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Publications (10 of 83) Show all publications
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)
Open this publication in new window or tab >>RACH performance in massive machine-type communications access scenario
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2018 (English)Conference paper, Published paper (Refereed)
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.

Place, publisher, year, edition, pages
New York: Institute of Electrical and Electronics Engineers (IEEE), 2018
Series
IEEE Wireless Communications and Networking Conference, ISSN 1525-3511
National Category
Media and Communication Technology
Research subject
Pervasive Mobile Computing; Pervasive Mobile Computing
Identifiers
urn:nbn:se:ltu:diva-70274 (URN)10.1109/WCNC.2018.8377151 (DOI)000435542401039 ()2-s2.0-85049181729 (Scopus ID)9781538617342 (ISBN)
Conference
2018 IEEE Wireless Communications and Networking Conference, WCNC 2018, Barcelona, Spain, 15-18 April 2018
Available from: 2018-08-08 Created: 2018-08-08 Last updated: 2018-08-09Bibliographically approved
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
Open this publication in new window or tab >>Smart Buildings as Cyber-Physical Systems:Data-Driven Predictive Control Strategies for Energy Efficiency
2018 (English)In: Renewable & sustainable energy reviews, ISSN 1364-0321, E-ISSN 1879-0690, Vol. 90, p. 742-756Article in journal (Refereed) 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.

Place, publisher, year, edition, pages
Elsevier, 2018
Keywords
Energy Efficiency, Predictive Control, Cyber-Physical System, Existing Buildings
National Category
Computer Systems Media and Communication Technology
Research subject
Pervasive Mobile Computing
Identifiers
urn:nbn:se:ltu:diva-67706 (URN)10.1016/j.rser.2018.04.013 (DOI)000434917700050 ()2-s2.0-85045248317 (Scopus ID)
Note

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

Available from: 2018-02-20 Created: 2018-02-20 Last updated: 2018-07-19Bibliographically approved
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.
Open this publication in new window or tab >>A Bayesian system for cloud performance diagnosis and prediction
2017 (English)In: Proceedings of the International Conference on Cloud Computing Technology and Science, CloudCom, Piscataway, NJ: IEEE Computer Society, 2017, p. 371-374, article id 7830706Conference paper, Published paper (Refereed)
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

Place, publisher, year, edition, pages
Piscataway, NJ: IEEE Computer Society, 2017
Series
International Conference on Cloud Computing Technology and Science, ISSN 2330-2194
National Category
Media and Communication Technology
Research subject
Mobile and Pervasive Computing
Identifiers
urn:nbn:se:ltu:diva-62202 (URN)10.1109/CloudCom.2016.0065 (DOI)000398536300049 ()2-s2.0-85013025438 (Scopus ID)9781509014453 (ISBN)
Conference
8th IEEE International Conference on Cloud Computing Technology and Science, CloudCom 2016, Luxembourg, 12-15 December 2016
Available from: 2017-02-28 Created: 2017-02-28 Last updated: 2018-06-28Bibliographically approved
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.
Open this publication in new window or tab >>ALPINE: A Bayesian System For Cloud Performance Diagnosis And Prediction
2017 (English)In: 2017 IEEE International Conference on Services Computing (SCC), Piscataway, NJ: IEEE, 2017, p. 281-288, article id 8034996Conference paper, Published paper (Refereed)
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%.

Place, publisher, year, edition, pages
Piscataway, NJ: IEEE, 2017
Keywords
Bayesian Network, Cloud Computing, Diagnosis, Quality of Service, Prediction
National Category
Computer and Information Sciences Computer Sciences Other Computer and Information Science Computer Systems
Research subject
Mobile and Pervasive Computing
Identifiers
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)
Conference
14th IEEE International Conference on Services Computing (SCC), Honolulu, HI, USA, 25-30 June 2017
Available from: 2017-06-25 Created: 2017-06-25 Last updated: 2018-06-28Bibliographically approved
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
Open this publication in new window or tab >>Applied Machine Learning: Forecasting Heat Load in District Heating System
2016 (English)In: Energy and Buildings, ISSN 0378-7788, E-ISSN 1872-6178, Vol. 133, p. 478-488Article in journal (Refereed) 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.

