| 1. |
- Araujo, Victor, et al.
(författare)
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Performance evaluation of FIWARE : A cloud-based IoT platform for smart cities
- 2019
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Ingår i: Journal of Parallel and Distributed Computing. - : Elsevier. - 0743-7315 .- 1096-0848. ; 132, s. 250-261
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Tidskriftsartikel (refereegranskat)abstract
- 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.
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| 2. |
- Banerjee, N, et al.
(författare)
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R-U-In?-Exploiting Rich Presence and Converged Communications for Next-Generation Activity-Oriented Social Networking
- 2009
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Ingår i: Tenth International Conference on Mobile Data Management: Systems, Services and Middleware (MDM 2009). - Piscataway, NJ : IEEE Communications Society. - 9780769536507 - 9781424441532 ; , s. 222-
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Konferensbidrag (refereegranskat)abstract
- With the growing popularity of social networking, traditional Internet Service Providers (ISPs) and telecom operators have both started exploring new opportunities to boost their revenue streams. The efforts have facilitated consumers to stay connected to their favorite social networks,be it from an ISP portal or a mobile device. The use of Web 2.0 technologies and converged communication tools has further led to a rise in both user-generated content as well as contextual information (i.e. rich presence) about users - including their current location, availability, interests and moods. In this evolving landscape, social networking players need to innovate for value-centric usage models that increase customer stickiness,along with business models to monetize the social media. To this end, we present R-U-In? - an activity-oriented social networking system for users to collaborate and participate in activities of mutual interest. Activities can be initiated and scheduled on-demand and be as ephemeral as the user interests themselves. R-U-In? leverages contextual modeling and reasoning techniques to enable "social search" based on real-time user interests and finds potential matches for the proposed activity. Further, it exploits next-generation presence and communication technologies to manage the entire activity lifecycle in real-time. Initial survey results, based on a prototype implementation of R-U-In?, attest to the promise of realtime activity-oriented social networking - both in terms of an effective collaboration tool for value-oriented social networking users and an enhanced end-user experience.
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| 3. |
- Belyakhina, Tamara, et al.
(författare)
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DisCPAQ : Distributed Context Acquisition and Reasoning for Personalized Indoor Air Quality Monitoring in IoT-Based Systems
- 2017
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Ingår i: Internet of Things, Smart Spaces, and Next Generation Networks and Systems. - Cham : Springer. - 9783319673790 - 9783319673806 ; , s. 75-86
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Konferensbidrag (refereegranskat)abstract
- 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.
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| 4. |
- Bezerra, Nibia Souza, et al.
(författare)
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Propagation Model Evaluation for LoRaWAN : Planning Tool Versus Real Case Scenario
- 2019
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Ingår i: IEEE 5th World Forum on Internet of Things. - : IEEE.
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Konferensbidrag (refereegranskat)abstract
- 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.
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| 5. |
- Bezerra, Nibia Souza, et al.
(författare)
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Temperature Impact in LoRaWAN : A Case Study in Northern Sweden
- 2019
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Ingår i: Sensors. - : MDPI. - 1424-8220. ; 19:20
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Tidskriftsartikel (refereegranskat)abstract
- 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.
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| 6. |
- de Koning, Enrico, et al.
(författare)
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AI Algorithm to Predict Acute Coronary Syndrome in Prehospital Cardiac Care: Retrospective Cohort Study
- 2023
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Ingår i: JMIR Cardio. - : JMIR Publications. - 2561-1011. ; 7:1
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Tidskriftsartikel (refereegranskat)abstract
- Background: Overcrowding of hospitals and emergency departments (EDs) is a growing problem. However, not all ED consultations are necessary. For example, 80% of patients in the ED with chest pain do not have an acute coronary syndrome (ACS). Artificial intelligence (AI) is useful in analyzing (medical) data, and might aid health care workers in prehospital clinical decision-making before patients are presented to the hospital.Objective: The aim of this study was to develop an AI model which would be able to predict ACS before patients visit the ED. The model retrospectively analyzed prehospital data acquired by emergency medical services' nurse paramedics.Methods: Patients presenting to the emergency medical services with symptoms suggestive of ACS between September 2018 and September 2020 were included. An AI model using a supervised text classification algorithm was developed to analyze data. Data were analyzed for all 7458 patients (mean 68, SD 15 years, 54% men). Specificity, sensitivity, positive predictive value (PPV), and negative predictive value (NPV) were calculated for control and intervention groups. At first, a machine learning (ML) algorithm (or model) was chosen; afterward, the features needed were selected and then the model was tested and improved using iterative evaluation and in a further step through hyperparameter tuning. Finally, a method was selected to explain the final AI model.Results: The AI model had a specificity of 11% and a sensitivity of 99.5% whereas usual care had a specificity of 1% and a sensitivity of 99.5%. The PPV of the AI model was 15% and the NPV was 99%. The PPV of usual care was 13% and the NPV was 94%.Conclusions: The AI model was able to predict ACS based on retrospective data from the prehospital setting. It led to an increase in specificity (from 1% to 11%) and NPV (from 94% to 99%) when compared to usual care, with a similar sensitivity. Due to the retrospective nature of this study and the singular focus on ACS it should be seen as a proof-of-concept. Other (possibly life-threatening) diagnoses were not analyzed. Future prospective validation is necessary before implementation.
