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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

Most of the utility meters in Sweden are connected using the Internet of Things (IoT) technology. This opens new possibilities for understanding society’s energy consumption dynamics and making citizens aware of their power consumption usage. In this study, we investigate the patterns of electricity consumption using machine learning methods. We collected metered data from Kalmar Energi company, the electrical grid for Kalmar city in Sweden. In addition, we collected the Kalmar weather and electricity price data from the Swedish Meteorological and Hydrological Institute (SMHI) and Nordpool, the European leading power market, respectively. We comprehensively analyze the electricity consumption data to assess the changes in overall electricity demand during the year 2021 in the city of Kalmar. This information can be of significant benefit to other regions seeking to improve their sustainability and energy consumption practices. For analysis and energy consumption prediction, we utilize two forecasting models, i.e., Random Forest (RF) and XGBoost. RF model results show a high level of accuracy with the achieved R-squared (R2) value of 0.91 compared to XGBoost value of 0.87.

Internet of Things (IoT) devices are increasingly popular due to their wide array of application domains. In IoT networks, sensor nodes are often connected in the form of a mesh topology and deployed in large numbers. Managing these resource-constrained small devices is complex and can lead to high system costs. A number of standardized protocols have been developed to handle the operation of these devices. For example, in the network layer, these small devices cannot run traditional routing mechanisms that require large computing powers and overheads. Instead, routing protocols specifically designed for IoT devices, such as the routing protocol for low-power and lossy networks, provide a more suitable and simple routing mechanism. However, they incur high overheads as the network expands. Meanwhile, reinforcement learning (RL) has proven to be one of the most effective solutions for decision making. RL holds significant potential for its application in IoT device’s communication-related decision making, with the goal of improving performance. In this paper, we explore RL’s potential in IoT devices and discuss a theoretical framework in the context of network layers to stimulate further research. The open issues and challenges are analyzed and discussed in the context of RL and IoT networks for further study.

The slogan “digital first” has become a buzzword for public organisational development at the local, regional, and national levels in Sweden. The slogan alludes to the idea that providing information to and communication with citizens should take place primarily through digital channels. This idea is also popular in other parts of the digitalised world. Obviously, digital solutions have the potential to become pedagogical, reliable, and effective interfaces for governmental interaction with citizens. However, the extent to which they are actually accessible, brought to use, and valued among older adult users (more than 65 years of age) has not attracted much research interest. Drawing on national survey data, collected in November–December 2020, on Swedish citizens (aged 65 to 90 years), this article will start to compensate for this deficit. First, it analyses the extent to which citizens have physical access to required devices and how access is related to material, discursive, and social resources. Second, it analyses resources and usage of important platforms for public services for older adults: the Pensions Agency, health care, e-pharmacy, the Social Insurance Agency, and the Tax Agency. Finally, the article examines outcomes: feelings and attitudes towards experiences of encountering a digitalising society. The article demonstrates how all three levels of divides—access, usage, and outcomes—are clearly related to older adults’ access to material, discursive, and social resources, as well as to age and pre-retirement experience with computers. It is comparatively younger individuals with longer formal education and at least average incomes and social networks who benefit the most from digitalised public services.

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.

Godkänd: 2023-05-31

The slogan “digital first” has become a buzzword for public organisational development at the local, regional, and national levels in Sweden. The slogan alludes to the idea that providing information to and communication with citizens should take place primarily through digital channels. This idea is also popular in other parts of the digitalised world. Obviously, digital solutions have the potential to become pedagogical, reliable, and effective interfaces for governmental interaction with citizens. However, the extent to which they are actually accessible, brought to use, and valued among older adult users (more than 65 years of age) has not attracted much research interest. Drawing on national survey data, collected in November–December 2020, on Swedish citizens (aged 65 to 90 years), this article will start to compensate for this deficit. First, it analyses the extent to which citizens have physical access to required devices and how access is related to material, discursive, and social resources. Second, it analyses resources and usage of important platforms for public services for older adults: the Pensions Agency, health care, e-pharmacy, the Social Insurance Agency, and the Tax Agency. Finally, the article examines outcomes: feelings and attitudes towards experiences of encountering a digitalising society. The article demonstrates how all three levels of divides—access, usage, and outcomes—are clearly related to older adults’ access to material, discursive, and social resources, as well as to age and pre-retirement experience with computers. It is comparatively younger individuals with longer formal education and at least average incomes and social networks who benefit the most from digitalised public services.

