Most of the utility meters in Sweden are now integrated with Internet of Things (IoT) technology. This modern approach significantly enhances our understanding of energy consumption patterns and empowers consumers with detailed insights into their power usage. Additionally, it provides energy companies and grid owners with critical data to facilitate future energy production planning. However, having data at our disposal is only half the battle won. The method employed to forecast energy consumption is equally important due to the complex interplay between long-term trends, seasonal fluctuations, and other unpredictable factors. To optimally utilize this data, we analyzed several robust time-series forecasting models: Random Forest, XGBoost, SARIMAX, FB Prophet, and a Convolutional Neural Network (CNN). Each of these models was chosen for its unique strengths in capturing long-term trends and short-term variations, making them appropriate candidates for predicting power consumption. We showcase the models' performance on the energy consumption data from commercial property owners in 2021 and evaluate their performance based on key performance metrics such as mean squared error (MSE), root mean squared error (RMSE), mean absolute error (MAE), mean absolute percentage error (MAPE), Relative Root Mean Square Error (RRMSE), Coefficient of determination (R2), R 2 ), and Standard Deviation (SD). Our results demonstrate that while FB Prophet, with its ability to effectively factor in external parameters such as price and temperature, fared well in predicting aggregated consumption, it was effectively outperformed by the CNN classifier. The CNN model demonstrated exceptional prediction capabilities and flexibility in adding additional features to the model. For example, the CNN model with the highest accuracy showed the lowest MSE compared to Random Forest, XGBoost, SARIMAX, and FB Prophet with reductions of 75.70%, 69.48%, 49.45%, and 30.62%, respectively. Additionally, the CNN model showed superior R2 2 values, indicating a better fit to the data. Specifically, the R2 2 value for the CNN model was 0.93% on the training set and 0.60% on the testing set, outperforming the other models in terms of explained variance. We also utilized AutoML to analyze a 4-year dataset (2021-2023) to showcase the generalizability of the models. Using AutoML, the R2 2 value increased from 47% to 83% with an expanded dataset, indicating that other models will also achieve better results. From a qualitative perspective, contrary to the prevailing notion that deep learning models demand substantial resources, our experience revealed that training a CNN model did not pose significantly greater challenges than traditional models. This reinforces the untapped potential of deep learning in time-series forecasting, highlighting that complex problems like electricity consumption forecasts may benefit from advanced solutions like CNN.

Small and medium-sized enterprises (SMEs)can significantly enhance their efficiency and productivity with the integration of emerging technologies like the Internet of Things (IoT). However, limited resources and a lack of expertise in information and communication technologies often present challenges for SMEs in their journey towards digitalization. This paper outlines the IoT roadmap, detailing the necessary knowledge and support required to support SMEs in using IoT technologies. Drawing upon several pilot case studies as illustrative examples, the paper underscores the value that IoT and related platforms can offer SMEs within the framework of smart and sustainable development. Additionally, it highlights the challenges typically encountered in the adoption and integration of these technologies

Architecture refactoring is a big challenge and requires thorough analysis and labor-intensive, error-prone activities to restructure functionalities from a legacy architecture to a new intended one. Indeed, source code should be adapted to match the new structure. In this context, automatically mapping source code to the intended architecture would significantly reduce manual work and prevent technical debt. To this end, in this paper, we aim to map methods to architectural modules solely defined by textual descriptions, i.e., formulated as a machine learning text classification problem. Methods are mapped into modules using different approaches. We apply the proposed approach to an open-source software system, results show that vectorizing text and code using large language models outperforms other modern methods. The different applied machine learning classifiers perform comparably well, where the best attain accuracy of around 40% and F1-score of around 30%.

Large language models (LLMs) have achieved planetary popularity and have become accepted in higher education. On the basis of a survey that revealed the attitudes of students, reinforced by face-to-face interviews, and our own extensive academic background, we defined a realistic solution that enables the integration of LLMs for the creation of assignments. It embraces essay writing as well as various aspects of computer programming. The experiments were carried out during the winter semester of academic 2023/24 at two universities from two different countries. This paper unveils the experience gained in the creation of computer science assignments with and without the use of LLM. Comparative analysis refers on three approaches: traditional or manual assignment preparation without using any LLM; full reliance on LLMs; and a hybrid mode, depending on the amount of application of the LLM in the preparation of the assignments. The proposed solution was evaluated quantitatively, with the aim of becoming a benchmark for examining the integration of LLM studies into higher education. Findings reveal the importance of hybrid mode, as the most preferred approach among students. 