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| 7. |
- Fejzo, Orsola, et al.
(författare)
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Proactive Context-Aware IoT-Enabled Waste Management
- 2019
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Ingår i: Proceeings of the 19th International Conference on Next Generation Wired/Wireless Advanced Networks and System. - Cham : Springer. ; , s. 3-15
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Konferensbidrag (refereegranskat)abstract
- 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.
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| 8. |
- Idowu, Samuel, et al.
(författare)
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Applied Machine Learning : Forecasting Heat Load in District Heating System
- 2016
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Ingår i: Energy and Buildings. - : Elsevier BV. - 0378-7788 .- 1872-6178. ; 133, s. 478-488
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Tidskriftsartikel (refereegranskat)abstract
- 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.
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| 9. |
- Idowu, Samuel, et al.
(författare)
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Forecasting Heat Load for Smart District Heating Systems : A Machine Learning Approach
- 2014
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Ingår i: 2014 IEEE International Conference on Smart Grid Communications (SmartGridComm 2014). - Piscataway, NJ : IEEE Communications Society. - 9781479949342 ; , s. 554-559
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Konferensbidrag (refereegranskat)abstract
- The rapid increase in energy demand requires effective measures to plan and optimize resources for efficient energy production within a smart grid environment. This paper presents a data driven approach to forecasting heat load for multi- family apartment buildings in a District Heating System (DHS). The forecasting model is built using six and eleven weeks of data from five building substations. The external factors and internal factors influencing the heat load in substations are parameters used as our model’s input. Short-term forecast models are generated using four supervised Machine Learning (ML) techniques: Support Vector Regression (SVR), Regression Tree, Feed Forwards Neural Network (FFNN) and Multiple Linear Regression (MLR). Performance comparison among these ML methods was carried out. The effects of combining the internal and external factors influencing heat load at substations was studied. The models are evaluated with varying horizon up to 24-hours ahead. The results show that SVR has the best accuracy of 5.6% MAPE for the best-case scenario.
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| 10. |
- Idowu, Samuel O., 1985-
(författare)
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Applied Machine Learning in District Heating System
- 2018
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Licentiatavhandling (övrigt vetenskapligt/konstnärligt)abstract
- In an increasingly applied domain of pervasive computing, sensing devices are being deployed progressively for data acquisition from various systems through the use of technologies such as wireless sensor networks. Data obtained from such systems are used analytically to advance or improve system performance or efficiency. The possibility to acquire an enormous amount of data from any target system has made machine learning a useful approach for several large-scale analytical solutions. Machine learning has proved viable in the area of the energy sector, where the global demand for energy and the increasingly accepted need for green energy is gradually challenging energy supplies and the efficiency in its consumption.This research, carried out within the area of pervasive computing, aims to explore the application of machine learning and its effectiveness in the energy sector with dependency on sensing devices. The target application area readily falls under a multi-domain energy grid which provides a system across two energy utility grids as a combined heat and power system. The multi-domain aspect of the target system links to a district heating system network and electrical power from a combined heat and power plant. This thesis, however, focuses on the district heating system as the application area of interest while contributing towards a future goal of a multi-domain energy grid, where improved efficiency level, reduction of overall carbon dioxide footprint and enhanced interaction and synergy between the electricity and thermal grid are vital goals. This thesis explores research issues relating to the effectiveness of machine learning in forecasting heat demands at district heating substations, and the key factors affecting domestic heat load patterns in buildings.The key contribution of this thesis is the application of machine learning techniques in forecasting heat energy consumption in buildings, and our research outcome shows that supervised machine learning methods are suitable for domestic thermal load forecast. Among the examined machine learning methods which include multiple linear regression, support vector machine, feed forward neural network, and regression tree, the support vector machine performed best with a normalized root mean square error of 0.07 for a 24-hour forecast horizon. In addition, weather and time information are observed to be the most influencing factors when forecasting heat load at heating network substations. Investigation on the effect of using substation's operational attributes, such as the supply and return temperatures, as additional input parameters when forecasting heat load shows that the use of substation's internal operational attributes has less impact.
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