A mobile stroke unit (MSU) is an advanced ambulance equipped with specialized technology and trained healthcare personnel to provide on-site diagnosis and treatment for stroke patients. Providing efficient access to healthcare (in a viable way) requires optimizing the placement of MSUs. In this study, we propose a time-efficient method based on a genetic algorithm (GA) to find the most suitable ambulance sites for the placement of MSUs (given the number of MSUs and a set of potential sites). We designed an efficient encoding scheme for the input data (the number of MSUs and potential sites) and developed custom selection, crossover, and mutation operators that are tailored according to the characteristics of the MSU allocation problem. We present a case study on the Southern Healthcare Region in Sweden to demonstrate the generality and robustness of our proposed GA method. Particularly, we demonstrate our method's flexibility and adaptability through a series of experiments across multiple settings. For the considered scenario, our proposed method outperforms the exhaustive search method by finding the best locations within 0.16, 1.44, and 10.09 minutes in the deployment of three MSUs, four MSUs, and five MSUs, resulting in 8.75x, 16.36x, and 24.77x faster performance, respectively. Furthermore, we validate the method's robustness by iterating GA multiple times and reporting its average fitness score (performance convergence). In addition, we show the effectiveness of our method by evaluating key hyperparameters, that is, population size, mutation rate, and the number of generations.

The rapidly evolving Internet of Things (IoT) systems demands addressing new requirements. This particularly needs efficient deployment of IoT systems to meet the quality requirements such as latency, energy consumption, privacy, and bandwidth utilization. The increasing availability of computational resources close to the edge has prompted the idea of using these for distributed computing and storage, known as edge computing. Edge computing may help and complement cloud computing to facilitate deployment of IoT systems and improve their quality. However, deciding where to deploy the various application components is not a straightforward task, and IoT system designer should be supported for the decision.

To support the designers, in this thesis we focused on the system qualities, and aimed for three main contributions. First, by reviewing the literature, we identified the relevant and most used qualities and metrics. Moreover, to analyse how computer simulation can be used as a supporting tool, we investigated the edge computing simulators, and in particular the metrics they provide for modeling and analyzing IoT systems in edge computing. Finally, we introduced a method to represent how multiple qualities can be considered in the decision. In particular, we considered distributing Deep Neural Network layers as a use case and raked the deployment options by measuring the relevant metrics via simulation.

Many Internet of Things (IoT) systems generate a massive amount of data needing to be processed and stored efficiently. Cloud computing solutions are often used to handle these tasks. However, the increasing availability of computational resources close to the edge has prompted the idea of using these for distributed computing and storage. Edge computing may help to improve IoT systems regarding important quality attributes like latency, energy consumption, privacy, and bandwidth utilization. However, deciding where to deploy the various application components is not a straightforward task. This is largely due to the trade-offs between the quality attributes relevant for the application. We have performed a systematic mapping study of 98 articles to investigate which quality attributes have been used in the literature for assessing IoT systems using edge computing. The analysis shows that time behavior and resource utilization are the most frequently used quality attributes; further, response time, turnaround time, and energy consumption are the most used metrics for quantifying these quality attributes. Moreover, simulation is the main tool used for the assessments, and the studied trade-offs are mainly between only two qualities. Finally, we identified a number of research gaps that need further study.

The deployment of Internet of Things (IoT) applications is complex since many quality characteristics should be taken into account, for example, performance, reliability, and security. In this study, we investigate to what extent the current edge computing simulators support the analysis of qualities that are relevant to IoT architects who are designing an IoT system. We first identify the quality characteristics and metrics that can be evaluated through simulation. Then, we study the available simulators in order to assess which of the identified qualities they support. The results show that while several simulation tools for edge computing have been proposed, they focus on a few qualities, such as time behavior and resource utilization. Most of the identified qualities are not considered and we suggest future directions for further investigation to provide appropriate support for IoT architects.

For the efficient execution of Deep Neural Networks (DNN) in the Internet of Things, computation tasks can be distributed and deployed on edge nodes. In contrast to deploying all computation to the cloud, the use of Distributed DNN (DDNN) often results in a reduced amount of data that is sent through the network and thus might increase the overall performance of the system. However, finding an appropriate deployment scenario is often a complex task and requires considering several criteria. In this paper, we introduce a multi-criteria decision-making method based on the Analytical Hierarchy Process for the comparison and selection of deployment alternatives. We use the RECAP simulation framework to model and simulate DDNN deployments on different scales to provide a comprehensive assessment of deployments to system designers. In a case study, we apply the method to a smart city scenario where different distributions and deployments of a DNN are analyzed and compared.

