In our thesis we explore the Automatic Question/Answer Generation (AQAG) and the application of Machine Learning (ML) in natural language queries. Initially we create a collection of question/answer tuples conceptually based on processing received data from (virtual) sensors placed in a smart city. Subsequently we train a Gated Recurrent Unit(GRU) model on the generated dataset and evaluate the accuracy we can achieve in answering those questions. This will help in turn to address the problem of automatic sensor composition based on natural language queries. To this end, the contribution of this thesis is two-fold: on one hand we are providing anautomatic procedure for dataset construction, based on natural language question templates, and on the other hand we apply a ML approach that establishes the correlation between the natural language queries and their virtual sensor representation, via their functional representation. We consider virtual sensors to be entities as described by Mihailescu et al, where they provide an interface constructed with certain properties in mind. We use those sensors for our application domain of a smart city environment, thus constructing our dataset around questions relevant to it.

<p>International Data Corporation^[3] (IDC) data estimates that 152,200 Internet of things (IoT) devices will be connected to the Internet every minute by the year 2025. This rapid expansion in the utilization of IoT devices in everyday life leads to an increase in the attack surface for cybercriminals. IoT devices are frequently compromised and used for the creation of botnets. However, it is difficult to apply the traditional methods to counteract IoT botnets and thus calls for finding effective and efficient methods to mitigate such threats. In this work, the network snapshots of IoT traffic infected with two botnets, i.e., Mirai and Bashlite, are studied. Specifically, the collected datasets include network traffic from 9 different IoT devices such as baby monitor, doorbells, thermostat, web cameras, and security cameras. Each dataset consists of 115 stream aggregation feature statistics like weight, mean, covariance, correlation coefficient, standard deviation, radius, and magnitude with a timeframe decay factor, along with a class label defining the traffic as benign or anomalous.</p><p>The goal of the research is to identify a proper machine learning method that can detect IoT botnet traffic accurately and in real-time on IoT edge devices with low computation power, in order to form the first line of defense in an IoT network. The initial step is to identify the most important features that distinguish between benign and anomalous traffic for IoT devices. Specifically, the Input Perturbation Ranking algorithm^[12] with XGBoost^[26]is applied to find the 9 most important features among the 115 features. These 9 features can be collected in real time and be applied as inputs to any detection method. Next, a supervised predictive machine learning method, i.e., Decision Trees, is proposed for faster and accurate detection of botnet traffic. The advantage of using decision trees over other machine learning methodologies, is that it achieves accurate results with low computation time and power. Unlike deep learning methodologies, decision trees can provide visual representation of the decision making and detection process. This can be easily translated into explicit security policies in the IoT environment. In the experiments conducted, it can be clearly seen that decision trees can detect anomalous traffic with an accuracy of 99.997% and takes 59 seconds for training and 0.068 seconds for prediction, which is much faster than the state-of-art deep-learning based detector, i.e., Kitsune^[4]. Moreover, our results show that decision trees have an extremely low false positive rate of 0.019%. Using the 9 most important features, decision trees can further reduce the processing time while maintaining the accuracy. Hence, decision trees with important features are able to accurately and efficiently detect IoT botnets in real time and on a low performance edge device such as Raspberry Pi^[9].</p>

<div> <div> <div> <p>This thesis aims to introduce a system design that supports the user experience of outfit selection, storage, and matching. Clothes are indispensable items in daily human life. Purchasing one's wardrobe has become more affordable. This has allowed people to focus on purchasing more fashionable clothes. Garment shopping has even become a type of social and leisure activity. With the development of internet technology, shopping methods have changed dramatically. However, these seemingly convenient shopping methods also bring unavoidable problems, such as an inability to understand apparel companies' different size standards and the challenge of seeing the details of materials. On the other side, while overemphasizing the convenience of the shopping process, online companies have ignored people's clothes-wearing experience that is the most enjoyable and valuable for customers. This paper introduces an IoT (Internet of Things) design: "Closet Go" including a mobile application and a clip-able camera. "Closet Go" aims to improve customers' daily outfit selection experience by digitalizing their closets and conducting data analysis of customized dressing habits. In this thesis, I present the entire design process: user research, Ideation, UI/UX design, product development, and evaluation. In the research section, potential users were recruited for interviews to discover the current problems in acquiring, selecting, and matching outfits in daily life. The design process section introduces the design development progress and results via user flow, experience map, prototype, and user interface. Finally, the thesis concludes with a heuristics evaluation section that tests the design's usability and experience to refine the project. </p> </div> </div> </div>