National Category
Media and Communication Technology
Research subject
Mobile and Pervasive Computing
Identifiers
urn:nbn:se:ltu:diva-59596 (URN)10.1016/j.enbuild.2016.09.068 (DOI)000389087300045 ()2-s2.0-84992362157 (Scopus ID)
Note

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

Available from: 2016-10-10 Created: 2016-10-10 Last updated: 2018-07-10Bibliographically approved
Nanda, R., Saguna, S., Mitra, K. & Åhlund, C. (2016). BayesForSG:: A Bayesian Model for Forecasting Thermal Load in Smart Grids (ed.). In: (Ed.), (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
Open this publication in new window or tab >>BayesForSG:: A Bayesian Model for Forecasting Thermal Load in Smart Grids
2016 (English)In: SAC '16: Proceedings of the 31st Annual ACM Symposium on Applied Computing, New York: ACM Digital Library, 2016, p. 2135-2141Conference paper, Published paper (Refereed)
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.

Place, publisher, year, edition, pages
New York: ACM Digital Library, 2016
Keywords
Smart Grid, smart city, District Heating System, bayesian network, machine learning, forecasting, Information technology - Computer science, Informationsteknik - Datorvetenskap
National Category
Media and Communication Technology
Research subject
Mobile and Pervasive Computing
Identifiers
urn:nbn:se:ltu:diva-30238 (URN)10.1145/2851613.2853127 (DOI)2-s2.0-84975788871 (Scopus ID)400b9534-955c-47d1-812d-fe87e9b05c73 (Local ID)978-1-4503-3739-7 (ISBN)400b9534-955c-47d1-812d-fe87e9b05c73 (Archive number)400b9534-955c-47d1-812d-fe87e9b05c73 (OAI)
Conference
ACM Symposium on Applied Computing : 04/04/2016 - 07/04/2016
Note
Godkänd; 2016; 20151203 (karan)Available from: 2016-09-30 Created: 2016-09-30 Last updated: 2018-07-10Bibliographically approved
Ngo, K., Saguna, S., Mitra, K. & Åhlund, C. (2016). IReHMo: An efficient IoT-Based Remote health Monitoring System for Smart Regions (ed.). In: (Ed.), 2015 17th International Conference on E-Health Networking, Application and Services, HealthCom 2015: Boston, United States, 13 - 17 October 2015. Paper presented at International Conference on E-health Networking, Applications & Services : IEEE HealthCom 2015 14/10/2015 - 17/10/2015 (pp. 563-568). Piscataway, NJ: IEEE Communications Society, Article ID 7454565.
Open this publication in new window or tab >>IReHMo: An efficient IoT-Based Remote health Monitoring System for Smart Regions
2016 (English)In: 2015 17th International Conference on E-Health Networking, Application and Services, HealthCom 2015: Boston, United States, 13 - 17 October 2015, Piscataway, NJ: IEEE Communications Society, 2016, p. 563-568, article id 7454565Conference paper, Published paper (Refereed)
Abstract [en]

The ageing population worldwide is constantly rising, both in urban and regional areas. There is a need for IoT-based remote health monitoring systems that take care of the health of elderly people without compromising their convenience and preference of staying at home. However, such systems may generate large amounts of data. The key research challenge addressed in this paper is to efficiently transmit healthcare data within the limit of the existing network infrastructure, especially in remote areas. In this paper, we identified the key network requirements of a typical remote health monitoring system in terms of real-time event update, bandwidth requirements and data generation. Furthermore, we studied the network communication protocols such as CoAP, MQTT and HTTP to understand the needs of such a system, in particular the bandwidth requirements and the volume of generated data. Subsequently, we have proposed IReHMo - an IoT-based remote health monitoring architecture that efficiently delivers healthcare data to the servers. The CoAP-based IReHMo implementation helps to reduce up to 90% volume of generated data for a single sensor event and up to 56% required bandwidth for a healthcare scenario. Finally, we conducted a scalability analysis to determine the feasibility of deploying IReHMo in large numbers in regions of north Sweden.

