper presents an efficient model for thermal load forecast in buildings with different variations of heat load consumption across both winter and spring seasons using a Bayesian Network. The model has been validated by utilizing the realistic district heating data of three residential buildings from the district heating grid of the city of Skellefteå, Sweden over a period of four months. The results from our model shows that the current heat load consumption and outdoor temperature forecast have the most influence on the heat load forecast. Further, our model outperforms state-of-the-art methods for heat load forecasting by achieving a higher average accuracy of 77.97% by utilizing only 10% of the training data for a forecast horizon of 1 hour.

sted, utgiver, år, opplag, sider
New York: ACM Digital Library, 2016
Emneord
Smart Grid, smart city, District Heating System, bayesian network, machine learning, forecasting, Information technology - Computer science, Informationsteknik - Datorvetenskap
HSV kategori
Forskningsprogram
Distribuerade datorsystem
Identifikatorer
urn:nbn:se:ltu:diva-30238 (URN)10.1145/2851613.2853127 (DOI)2-s2.0-84975788871 (Scopus ID)400b9534-955c-47d1-812d-fe87e9b05c73 (Lokal ID)978-1-4503-3739-7 (ISBN)400b9534-955c-47d1-812d-fe87e9b05c73 (Arkivnummer)400b9534-955c-47d1-812d-fe87e9b05c73 (OAI)
Konferanse
ACM Symposium on Applied Computing : 04/04/2016 - 07/04/2016
Merknad

Godkänd; 2016; 20151203 (karan)

Tilgjengelig fra: 2016-09-30 Laget: 2016-09-30 Sist oppdatert: 2019-03-04bibliografisk kontrollert
Organisasjoner
Identifikatorer
ORCID-id: ORCID iD iconorcid.org/0000-0002-8681-9572