Machine learning algorithms that run on Internet of Things sensory data requires high data quality to produce relevant output. By providing data cleaning at the edge, cloud infrastructures performing AI computations is relieved by not having to perform preprocessing. The main problem connected with edge cleaning is the dependency on unsupervised pre-processing as it leaves no guarantee of high quality output data. In this thesis an IoT gateway is extended to provide cleaning and live configuration of cleaning parameters before forwarding the data to a server cluster. Live configuration is implemented to be able to fit the parameters to match a time series and thereby mitigate quality issues. The gateway framework performance and used resources of the container was benchmarked using an MQTT stress tester. The gateway’s performance was under expectation. With high-frequency data streams, the throughput was below50%. However, these issues are not present for its Glava Energy Center connector, as their sensory data generates at a slower pace. / AI4ENERGY

Thesis(M.Sc. (Computer Science)) -- University of Limpopo, 2020 / Spectrum scarcity is one of the major problems affecting the advancement of wireless technology. The world is now entering into a new era called the “Fourth Industrial Revolution” and technologies like the Internet of Things (IoT) and blockchain are surfacing at a rapid pace. All these technologies and this new era need high speed network (Internet) connectivity. Internet connectivity is reliant on the availability of spectrum Channels. The Federal Communication Commission (FCC) has emphatically alluded on the urgency of finding quick and effective solutions to the problem of spectrum scarcity because the available spectrum bands are getting depleted at an alarming rate. Cognitive Radio Ad Hoc Networks (CRAHNs) have been introduced to solve the problem of spectrum depletion. CRAHNs are mobile networks which allow for two groups of users: Primary Users (PUs) and Secondary Users (SUs). PUs are the licensed users of the spectrum and SUs are the unlicensed users. The SUs access spectrum bands opportunistically by switching between unused spectrum bands. The current licensed users do not fully utilize their spectrum bands. Some licensed users only use their spectrum bands for short time periods and their bands are left idling for the greater part of time. CRNs take advantage of the periods when spectrum bands are not fully utilized by introducing secondary users to switch between the idle spectrum bands. The CRAHNs technology can be implemented in different types of routing environments including military networks. The military version of CRAHNs is called Military Cognitive Radio Ad Hoc Networks (MCRAHNs). Military networks are more complex than ordinary networks because they are subject to random attacks and possible destruction. This research project investigates the delays experienced in routing packets for MCRAHNs and proposes a new routing algorithm called Spectrum-Aware Transitive Multicasting On Demand Distance Vector (SAT-MAODV) which has been optimized for reducing delays in packet transmission and increasing throughput. In the data transmission process, there are several levels where delays are experienced. Our research project focuses on Routing Path (RP) delay, Spectrum Mobility (SM) delay and Node Relay (NR) delay. This research project proposes techniques for spectrum switching and routing called Time-Based Availability (TBA), Informed Centralized Multicasting (ICM), Node Roaming Area (NRA) and Energy Smart Transitivity (EST). All these techniques have been integrated into SAT-MAODV. SAT-MAODV was simulated and compared with the best performing algorithms in MCRHANs. The results show that SAT-MAODV performs better than its counterparts

Title: Internet of Things in real estate companies owned by the municipality: A study on how IoT can be used to meet future climate goals Subject: Bachelor's thesis in business administration 15 credits Authors: Hampus Heikkinen and Victor Ifver Kävrestad Keywords: Internet of Things (IoT), Digitized property management, Real estate companies owned by the municipality, Energy consumption, Agenda 2030 Problem statements: What does the usage of Internet of Things look like today and does management control systems have any impact on its use? Will the Internet of Things be accepted and used in the future by real estate companies owned by the municipality?What opportunities are there to use IoT within the companies to achieve the climate goals that must be met by 2030? Purpose: Describe and analyze how municipal real estate companies use Internet of Things in their property management with a focus on the work to improve the energy efficiency. The thesis also wants to find out if companies will use the Internet of Things in the future. Method: We have used the qualitative method in this essay. Four interviews have been conducted through Zoom with people who work at municipal owned real estate companies to gather information on management and business development. Conclusion: Digitization in the real estate industry is far from fully developed which is the reason that the respondents in this study show a relatively low degree of digitization. Management control on the other hand does not hinder these companies but promotes the respondents ability to adapt to digitalization. The study also indicates that the Internet of Things will be accepted in the future. Finally, the study concludes that IoT sensors can be used to reduce energy consumption which will help companies to achieve the climate goals for 2030.