Place, publisher, year, edition, pages
Piscataway, NJ: IEEE Communications Society, 2016
Keywords
IoT, Cloud Computing, remote health monitoring, sensor, coap, perfomance, pervasive computing, smart city, smart region, Information technology - Computer engineering, Informationsteknik - Datorteknik
National Category
Media and Communication Technology
Research subject
Mobile and Pervasive Computing
Identifiers
urn:nbn:se:ltu:diva-32413 (URN)10.1109/HealthCom.2015.7454565 (DOI)6e8414cf-1df1-4450-ab2f-bd84f0a140cb (Local ID)978-1-4673-8325-7 (ISBN)6e8414cf-1df1-4450-ab2f-bd84f0a140cb (Archive number)6e8414cf-1df1-4450-ab2f-bd84f0a140cb (OAI)
Conference
International Conference on E-health Networking, Applications & Services : IEEE HealthCom 2015 14/10/2015 - 17/10/2015
Note

Validerad; 2016; Nivå 1; 20150917 (karan)

Available from: 2016-09-30 Created: 2016-09-30 Last updated: 2018-06-28Bibliographically approved
Louis, B., Mitra, K., Saguna, S. & Åhlund, C. (2015). CloudSimDisk: Energy-Aware Storage Simulation in CloudSim (ed.). In: (Ed.), (Ed.), 2015 IEEE/ACM 8th International Conference on Utility and Cloud Computing (UCC): Limassol, 7-10 Dec. 2015. Paper presented at IEEE/ACM International Conference on Utility and Cloud Computing : 07/12/2015 - 07/12/2015 (pp. 11-15). Piscataway, NJ: IEEE Communications Society, Article ID 7431390.
Open this publication in new window or tab >>CloudSimDisk: Energy-Aware Storage Simulation in CloudSim
2015 (English)In: 2015 IEEE/ACM 8th International Conference on Utility and Cloud Computing (UCC): Limassol, 7-10 Dec. 2015, Piscataway, NJ: IEEE Communications Society, 2015, p. 11-15, article id 7431390Conference paper, Published paper (Refereed)
Abstract [en]

The cloud computing paradigm is continually evolving, and with it, the size and the complexity of its infrastructure. Assessing the performance of a cloud environment is an essential but an arduous task. Further, the energy consumed by data centers is steadily increasing and major components such as the storage systems need to be more energy efficient. Cloud simulation tools have proved quite useful to study these issues. However, these simulation tools lack mechanisms to study energy efficient storage in cloud systems. This paper contributes in the area of cloud computing by extending the widely used cloud simulator CloudSim. In this paper, we propose CloudSimDisk, a scalable module for modeling and simulation of energy-aware storage in cloud systems. We show how CloudSimDisk can be used to simulate energy-aware storage, and can be extended to study new algorithms for energy-awareness in cloud systems. Our simulation results proved to be in accordance with the analytical models that were developed to model energy consumption of hard disk drives in cloud systems. The source code of CloudSimDisk is also made available for the research community for further testing and development.

Place, publisher, year, edition, pages
Piscataway, NJ: IEEE Communications Society, 2015
Keywords
Cloud Computing, simulation, Energy efficiency, Storage, cloudsim, Information technology - Computer engineering, Informationsteknik - Datorteknik
National Category
Media and Communication Technology
Research subject
Mobile and Pervasive Computing
Identifiers
urn:nbn:se:ltu:diva-32408 (URN)10.1109/UCC.2015.15 (DOI)000380446800002 ()2-s2.0-84965023589 (Scopus ID)6e79bb29-f127-4b5a-acf0-21cd894f562d (Local ID)9780769556970 (ISBN)6e79bb29-f127-4b5a-acf0-21cd894f562d (Archive number)6e79bb29-f127-4b5a-acf0-21cd894f562d (OAI)
Conference
IEEE/ACM International Conference on Utility and Cloud Computing : 07/12/2015 - 07/12/2015
Note
Validerad; 2016; Nivå 1; 20150919 (karan)Available from: 2016-09-30 Created: 2016-09-30 Last updated: 2018-07-10Bibliographically approved
Jimenez, L. L., Simon, M. G., Schelén, O., Kristiansson, J., Synnes, K. & Åhlund, C. (2015). CoMA: Resource Monitoring of Docker Containers (ed.). In: (Ed.), (Ed.), Proceedings of the 5th International Conference on Cloud Computing and Services Science (CLOSER 2015): . Paper presented at International Conference on Cloud Computing and Services Science : 20/05/2015 - 22/05/2015 (pp. 145-154). : SCITEPRESS Digital Library, 1
Open this publication in new window or tab >>CoMA: Resource Monitoring of Docker Containers
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2015 (English)In: Proceedings of the 5th International Conference on Cloud Computing and Services Science (CLOSER 2015), SCITEPRESS Digital Library , 2015, Vol. 1, p. 145-154Conference paper, Published paper (Refereed)
Abstract [en]

This research paper presents CoMA, a Container Monitoring Agent, that oversees resource consumption of operating system level virtualization platforms, primarily targeting container-based platforms such as Docker. The core contribution is CoMA, together with a quantitative evaluation verifying the validity of the measurements reported by the agent for three metrics: CPU, memory and block I/O. The proof-of-concept is implemented for Docker-based systems and consists of CoMA, the Ganglia Monitoring System and the Host sFlow agent. This research is in line with the rising trend of container adoption which is due to the resource efficiency and ease of deployment. These characteristics have set containers in a position to topple virtual machines as the reigning virtualization technology in data centers.