Emerging 5G and future networks (5GFNs) will change the mobile communication business ecosystem by introducing multi-service wireless applications with diverse specifications. These smart and future-oriented wireless networks are intended to address not only consumer-based smartphone applications, but also the needs of various vertical industry markets (e.g., education, energy, healthcare, transportation, manufacturing, agriculture, and so forth). 5GFNs are aimed at attaining economic value for all key stakeholders, including customers, mobile network operators (MNOs), equipment vendors, public institutions, private enterprises, digital business start-ups and various third parties. This paper discusses business model options for 5GFNs from the MNOs’ perspective.  We describe current MNOs’ business models and their shortcomings. Thereafter, we present emerging business models for 5GFNs which MNOs should consider when rolling out these networks.  

Emerging Fifth-generation (5G) mobile networks and associated technologies are expected to provide multi-service wireless applications with diverse specifications intended to address not only consumer-based smartphone applications, but also the needs of various vertical industry markets (e.g., healthcare, education, energy, mining, agriculture, manufacturing, and so forth). This paper extends 5G networks' technology orientation towards attaining economic value for all key 5G stakeholders, including customers, mobile network operators (MNOs), equipment vendors, public institutions, private enterprises, digital business start-ups and other third parties. Although several surveys and tutorials have discussed business models in connection with 5G networks, there is no comprehensive study on business models for emerging 5G networks from the MNO's perspective. In this survey article, we present and investigate key advances on business models for 5G networks and 5G MNOs in particular, from industry, use cases and research community perspectives. The paper focuses the theoretical business model concept from both strategic management and technological innovation perspectives. Thereafter, we discuss conventional business models for MNOs before presenting particular disruptive business models which can be considered for rolling out 5G networks with an aim to improve business efficiency. Additionally, the paper explores the emerging network deployment concept of private 5G networks and their related business models. Finally, we present some of the open research challenges and provide possible guidelines for implementing 5G business models based on various countries' socio-economic status and relevant 5G use cases applicable in a specific context of emerging economies.

The future delivery of intelligent services and the sustainable digitalization of industry can be enhanced through the ongoing integration of Internet of Things (IoT) smart sensors with the fifth-generation (5G) wireless ecosystems. 5G-IoT technologies are intended to support a variety of use cases, each with different requirements. These applications are so diverse and demanding that 5G networks must be adaptable to a wide variety of individual scenarios. One of the main use cases for 5G-IoT technologies is smart cities, which prioritize enhancing the quality of life, fostering teamwork, and resolving resource limitations. Typical smart city networks include a plethora of services related to smart buildings, smart offices, smart industry, smart universities, smart transportation, smart health, and more. This paper aims to explore role of 5G-IoT technologies for developing sustainable smart city systems, including the role of mobile operators to enable innovative smart city services

“Request Triggered Recharging” has been a flexible type of scheduling schemes to allow the Mobile Charging Vehicle (MCV) to supply energy for sensor nodes on demand. However, in most existing works, MCV always passively waits for the arrival of the unpredictable requests that may cause it missing the best departure time to serve nodes. To solve this problem, we propose a Balanced Distribution strategy for the number of Recharging Requests based on dynamic dual-thresholds (BDRR). Firstly, the adjustable Double Recharging Request Thresholds (DRRTs) are set for each node to ensure that all the requesting nodes can be successfully charged. Then, the Method for Setting the Energy Replenishment Value (MSERV) is proposed to enable the distribution of the moments at which nodes send out their recharging requests being concentrated within each period. Furthermore, an efficient traversal path for the MCV is constructed by safe or dangerous scheduling strategy, and the Charging Capacity Reduction Scheme (CCRS) is also executed to help survive more nodes in need. Finally, a Passer-by Recharging Scheme (PRS) is introduced to further improve the energy efficiency of the MCV. Simulation results show that BDRR outperforms the compared algorithms in terms of surviving rate of sensors as well as the energy efficiency of MCV with different network scales.