Denna rapport beskriver ett kandidatarbete som utfördes av åtta studenter i kursen TDDD96 - Kandidatprojekt i programvaruutvecklingvid Linköpings universitet våren 2021. Projektet handlade om att skapa ett system för att spåra och visualisera förekomsten av farliga ämnen som kan förekomma på olycksplatser och på så sätt underlätta räddningstjänstens arbete. Resultatet av projektet bestod av en sensorenhet som fästs på räddningspersonalens hjälm. Sensorenheten detekterar halten butan, vätgas, ammoniak, sulfider och bensen i luften och skickar dessa värden tillsammans med GPS och acceleration till en instrumentbräda. Där visas all data och kan användas avbakre led. Rapporten beskriver projektets arbete beställt av forskningsgruppen Ubiquitous Computing and Analytics Group vid Institutionen för Datavetenskap på Linköpings universitet. Teorin kring systemet lägger grunden till förståelse av rapporten, samt hur arbete kan effektiviseras med hjälp av till exempel Scrum, parprogrammering och ärendespårning (eng.issue tracking). Rapporten redovisar även projektets resultat och hur den slutgiltiga produktens hårdvara, server och användargränssnitt fungerar. Till sist presenteras gruppmedlemmarnas individuella bidrag som innehåller djupdykningar inom olika områden med koppling till projektet.

Design is all about visuals, or that is what I have found out during this thesis, from the process materials to the outcome our main entry point to any problem is how will we solve it visually so it’s understandable for the general user. This aspect is problematic in itself due to the fact that we, as humans, understand the world and the things around using all our senses continuously, even though we can forget as visuals are so overpowering. There is a huge opportunity area in exploring our other senses and bringing them back to technology, and this can be seen in works in the past like Tangible Interactions [1] or Natural User Interfaces [2]. But in this moment in time, where all these new technologies like VR/AR and IoT are about to enter our lives and change them forever, this topic is more important than ever. We have already seen what happens when we turn humans into mere machines with some fingers as interactive inputs, and barely any senses to process all the information given to us. Now that these technologies are still young and malleable, we can direct the future to where we want it instead of being guided by the technology itself. To do this we need to reimagine the design process, not reinvent the wheel, but add experts which we currently leave behind and I argue are key to unlock these technologies, experts not only of the technological side of things but on the human side too, like physiotherapists and dancers. Add also people who we never think about when we think of VR like visually impaired users, which could make these technologies inclusive since early on, instead of as an afterthought like we usually do. Not only people, but we also need to add new materials to understand how we use our senses and explore ways that we can understand and explore them differently; like bodystorming and improv theatre because when things aren’t visual, how do you sketch it? A sketch turns into a video about movement. The end result provides a wide breadth of examples of the types of innovations that can come out of using these new design materials, and to open new frontiers. From a VR game with no visuals whatsoever to an AR location based story game, to a home sized multimodal operating system containing several different apps controlled through physical movement. The examples open up the space instead of closing into a single solution. This is just the tip of the iceberg, a hope that others will be inspired by it and continue with this journey that has just started, to guide the future into one that is more technological and at the same time more human than ever before. What we know is that VR does not equate Visual Reality.

Telehealth is an online health care system that is extensively used in the current pandemic situation. Our proposed technique is considered a fog computing-based attack detection architecture to protect IoT Telehealth Networks. As for IoT Telehealth Networks, the sensor/actuator edge devices are considered the weakest link in the IoT system and are obvious targets of attacks such as botnet attacks. In this thesis, we introduce a novel framework that employs several machine learning and data analysis techniques to detect those attacks. We evaluate the effectiveness of the proposed framework using two publicly available datasets from real-world scenarios. These datasets contain a variety of attacks with different characteristics. The robustness of the proposed framework and its ability, to detect and distinguish between the existing IoT attacks that are tested by combining the two datasets for cross-evaluation. This combination is based on a novel technique for generating supplementary data instances, which employs GAN (generative adversarial networks) for data augmentation and to ensure that the number of samples and features are balanced. / Graduate

Due to the influence of information fusion in Industrial Internet of Things (IIoT) environments, there are many problems, such as weak intelligent visual target positioning, disappearing features, large error in visual positioning processes, and so on. Therefore, this paper proposes a weak target positioning method based on multi-information fusion, namely the “confidence interval method”. The basic idea is to treat the brightness and gray value of the target feature image area as a population with a certain average and standard deviation in IIoT environments. Based on the average and the standard deviation, and using a reasonable confidence level, a critical threshold is obtained. Compared with the threshold obtained by the maximum variance method, the obtained threshold is more suitable for the segmentation of key image features in an environment in which interference is present. After interpolation and de-noising, it is applied to mobile weak target location of complex IIoT systems. Using the metallurgical industry for experimental analysis, results show that the proposed method has better performance and stronger feature resolution.