Place, publisher, year, edition, pages
SCITEPRESS Digital Library, 2015
Keywords
Docker, Containers, OS-level virtualization, Cloud Computing, Information technology - Computer science, Informationsteknik - Datorvetenskap
National Category
Media and Communication Technology
Research subject
Mobile and Pervasive Computing
Identifiers
urn:nbn:se:ltu:diva-31992 (URN)10.5220/0005448001450154 (DOI)65372d05-6cc9-4916-b19f-df00f4a146dc (Local ID)978-989-758-104-5 (ISBN)65372d05-6cc9-4916-b19f-df00f4a146dc (Archive number)65372d05-6cc9-4916-b19f-df00f4a146dc (OAI)
Conference
International Conference on Cloud Computing and Services Science : 20/05/2015 - 22/05/2015
Projects
Cloudberry Datacenters
Note
Godkänd; 2015; Bibliografisk uppgift: The full text of this paper is only available to INSTICC members. ; 20150821 (larjim)Available from: 2016-09-30 Created: 2016-09-30 Last updated: 2018-05-09Bibliographically approved
Mitra, K., Zaslavsky, A. & Åhlund, C. (2015). Context-Aware QoE Modelling, Measurement and Prediction in Mobile Computing Systems (ed.). Paper presented at . IEEE Transactions on Mobile Computing, 14(5), 920-936
Open this publication in new window or tab >>Context-Aware QoE Modelling, Measurement and Prediction in Mobile Computing Systems
2015 (English)In: IEEE Transactions on Mobile Computing, ISSN 1536-1233, E-ISSN 1558-0660, Vol. 14, no 5, p. 920-936Article in journal (Refereed) Published
Abstract [en]

Quality of Experience (QoE) as an aggregate of Quality of Service (QoS) and human user-related metrics will be the key success factor for current and future mobile computing systems. QoE measurement and prediction are complex tasks as they may involve a large parameter space such as location, delay, jitter, packet loss and user satisfaction just to name a few. These tasks necessitate the development of practical context-aware QoE models that efficiently determine relationships between user context and QoE parameters. In this paper, we propose, develop and validate a novel decision-theoretic approach called CaQoEM for QoE modelling, measurement and prediction. We address the challenge of QoE measurement and prediction where each QoE parameter can be measured on a different scale and may involve different units of measurement. CaQoEM is context-aware and uses Bayesian networks and utility theory to measure and predict users' QoE under uncertainty. We validate CaQoEM using extensive experimentation, user studies and simulations. The results soundly demonstrate that CaQoEM correctly measures range-defined QoE using a bipolar scale. For QoE prediction, an overall accuracy of 98.93\% was achieved using 10-fold cross validation in multiple diverse network conditions such as vertical handoffs, wireless signal fading and wireless network congestion.

Keywords
QoE, Quality of Experience, Context-Awareness, context, Mobile IP, mobile computing, Bayesian Networks, Prediction, wireless networks, decision making, Decision Theory, use cases, subjective test, experimentation, Information technology - Telecommunication, Informationsteknik - Telekommunikation
National Category
Media and Communication Technology
Research subject
Mobile and Pervasive Computing
Identifiers
urn:nbn:se:ltu:diva-9731 (URN)10.1109/TMC.2013.155 (DOI)000352569700004 ()2-s2.0-84926301859 (Scopus ID)865f01a9-1f24-4f6b-88a5-502fb8072562 (Local ID)865f01a9-1f24-4f6b-88a5-502fb8072562 (Archive number)865f01a9-1f24-4f6b-88a5-502fb8072562 (OAI)
Note
Validerad; 2015; Nivå 2; 20131114 (karan)Available from: 2016-09-29 Created: 2016-09-29 Last updated: 2018-07-10Bibliographically approved
Organisations
Identifiers
ORCID iD: ORCID iD iconorcid.org/0000-0002-8681-9572

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