With the rapid development of Internet services, mobile communications, and IoT applications, Location-Based Service (LBS) has become an indispensable part in our daily life in recent years. However, when users benefit from LBSs, the collection and analysis of users' location data and trajectory information may jeopardize their privacy. To address this problem, a new privacy-preserving method based on historical proximity locations is proposed. The main idea of this approach is to substitute one existing historical adjacent location around the user for his/her current location and then submit the selected location to the LBS server. This method ensures that the user can obtain location-based services without submitting the real location information to the untrusted LBS server, which can improve the privacy-preserving level while reducing the calculation and communication overhead on the server side. Furthermore, our scheme can not only provide privacy preservation in snapshot queries but also protect trajectory privacy in continuous LBSs. Compared with other location privacy-preserving methods such ask-anonymity and dummy location, our scheme improves the quality of LBS and query efficiency while keeping a satisfactory privacy level.

Named data networking (NDN) is an emerging information-centric networking paradigm, in which the Internet of Things (IoT) achieves excellent scalability. Recent literature proposes the concept of NDN-IoT, which maximizes the expansion of IoT applications by deploying NDN in the IoT. In the NDN, the security is built into the network by embedding a public signature in each data package to verify the authenticity and integrity of the content. However, signature schemes in the NDN-IoT environment are facing several challenges, such as signing security challenge for resource-constrained IoT end devices (EDs) and verification efficiency challenge for NDN routers. This article mainly studies the data package authentication scheme in the package-level security mechanism. Based on mobile edge computing (MEC), an efficient certificateless group signature scheme featured with anonymity, unforgeability, traceability, and key escrow resilience is proposed. The regional and edge architecture is utilized to solve the device management problem of IoT, reducing the risks of content pollution attacks from the data source. By offloading signature pressure to MEC servers, the contradiction between heavy overhead and shortage of ED resources is avoided. Moreover, the verification efficiency in NDN router is much improved via batch verification in the proposed scheme. Both security analysis and experimental simulations show that the proposed MEC-based certificateless group signature scheme is provably secure and practical.

The ownership and demand for fitness trackers, smartwatches, and wrist-worn deviceshave been increasing globally. These devices offer various features such as measuringphysical activity, sleep monitoring, and health-related measurements like heart rate andheart rate variability using PhotoPlethysmoGraphy (PPG). However, research indicatesthat PPG measurements are less accurate on darker skin compared to lighter skin due to thehigher presence of melanin, a light-absorbing substance in dark skin. This thesis addresses the impact of melanin on the accuracy of heart rate measurements on different skin colors using four commercial smartwatches. The study involves analyzing the accuracy of these smartwatches on individuals with varying skin colors while controlling for external factors. The collected data from the smartwatches are compared to a reference sensor that uses electrocardiography (ECG) measurements with electrodes placed around the chest. Three different tests are conducted wearing the devices, with no movement, while walking, and with circular hand motions.The tests were conducted on twelve participants representing the 6 different skin types categorized using the Fitzpatrick scale. With the presented results in this thesis, it was concluded that the 4 smartwatches' measurement accuracy does not seem to be dependent on specific skin types. 

In this paper a review of current research, design and development of an Ontological Cognitive Radio (OCR) which uses Stochastically Resonant Spectrum Sensing (SRSS) and Interference Management (IM) to facilitate, expedite and optimize TV White Space is provided. We discuss the OCR basis and address IM aspects briefly, and, discuss and demonstrate the Stochastic Resonance (SR) effect using additive noise.

