Change search
Link to record
Permanent link

Direct link
BETA
Publications (10 of 32) Show all publications
Nurgazy, M., Zaslavsky, A., Jayaraman, P., Kubler, S., Mitra, K. & Saguna, S. (2019). CAVisAP: Context-Aware Visualisation of Air Pollution with IoT Platforms. In: : . Paper presented at The International Conference on High Performance Computing & Simulation (HPCS 2019), July 15 – 19, 2019 Dublin, Ireland.
Open this publication in new window or tab >>CAVisAP: Context-Aware Visualisation of Air Pollution with IoT Platforms
Show others...
2019 (English)Conference paper (Refereed)
Keywords
air pollution, context-aware, data visualization, environmental monitoring, Internet of Things, location-based
National Category
Media and Communication Technology
Research subject
Pervasive Mobile Computing; Pervasive Mobile Computing
Identifiers
urn:nbn:se:ltu:diva-76722 (URN)10.29007/9ld4 (DOI)
Conference
The International Conference on High Performance Computing & Simulation (HPCS 2019), July 15 – 19, 2019 Dublin, Ireland
Available from: 2019-11-15 Created: 2019-11-15 Last updated: 2019-11-15
Schürholz, D., Nurgazy, M., Zaslavsky, A., Jayaraman, P., Kubler, S., Mitra, K. & Saguna, S. (2019). MyAQI: Context-aware Outdoor Air Pollution Monitoring System. In: International Conference on the Internet of Things: . Paper presented at 9th International Conference on the Internet of Things (IoT 2019), October 22-25 2019, Bilbao, Spain..
Open this publication in new window or tab >>MyAQI: Context-aware Outdoor Air Pollution Monitoring System
Show others...
2019 (English)In: International Conference on the Internet of Things, 2019Conference paper, Published paper (Refereed)
National Category
Media and Communication Technology
Research subject
Pervasive Mobile Computing; Pervasive Mobile Computing
Identifiers
urn:nbn:se:ltu:diva-76721 (URN)
Conference
9th International Conference on the Internet of Things (IoT 2019), October 22-25 2019, Bilbao, Spain.
Available from: 2019-11-15 Created: 2019-11-15 Last updated: 2019-11-15
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
Open this publication in new window or tab >>Performance evaluation of FIWARE: A cloud-based IoT platform for smart cities
2019 (English)In: Journal of Parallel and Distributed Computing, ISSN 0743-7315, E-ISSN 1096-0848, Vol. 132, p. 250-261Article in journal (Refereed) 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.

Place, publisher, year, edition, pages
Elsevier, 2019
Keywords
Benchmarking, Cloud computing, Internet of things, Middleware, Quality of service, Smart cities
National Category
Media and Communication Technology
Research subject
Pervasive Mobile Computing
Identifiers
urn:nbn:se:ltu:diva-75060 (URN)10.1016/j.jpdc.2018.12.010 (DOI)000476580400021 ()2-s2.0-85066430784 (Scopus ID)
Note

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

Available from: 2019-06-27 Created: 2019-06-27 Last updated: 2019-10-03Bibliographically approved
Fejzo, O., Zaslavsky, A., Saguna, S. & Mitra, K. (2019). Proactive Context-Aware IoT-Enabled Waste Management. In: Proceeings of the 19th International Conference on Next Generation Wired/Wireless Advanced Networks and System: . Paper presented at International Conference on Next Generation Wired/Wireless Networking (NEW2AN) 2019,26-28 August, St. Petersburg, Russia (pp. 3-15). Springer
Open this publication in new window or tab >>Proactive Context-Aware IoT-Enabled Waste Management
2019 (English)In: Proceeings of the 19th International Conference on Next Generation Wired/Wireless Advanced Networks and System, Springer, 2019, p. 3-15Conference paper, Published paper (Refereed)
Abstract [en]

Exploiting future opportunities and avoiding problematic upcoming events is the main characteristic of a proactively adapting system, leading to several benefits such as uninterrupted and efficient services. In the era when IoT applications are a tangible part of our reality, with interconnected devices almost everywhere, there is potential to leverage the diversity and amount of their generated data in order to act and take proactive decisions in several use cases, smart waste management as such. Our work focuses in devising a system for proactive adaptation of behavior, named ProAdaWM. We propose a reasoning model and system architecture that handles waste collection disruptions due to severe weather in a sustainable and efficient way using decision theory concepts. The proposed approach is validated by implementing a system prototype and conducting a case study.

