With the emergence of the Internet of things (IoT) networks, the replacement of batteries for IoT devices became challenging. In particular, the battery replacement is more expensive and cumbersome for scenarios where there are many IoT devices; or where the IoT devices are in unreachable locations; or when they have to be replaced often. Some IoT devices might be lost or forgotten, and there is a risk of hazardous chemicals leakage and e-waste in large scale in nature. Radio frequency (RF) wireless power transfer (WPT) is an alternative technology for powering those devices. It has been shown that only less than one millionth of the transmitted energy is absorbed by the receivers, the rest is absorbed by the objects in the environment. We can utilize the existing infrastructure for wireless communications such as base stations (BS) to charge IoT devices. The present work is devoted to analyze the feasibility and limitations of the battery-less operation of IoT devices with RF WPT technology and energy harvesting from existing infrastructure for wireless communications. We study the indoor and outdoor scenarios for powering of IoT devices. In the first scenario, we consider an outdoor environment where an IoT device periodically harvests energy from an existing BS and transmits a data packet related to the sensor measurement under shadow fading channel conditions. We analyze the limits (e.g., coverage range) of energy harvesting from a BS for powering IoT devices. We characterize the "epsilon-coverage range, where" is the probability of the coverage. Our analysis shows a tradeoff between the coverage range and the rate of sensor measurements, where the maximal "epsilon-coverage range is achieved as the sensor measurement rate approaches zero. We demonstrate that the summation of the sleep power consumption and the harvesting sensitivity power of an IoT device limits the maximal "epsilon-coverage range. Beyond that range, the IoT device cannot harvest enough energy to operate. The desired rate of the sensor measurements also significantly impacts the "epsilon-coverage range. We also compare the operational domain in terms of the range and measurement rate for the WPT and battery-powered technologies. In the second scenario, we consider the remote powering of IoT devices inside an aircraft. Sensors currently deployed on board have wired connectivity, which increases weight and maintenance costs for aircraft. Removing cables for wireless communications of sensors on board alleviates the cost, however, the powering of sensors becomes a challenge inside aircraft. We assume that the IoT devices have fixed and known locations inside an aircraft. The design problem is to minimize the number of WPT transmitters given constraints based on the cabin geometry and duty cycle of the IoT devices. We formulate a robust optimization problem to address the WPT system design under channel uncertainties. We also derive an equivalent integer linear programming and solve that for an optimal deployment to satisfy the duty cycle requirements of the cabin sensors. / <p>QC 20220223</p><p></p>

Drömmen om ett smart hem som är helt automatiserat är snart inte en dröm längre. Uppkopplade enheter kan anpassa sig efter sin omgivning och efter givna situationer, vilket exempelvis gör det möjligt för kökslamporna att tändas när garageporten öppnas. Vidare har smarta hem möjligheten att öka livskvaliteten för användare genom att anpassa sig efter deras vanor och beteenden. Om enheterna ska kunna anpassa sig efter användarna och sin omgivning krävs det oerhört mycket information, som i fel händer skulle kunna få förödande konsekvenser för användaren. Dock finns det brister i säkerheten bland enheter och vissa produceras utan säkerhet i åtanke. Det finns flertalet säkerhetsrisker med smarta hem och studien har fokuserat på säkerhetsrisker med fokus på autentisering och beteendeanpassning, med ett användarperspektiv, då det är något som saknas i befintlig litteratur. Frågeställningen som behandlas i studien är: ”Hur ser användare av uppkopplade-enheter i smarta hem på säkerhet utifrån autentisering och beteendeanpassning?”. För att kunna besvara denna frågeställning har en kvantitativ metod tillämpats. En webbenkät utformades och testades i en pre-pilot studie och sedan i en pilotstudie innan den publicerades. Frågorna i enkäten har utvecklats från den teori som presenteras i det teoretiska ramverket. Resultatet visar på att användarna har en hög tilltro till säkerheten hos sina enheter och att en majoritet inte är oroliga över säkerhetsrisker kopplat till autentisering. Respondenterna är mer oroliga över säkerhetsrisker associerade med beteendeanpassning än med autentisering. De respondenterna med hög tillit tenderar att vara villiga att ge ut mer privat information än de med låg tillit. En majoritet är också positivt inställda till att lämna ut mer information för ökad bekvämlighet samt att enheterna använder lagrad information för ökad bekvämlighet.

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

This thesis investigates the possibility of connecting an embedded device, STM32 B-L4S5I-IOT01A IoT discovery node, to a Eclipse Arrowhead framework local cloud.This thesis also examines the benefits of using the Eclipse Arrowhead framework compared to its competitors Amazon Web Services and Microsoft Azure. The world is entering a new industrial revolution, often referred to as Industry 4.0, moving towards a more decentralized and software-oriented means of production.This fourth industrial revolution incorporates System of Systems, Cyber-Physical Systems, and embedded software technologies. One of the internet-based industrial solutions is the Eclipse Arrowhead framework. The Eclipse Arrowhead framework contains many examples in various promgramming languages and technologies but lacks an example of a specific piece of hardware connecting to a local Eclipse Arrowhead cloud.Therefore, a project with the clear intent to showcase both the capabilities and possibilities of Cyber-Physical systems and the Eclipse Arrowhead framework is needed. The system this thesis implements consists of three major parts: the stm32 board, a Python flask app, and the Eclipse Arrowhead framework.The main objective of the Eclipse Arrowhead framework is to connect the consumer and the provider in a safe and structured way.The provider is built with C/C++ using ARMs' mbed os.  The response time of the different frameworks, Eclipse Arrowhead framework and Amazon Web Services, was measured.We made a thousand attempts to form an adequate basis for an average response time. In addition to presenting the average response time, we calculated the maximum and minimum response times to understand the different frameworks' performance further.  The thesis shows some benefits in response time when running an Eclipse Arrowhead framework local cloud instead of using a remote service such as Amazon Web Services. Average response time decreased by 17.5 times while running an Eclipse Arrowhead framework local cloud.Maximum and minimum response times decreased by 1.9 and 134 times, respectively.

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.