This work is the research result of the study conducted to try to find the common denominators between the construction industry and the climate crisis. During the education that the author has received, large parts of the teaching have been linked to the industry's climate impact and the non-existent climate awareness that the industry seems to have. As society's climate measures are perhaps the most important issue today, the author is interested in seeing how the theoretical claims about construction and the environment actually correspond to the practical reality. Reading about construction and architecture is one thing, but what happens physically in the industry is at least as crucial. Therefore, it is important that architecture students also study the practical reality, which is the reason why this report has been published. The research aims to show the simplicity required to solve and unite several complex systems based on understanding what the construction industry's greenhouse gas emissions depend on and how this can be reduced in the long term. This includes how the construction industry's active craftsmen and actors who may already be trying to influence the industry's transition to climate neutrality think and do, as well as how the higher efforts work in the issue. The research has been conducted through two cases, a Swedish Large City and a Swedish Small City, where comparisons in the planning work and sustainability work around the construction industry have been investigated. In this way, this research is triangulating. A database search is also done to support the case study and the interviews with theoretical knowledge. Several key people from the entire industry's value chain have been interviewed to gain an understanding of how the work is done at all levels and to see the attitude and how the knowledge levels are. Research shows that the climate crisis does not belong in our lives if we want to save future generations. The climate crisis unites world societies and business, and forces us to find new solutions for a common tomorrow. And the construction industry has a responsibility in that work just like everyone else. Just as suspected, there is in the world, the EU and Sweden a well-established work to counteract the climate crisis and achieve climate neutrality by the year 2045, but that this work is not followed up and worked on in a clear way by the private builders. The reasons for this are many. Among other things, poorly specified laws and regulations but also a large gap in the general level of knowledge among authorities and the industry's craftsmen or performers.

A mobile stroke unit (MSU) is an ambulance, where stroke patients can be diagnosed and treated. Recently, placement of MSUs has been studied focusing on either maximum population coverage or equal service for all patients, termed efficiency and equity, respectively. In this study, we propose an unconstrained optimization model for the placement of MSUs, designed to introduce a tradeoff between efficiency and equity. The tradeoff is based on the concepts of weighted average time to treatment and the time difference between the expected time to treatment for different geographical areas. We conduct a case-study for Sweden’s Southern Health care Region (SHR), generating three scenarios (MSU1, MSU2, and MSU3) including 1, 2, and 3 MSUs, respectively. We show that our proposed optimization model can tune the tradeoff between the efficiency and equity perspectives for the MSU(s) allocation. This enables a high level of equal service for most inhabitants, as well as reducing the time to treatment for most inhabitants of a geographic region. In particular, placing three MSUs in the SHR with the proposed tradeoff, the share of inhabitants who are expected to receive treatment within an hour potentially improved by about a factor of 14 in our model.

Digitization has been a growing phenomenon in today's society where organizations, individuals and society at large are affected by it. In pace with the emerging use of digitization,a realization of the relevance of cyber security in the public sector has increased, but not to a sufficient extent. Cyber security is about processes used to protect personal information and important data in organizations. Cyber security also includes knowledge of cyberattacks, where actors attack an organization's data most often for financial reasons. Cyber attacks have affected the public sector in several countries. The study focuses on known cyber attacks around the world that are related to public actors in healthcare, transport and electricity supply,among others. The study's analysis compares implemented measures after the incidents based on a cyber risk assessment framework. The survey shows that increased investments, new and clear work routines, training for employees and continuous testing of computer systems are important measures for the prevention of cyber attacks. The mentioned main actions that are common between the studied actors are supported by the theoretical frame of reference. This is because frameworks linked to cyber attacks also point out that investments, clear work routines and monitoring of systems contribute to protection against cyber attacks.

Den här studien ämnar minska gapet mellan socialt arbete och datavetenskap. När socialtjänsten utreder fall som handlar om hedersvåld och förtryck så brukar verktyget Nätverkskartan att användas. Ändamålet är att kartlägga klientens nätverk med syftet att hitta trygga eller otrygga punkter i nätverket, samt för att ta reda på om det finns någon eller några i nätverket som påverkar klienten negativt. Problemet är att kartan ritas upp med hjälp av papper och penna, vilket begränsar möjligheterna för djupare analyser av klientens nätverk. Genom att digitalisera verktyget Nätverkskartan med hjälp av grafteori så öppnas det dörrar för djupare analyser. Med hjälp av metodiken Design and Creation research besvaras frågorna om hur en analog nätverkskarta, anpassad för att analysera fall av hedersvåld och förtryck och genom vilka medel inom grafteori verktyget nätverkskartan skulle gynnas av digitaliseringen. Resultatet pekar på att en digitalisering av verktyget nätverkskartan är gynnsamt eftersom det möjliggör analyser av nätverkskartan utifrån fler olika perspektiv.