Place, publisher, year, edition, pages
Springer, 2019
Series
Lecture Notes in Computer Science ; 11660
Keywords
Proactive adaptation, Reasoning model, Smart cities, IoT-enabled Waste Management
National Category
Media and Communication Technology
Research subject
Pervasive Mobile Computing
Identifiers
urn:nbn:se:ltu:diva-76618 (URN)10.1007/978-3-030-30859-9_1 (DOI)978-3-030-30858-2 (ISBN)
Conference
International Conference on Next Generation Wired/Wireless Networking (NEW2AN) 2019,26-28 August, St. Petersburg, Russia
Available from: 2019-11-05 Created: 2019-11-05 Last updated: 2019-11-05Bibliographically approved
Bezerra, N. S., Åhlund, C., Saguna, S. & A. de Sousa Jr., V. (2019). Propagation Model Evaluation for LoRaWAN: Planning Tool Versus Real Case Scenario. In: : . Paper presented at 2019 IEEE 5th World Forum on Internet of Things (WF-IoT). IEEE
Open this publication in new window or tab >>Propagation Model Evaluation for LoRaWAN: Planning Tool Versus Real Case Scenario
2019 (English)Conference paper, Published paper (Refereed)
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 smart things. 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 calculated by 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.

Place, publisher, year, edition, pages
IEEE, 2019
Keywords
LoRa, LoRaWAN, IoT, propagation, ITM, ITMWO, Okumura-Hata, smart city.
National Category
Communication Systems
Research subject
Pervasive Mobile Computing
Identifiers
urn:nbn:se:ltu:diva-76399 (URN)10.1109/WF-IoT.2019.8767299 (DOI)978-1-5386-4980-0 (ISBN)
Conference
2019 IEEE 5th World Forum on Internet of Things (WF-IoT)
Available from: 2019-10-15 Created: 2019-10-15 Last updated: 2019-10-22
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
Open this publication in new window or tab >>Propagation Model Evaluation for LoRaWAN: Planning Tool Versus Real Case Scenario
2019 (English)In: 2019 IEEE 5th World Forum on Internet of Things (WF-IoT) / [ed] IEEE, https://ieeexplore.ieee.org/document/8767299, 2019, p. 1-6Conference paper, Published paper (Refereed)
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.

Place, publisher, year, edition, pages
https://ieeexplore.ieee.org/document/8767299: , 2019
Keywords
LoRa, LoRaWAN, IoT, propagation, ITM, ITMWO, Okumura-Hata, smart city
National Category
Telecommunications
Research subject
Computer Science
Identifiers
urn:nbn:se:ltu:diva-75384 (URN)10.1109/WF-IoT.2019.8767299 (DOI)978-1-5386-4980-0 (ISBN)
Conference
2019 IEEE 5th World Forum on Internet of Things (WF-IoT)
Available from: 2019-08-03 Created: 2019-08-03 Last updated: 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.
Open this publication in new window or tab >>Temperature Impact in LoRaWAN: A Case Study in Northern Sweden
2019 (English)In: Sensors, ISSN 1424-8220, E-ISSN 1424-8220, Vol. 19, no 20, article id 4414Article in journal (Refereed) 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.