The ongoing digitalisation of societies, exacerbated by the COVID-19 pandemic, has led to increased efforts to ensure the digital inclusion of older adults. Digital inclusion strategies throughout the COVID-19 pandemic predominantly focused on increasing access and basic digital literacy of Information and Communication Technologies (ICTs) for all members of society. Older adults, who are more likely to experience digital exclusion, are amongst the target groups of digital inclusion strategies. We propose that beyond digital inclusion, there is a need to focus on digital participation and optimise opportunities for everyone to participate in communities and society in post-pandemic times. Creative digital skills are the foundation of digital participation and can lead to a variety of contributions. Digital participation offers conditions that support agency and active contributions in a digitalised society. Taking macro-, meso-, and micro-level enablers of digital participation in later life into account, we argue for the establishment and implementation of multi-layered and multisectoral partnerships that address environmental factors (including social and physical dimensions) of digital participation and create opportunities for diverse, meaningful and fulfilling engagement with ICTs in later life. The partnership approach can be used in designing and implementing digital participation programmes and should be further evaluated against the needs and lived experiences of older individuals. Foresighted research is needed to investigate key factors of effective partnerships for optimising environments for digital participation in later life.

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.

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.

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.

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., IoT lab for SME

Modern systems produce and handle a large volume of sensitive enterprise data. Therefore, security vulnerabilities in the software systems must be identified and resolved early to prevent security breaches and failures. Predicting security vulnerabilities is an alternative to identifying them as developers write code. In this study, we studied the ability of several machine learning algorithms to predict security vulnerabilities. We created two datasets containing security vulnerability information from two open-source systems: (1) Apache Tomcat (versions 4.x and five 2.5.x minor versions). We also computed source code metrics for these versions of both systems. We examined four classifiers, including Naive Bayes, Decision Tree, XGBoost Classifier, and Logistic Regression, to show their ability to predict security vulnerabilities. Moreover, an ensemble learner was introduced using a stacking classifier to see whether the prediction performance could be improved. We performed cross-version and cross-project predictions to assess the effectiveness of the best-performing model. Our results showed that the XGBoost classifier performed best compared to other learners, i.e., with an average accuracy of 97% in both datasets. The stacking classifier performed with an average accuracy of 92% in Struts and 71% in Tomcat. Our best-performing model-XGBoost-could predict with an average accuracy of 87% in Tomcat and 99% in Struts in a cross-version setup.

Technical debt at the architectural level is a severe threat to software development projects. Uncontrolled technical debt that is allowed to accumulate will undoubtedly hinder speedy development and maintenance, introduce bugs and problems in the software product, and may ultimately result in the abandonment of the source code. It is possible to detect debt accumulation by analyzing the source code and intended modules in the software architecture. However, this is seldom done in practice since it requires a correct and up-to-date mapping from source code to intended modules in the architecture. This mapping requires significant manual effort to create and maintain, something often considered too costly and laborsome. We investigate how to automate the mapping from source code to intended modules. The state-of-the-art considers it an incremental clustering problem, where source code entities should be clustered to the intended modules based on some similarity measure. As the system evolves and source code entities are added or modified, the clustering needs to be updated. The state-of-the-art techniques determine similarity based on either syntactic or semantic features, e.g., dependencies or identifier names. Large sets of parameters modify these features, e.g., weights for various types of dependencies. These parameters have a significant impact on how well the clustering performs. Unfortunately, we have not been able to identify any heuristics to help human experts determine a good set of parameters for a given system. Based on the parameters determined by, e.g., genetic optimization, it seems unlikely that general heuristics exist. Instead, we compute the similarity using a multinomial na\"ive Bayes text classifier trained on tokens from the source code entities. We also include a novel feature that captures dependencies as text to add syntactic features. Our classifier, which relies on significantly fewer parameters, outperforms the state-of-the-art techniq