This book offers the latest research results in recent development on the principles, techniques and applications in mobile crowdsourcing. It presents state-of-the-art content and provides an in-depth overview of the basic background in this related field. Crowdsourcing involves a large crowd of participants working together to contribute or produce goods and services for the society. The early 21st century applications of crowdsourcing can be called crowdsourcing 1.0, which includes businesses using crowdsourcing to accomplish various tasks, such as the ability to offload peak demand, access cheap labor, generate better results in a timely matter, and reach a wider array of talent outside the organization.  Mobile crowdsensing can be described as an extension of crowdsourcing to the mobile network to combine the idea of crowdsourcing with the sensing capacity of mobile devices. As a promising paradigm for completing complex sensing and computation tasks, mobile crowdsensing serves the vital purpose of exploiting the ubiquitous smart devices carried by mobile users to make conscious or unconscious collaboration through mobile networks. Considering that we are in the era of mobile internet, mobile crowdsensing is developing rapidly and has great advantages in deployment and maintenance, sensing range and granularity, reusability, and other aspects. Due to the benefits of using mobile crowdsensing, many emergent applications are now available for individuals, business enterprises, and governments. In addition, many new techniques have been developed and are being adopted.

The Internet of Things (IoT) notion is quickly influencing t he architectures of data-driven systems d ue to the ever-increasing rapid technological progress in all sectors. The IoT involves the collection and exchange of data from a large number of interconnected devices or sensors. The collected data is structured and transmitted in a variety of different data formats such as JSON, CBOR, BSON, or simply a binary format. The data format used by an IoT device can have a significant i mpact on t he efficiency of its data transmission. In general, using a more compact and efficient data format can help to reduce t he amount of data that needs to be transmitted, which can improve the overall speed and performance of the device. For example, using a binary data format rather than a text-based format can often result in smaller data sizes and faster transmission times. Similarly, using a binary format in a more compressed form can further help to reduce the size of the data being transmitted, which can further improve the efficiency of the transmission. In this paper, we propose Delta Binary (i.e., DeltaBin) to reduce the binary data format by transmitting only changed data. We assess DeltaBin using a real IoT deployment scenario.

An accurate fuel consumption prediction model is the basis for ship navigation status analysis, energy conservation, and emission reduction. In this study, we develop a black-box model based on machine learning and a white-box model based on mathematical methods to predict ship fuel consumption rates. We also apply the Kwon formula as a data preprocessing cleaning method for the black-box model that can eliminate the data generated during the acceleration and deceleration process. The ship model test data and the regression methods are employed to evaluate the accuracy of the models. Furthermore, we use the predicted correlation between fuel consumption rates and speed under simulated conditions for model performance validation. We also discuss applying the data-cleaning method in the preprocessing of the black-box model. The results demonstrate that this method is feasible and can support the performance of the fuel consumption model in a broad and dense distribution of noise data in data collected from real ships. We improved the error to 4% of the white-box model and the R2² to 0.9977 and 0.9922 of the XGBoost and RF models, respectively. After applying the Kwon cleaning method, the value of R2² also can reach 0.9954, which can provide decision support for the operation of shipping companies.

This paper presents the activities of the Applied IoT Lab at the Department of Computer Science and Media Technology, Linnaeus University (LNU), Kalmar, Sweden. The lab is actively engaged in IoT-based educational programs, including a series of workshops and pilot cases. The lab is funded by the European Union and two Swedish counties – Kalmar and Kronoberg. The workshops and pilot cases are part of the research project named IoT Lab for Small and Medium-sized Enterprises (SMEs). One of the lab’s main objectives is to strengthen and support local companies with IoT. The project IoT Lab for SMEs also aims to spread knowledge and inspire the local community about the possibilities of using IoT technologies by organizing open lab days, in-depth lectures, and seminars. This paper introduces Applied IoT Lab at LNU, its educational programs, and industry-academic cooperation, including workshops and a number of ongoing pilot cases.

Low-Power Wide-Area Network (LPWAN) technologies offer new opportunities for data collection, transmission, and decision-making optimization. Similarly, a wide range of use cases of computer vision and object detection algorithms can be found across different industries. This paper presents a case study focusing on the utilization of LPWAN infrastructure, specifically the Helium network, coupled with computer vision and object detection algorithms, to optimize passenger ferry operation. The passenger ferry called M/S Dessi operates between Kalmar and Färjestaden in Sweden during the summer season. By implementing an Edge-computing solution, real-time data collection and communication are achieved, enabling accurate measurement of passenger flow. This approach is superior to traditional methods of collecting passenger data, such as manual counting or CCTV surveillance. Real-time passenger data is invaluable for traffic planning, crowd prediction, revenue enhancement, and speed and fuel optimization. The utilization of the Helium network ensures reliable and long-distance data transmission, extending the system’s applicability to multiple ferries and distant locations. The proposed approach can be utilized to integrate passenger ferries that operate in close proximity to urban areas into society’s digital transformation efforts. This study highlights the potential of LPWAN, computer vision, and object detection in enhancing passenger ferry operations, contributing to enhanced efficiency and sustainability.