Place, publisher, year, edition, pages
MDPI, 2019
Keywords
ADR, IoT, LoRa, LoRaWAN, propagation model, smart city
National Category
Communication Systems Media and Communication Technology
Research subject
Pervasive Mobile Computing
Identifiers
urn:nbn:se:ltu:diva-76400 (URN)10.3390/s19204414 (DOI)000497864700062 ()31614808 (PubMedID)2-s2.0-85073456473 (Scopus ID)
Note

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

Available from: 2019-10-15 Created: 2019-10-15 Last updated: 2019-12-09Bibliographically 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
Pervasive Mobile 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: 2019-04-03Bibliographically 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
Belyakhina, T., Zaslavsky, A., Mitra, K., Saguna, S. & Jayaraman, P. P. (2017). DisCPAQ: Distributed Context Acquisition and Reasoning for Personalized Indoor Air Quality Monitoring in IoT-Based Systems. In: Galinina O., Andreev S., Balandin S., Koucheryavy Y. (Ed.), Internet of Things, Smart Spaces, and Next Generation Networks and Systems: 17th International Conference, NEW2AN 2017, 10th Conference, ruSMART 2017, Third Workshop NsCC 2017, St. Petersburg, Russia, August 28–30, 2017, Proceedings. Paper presented at 17th International Conference on Next Generation Teletraffic and Wired/Wireless Advanced Networks and Systems, NEW2AN 2017, 10th Conference on Internet of Things and Smart Spaces, ruSMART 2017 and 3rd International Workshop on Nano-scale Computing and Communications, NsCC 2017, St. Petersburg, Russia, August 28–30, 2017 (pp. 75-86). Cham: Springer
Open this publication in new window or tab >>DisCPAQ: Distributed Context Acquisition and Reasoning for Personalized Indoor Air Quality Monitoring in IoT-Based Systems
Show others...
2017 (English)In: Internet of Things, Smart Spaces, and Next Generation Networks and Systems: 17th International Conference, NEW2AN 2017, 10th Conference, ruSMART 2017, Third Workshop NsCC 2017, St. Petersburg, Russia, August 28–30, 2017, Proceedings / [ed] Galinina O., Andreev S., Balandin S., Koucheryavy Y., Cham: Springer, 2017, p. 75-86Conference paper, Published paper (Refereed)
Abstract [en]

The rapidly emerging Internet of Things supports many diverse applications including environmental monitoring. Air quality, both indoors and outdoors, proved to be a significant comfort and health factor for people. This paper proposes a smart context-aware system for indoor air quality monitoring and prediction called DisCPAQ. The system uses data streams from air quality measurement sensors to provide real-time personalised air quality service to users through a mobile app. The proposed system is agnostic to sensor infrastructure. The paper proposes a context model based on Context Spaces Theory, presents the architecture of the system and identifies challenges in developing large scale IoT applications. DisCPAQ implementation, evaluation and lessons learned are all discussed in the paper.

Place, publisher, year, edition, pages
Cham: Springer, 2017
Series
Lecture Notes in Computer Science, ISSN 0302-9743 ; 10531
National Category
Media and Communication Technology
Research subject
Pervasive Mobile Computing
Identifiers
urn:nbn:se:ltu:diva-66097 (URN)10.1007/978-3-319-67380-6_7 (DOI)2-s2.0-85031425522 (Scopus ID)978-3-319-67379-0 (ISBN)978-3-319-67380-6 (ISBN)
Conference
17th International Conference on Next Generation Teletraffic and Wired/Wireless Advanced Networks and Systems, NEW2AN 2017, 10th Conference on Internet of Things and Smart Spaces, ruSMART 2017 and 3rd International Workshop on Nano-scale Computing and Communications, NsCC 2017, St. Petersburg, Russia, August 28–30, 2017
Available from: 2017-10-12 Created: 2017-10-12 Last updated: 2019-04-03Bibliographically approved
Organisations
Identifiers
ORCID iD: ORCID iD iconorcid.org/0000-0001-8561-7963

Search in DiVA

Show all publications