The concept warm experts originally referred to people who helped their friends and family to come to terms with home-based computers and Internet connections. As digital technologies have continuously come to permeate our everyday lives, the tasks for warm experts have grown in kinds and character. The present study contributes to our understanding of warm experts by exploring the learning process involving the warm expert and the less knowledgeable other(s). Drawing on interviews with older adults (70 to 94 years of age), the study specifically explores older users’ experiences of learning about digital media with children and grandchildren. The results reveal how interaction with warm experts constituted important learning opportunities for the older adults, in which they developed their skills in using digital media. However, establishing potential learning situations and learning from warm experts was not a straightforward matter, but surrounded by a multitude of barriers structuring the possibilities for learning. This shows how the role of the warm expert is fluid and materializes in different ways in different situations. The warm expert can take the position (or be positioned) as one who solves technical issues. The warm expert can be one who fails in teaching, or one who adopts the position as a warm teacher and contributes to learning among the less knowledgeable user. In order to also be a warm teacher, the warm expert needs to understand the specific learning needs and styles of the less knowledgeable other and adapt to these needs.

The Internet of Things (IoT), as a new paradigm of connected things or objects to the Internet, allows us to monitor the environment by collecting data in a wide spatial and temporal window. Especially the utilization of IoT has increased significantly since the development of the Long Range Wide Area Network (LoRaWAN). However, deploying LoRa gateways, maintaining network infrastructure, operational cost, and quality of service are challenging. Helium has emerged as one of the largest networks in terms of coverage for IoT devices to solve such problems. Helium is decentralized, cryptocurrency incentives-based network infrastructure replacing traditional service providers. However, due to network incentives, currently, it contains more hotspots compared to active users. This paper presents our experience and analysis of deploying IoT devices for real-world applications using the Helium network. We present experiences from the IoT device’s deployment for wetland conservation in southern Sweden.

Utan tillgång till digitala medier – som smarttelefon, surfplatta eller dator – är det svårt att få vardagen att fungera i ett digitaliserande land som Sverige. För att betala räkningar, parkeringsavgifter, köpa bussbiljetter eller boka tid på vårdcentralen är det i princip nödvändigt att inneha, och kunna använda, någon form av digital apparat. Den här studien, som analyserar ett representativt urval av den svenska befolkningen i åldrarna 65 till 90 år (n = 1614), uppmärksammar särskilt de ungefär 400 000 individer som inte är digitalt aktiva och därigenom har frånkopplats från betydande delar av den offentliga som privata servicen. Överrepresenterade bland de frånkopplade är individer över 75 år med kortare utbildning, lägre inkomster än genomsnittet och med jämförelsevis begränsade sociala nätverk liksom begränsade yrkeserfarenheter av datorer eller annan digital teknik.

The Internet of Things (IoT) have shown numerous potential applications that can enhance our quality of life. IoT is becoming a core technology to bring smart homes, smart cities, and smart industries into reality. However, with potential benefits comes a challenge of sustainability, and one major concern is to minimize energy consumption. In a citywide area, managing the operation of such large-scale IoT networking is one of the complex tasks. One of the ways is to utilize dynamic sensing scheduling where the IoT device goes to the sleep mode and prevents unnecessary data transmission. In this paper, we propose a dynamic sensing (DynaSens) algorithm for an IoT-based waste management system. This algorithm helps to reduce the waste bin overflowing, thus, provides better sanitation, and it is also helpful in reducing the fuel cost of waste collection vehicles. Our work utilizes measured values such as current consumption, LiDAR measurement time, and LoRa transmission time as the input data for the simulation experiment to evaluate energy consumption. We also assessed DynaSens using a real dataset obtained from a recycling house. We use Pycom LoPy4 micro-controller as a development board. For a number of garbage-thrown scenarios, DynaSens enables longer battery longevity by reducing the repeated execution of the same tasks. © 2022 IEEE.

What happens if we see a suitable font for our design work but we do not know its name? Visual Font Recognition (VFR) systems are used to identify the font typeface in an image. These systems can assist graphic designers in identifying fonts used in images. A VFR system also aids in improving the speed and accuracy of Optical Character Recognition (OCR) systems. In this paper, we proposed the first publicly available datasets in the field of Persian font recognition and employed Convolutional Neural Networks (CNN) to address the Persian font recognition problem. The results show that the proposed pipeline obtained 78.0% top-1 accuracy on our new datasets, 89.1% in the IDPL-PFOD dataset, and 94.5% in the KAFD dataset. Furthermore, the average time spent in the entire pipeline for one sample of our proposed datasets is 0.54 and 0.017 seconds for CPU and GPU, respectively. We conclude that CNN methods can be used to recognize Persian fonts without the need for additional pre-processing steps such as feature extraction, binarization, normalization, etc.