An important challenge to guarantee the successful operation of white space networks based on spectrum sharing, in a heterogeneous wireless network environment, is mitigation of interference. Interference mitigation (IM) is a term used to describe the techniques applied to minimize interference between networks operating in the same or adjacent spectrum hands. This paper will present a review of existing interference mitigation techniques with the aim of improving co-existence between spectrum sharing white space networks; it will also improve network quality of service. Furthermore, the paper will provide a detailed description of some of the recent IM techniques developed for TV white space networks, involving the geo-location spectrum database (GLSD) and reconfigurable radio devices.

The ever rising demand for energy all over the world, following the dwindling of the fossil fuel resources and adverse effects on the climate change, has compelled the need to save energy and lower the cost of energy utilization in the wireless computing, communication and networking applications. One of the best ways to obtain energy-efficient communication and networking is to invest in the renewable energy sources to charge batteries for communication devices and to develop smart energy consumption monitoring tools. As a result, this paper presents an energy monitoring software tool hereby referred to as the 'eCOMBAT' which is aimed at getting real-time information on the quantity of energy consumed by the communication and networking device. The knowledge of such data on time may enable the device user to reduce energy consumption thereby lowering the CO2 emissions into the environment. This paper proposes an integrated battery's discharge and device's energy consumption model; the description of the developed tool and experimental results of the tool obtained via a user friendly and well designed graphic user interface (GUI).

Jobbsökning är den process som syftar till att matcha arbetssökande med lämpliga jobb och jobbmöjligheter. Den vanligaste metoden för jobbsökning är användningen av Internet, som tillhandahåller diverse sociala nätverk. Dessa tjänster lockar miljontals besökare varje år, vilket bland annat resulterat i att Internet i nuläget spelar en genomgripande roll vad gäller jobbsökning och sysselsättning. Internet och dess informations- och kommunikationsteknologi kommer framöver fortsätta ta en mer betydelsefull roll i en del av arbets- och jobbsökningsprocessen, vilket efterlämnar dagens socialt missgynnade arbetssökande eftersom nätverken inte täcker dessa individers behov. Således utvärderades konceptet FindMeAJob vars idé handlar om att sammanknyta socialt missgynnade arbetssökande med olika typer av individer och verksamheter som är i behov av arbetskraft. Studien rekryterade socialt missgynnade arbetssökande, HR-specialister, rekryterare och Arbetsmarknadsförvaltningen i Helsingborg för att besvara frågeformulär baserat på Likertskala, fördjupningsfrågor och Storyboard. Resultaten var blandade; respondenter för gruppen socialt missgynnade arbetssökande ser i helhet positivt på FindMeAJob och anser att deras behov kan täckas någorlunda, medan sakkunniga respondenter har delade meningar. En djupare analys av resultatet resulterade i en slutsats om vad FindMeAJob torde innehålla för att möta de behov som kan ställas av de socialt missgynnade arbetssökande.

The issue of using vehicles to wirelessly recharge nodes for energy supplement in Wireless Sensor Networks has become a research hotspot in recent works. Unfortunately, most of the researches did not consider the rationality of the recharging request threshold and also overlooked the difference of node’s power consumption, which may lead to premature death of nodes as well as low efficiency of Wireless Charging Vehicles(WCVs). In order to solve the above problems, a Periodic and Distributed Energy Supplement Method based on maximum recharging benefit (PDESM) is proposed in this paper. Firstly, to avoid frequent recharging requests from nodes, we put forward an annuluses based cost-balanced data uploading strategy under deterministic deployment. Then, one WCV in each annulus periodically selects and recharges nodes located in this region which send the energy supplement requests. In addition, the predicted value of power consumption of nodes are calculated out according to the real-time energy consumption rate, and thus the most appropriate recharging request threshold is obtained. Finally, a moving path optimization scheme based on Minimum Spanning Tree is constructed for distributed recharging. Simulation results show that, PDESM performs well on enhancing the proportion of the alive nodes as well as the wireless recharging efficiency compared with NFAOC and FCFS. Moreover, it also has advantage in balancing the energy consumption of WCVs.

In order to maximize the utilization rate of the Mobile Wireless Chargers (MWCs) and reduce the recharging delay in large-scale Rechargeable Wireless Sensor Networks (WRSNs), a type of C ost- B alanced M obile E nergy R eplenishment S trategy (CBMERS) is proposed in this paper. Firstly, nodes are assigned into groups according to their remaining lifetime, which ensures that only the ones with lower residual energy are recharged in each time slot. Then, to balance energy consumption among multiple MWCs, the moving distance as well as the power cost of the MWC are taken as constraints to get the optimal trajectory allocation scheme. Moreover, by further adjusting the amount of energy being replenished to some sensor nodes, it ensures that the MWC have enough energy to fulfill the recharging task and return back to the base station. Experiment results show that, compared with the Periodic recharging strategy and the C luster based M ultiple C harges C oordination algorithm (C-MCC), the proposed method can improve the recharging efficiency of MWCs by about 48.22% and 43.35%, and the average waiting time of nodes is also reduced by about 55.72% and 30.7%, respectively.

Digital communication has gone through much development in the past decade. The literature in this field has developed and improved as well, but the educational materials still lack illustrative descriptions. The modulation techniques are a very complex area that needs practical implementation to facilitate intuitive understanding for engineering students. This thesis presents an educational application where users conduct experiments in digital modulation techniques, ASK, PSK, QAM, and OFDM. The product is a simulation application of the transmission of information from transmitter to receiver with a theoretical description. The application is programmed within the MATLAB environment, and the result is presented with a GUI that contains signal diagrams for each step of the modulation and demodulation process. This tool provides an illustrative description of the modulation techniques without any need for more extensive mathematical knowledge.

This article has the objective to study the effect voice recognition can have on the documentation and team work for janitors in EOS-Hallen in Lund. The study is built up on design and creation. An application was built for the janitors with the help of Microsoft’s voice recognition service. To gather data from the janitors, observations and interviews were conducted. The observations were conducted on 4 janitors for 2 hours during their normal work days. From the observations and the interviews conducted after the observations showed that voice recognition was welcomed by the janitors. They felt that the team work between the colleagues improved and the documentation became faster, more frequent and more detailed.

Fast and reliable connectivity is essential to enhance situational awareness and operational efficiency for public safety mission-critical (MC) users. In emergency or disaster circumstances, where existing cellular network coverage and capacity may not be available to meet MC communication demands, deployable-network-based solutions such as cells-on-wheels/wings can be utilized swiftly to ensure reliable connection for MC users. In this paper, we consider a scenario where a macro base station (BS) is destroyed due to a natural disaster and an unmanned aerial vehicle carrying BS (UAV-BS) is set up to provide temporary coverage for users in the disaster area. The UAV-BS is integrated into the mobile network using the 5G integrated access and backhaul (IAB) technology. We propose a framework and signalling procedure for applying machine learning to this use case. A deep reinforcement learning algorithm is designed to jointly optimize the access and backhaul antenna tilt as well as the three-dimensional location of the UAV-BS in order to best serve the on-ground MC users while maintaining a good backhaul connection. Our result shows that the proposed algorithm can autonomously navigate and configure the UAV-BS to improve the throughput and reduce the drop rate of MC users.

Fast and reliable wireless communication has become a critical demand in human life. In the case of mission-critical (MC) scenarios, for instance, when natural disasters strike, providing ubiquitous connectivity becomes challenging by using traditional wireless networks. In this context, unmanned aerial vehicle (UAV) based aerial networks offer a promising alternative for fast, flexible, and reliable wireless communications. Due to unique characteristics such as mobility, flexible deployment, and rapid reconfiguration, drones can readily change location dynamically to provide on-demand communications to users on the ground in emergency scenarios. As a result, the usage of UAV base stations (UAV-BSs) has been considered an appropriate approach for providing rapid connection in MC scenarios. In this paper, we study how to control multiple UAV-BSs in both static and dynamic environments. We use a system-level simulator to model an MC scenario in which a macro-BS of a cellular network is out of service and multiple UAV-BSs are deployed using integrated access and backhaul (IAB) technology to provide coverage for users in the disaster area. With the data collected from the system-level simulation, a deep reinforcement learning algorithm is developed to jointly optimize the three-dimensional placement of these multiple UAV-BSs, which adapt their 3-D locations to the on-ground user movement. The evaluation results show that the proposed algorithm can support the autonomous navigation of the UAV-BSs to meet the MC service requirements in terms of user throughput and drop rate.