Browsing by Subject "IoT"

The rise of the Internet of Things (IoT) has brought with itself an unimaginable ease to large-scale collection and sharing of personal data. Such large-scale collection and sharing are often done on the basis of data subject’s consent. Consent enjoys a prominent role in the European data protection framework. Consent has, however, been criticised for not providing individuals with adequate protection in online environments. This problem will only be exacerbated with the rise of IoT as IoT extends the data collection practices of the online environments also to offline environments. The purpose of this thesis is to explore the use of consent in the processing of personal data in the IoT. There are two research questions this thesis aims to answer: i) what are the problems and challenges related to the traditional consent based model in relation to IoT, and ii) is there an alternative way forward to user consent? This will be done through legal doctrinal methodology. However, this thesis will also take an interdisciplinary approach as it also draws from different disciplines than law such as technology, behavioural sciences and economics. This thesis shows that, in digitalized world, consent is neither freely given nor informed; thus, challenging the notion of valid consent. These problems arise from information and power asymmetries that are present between data subjects and controllers. However, IoT also brings with itself a unique set of problems as most IoT devices lack screens and input methods making it hard for individuals to access information and provide consent. Moreover, the unobtrusive and ubiquitous nature of IoT makes data collection activities invisible making it hard to apply transparency principle. It is also predicted that the presence of IoT in public spaces leads to the diminishment of private spaces. In light of this, this thesis discusses some alternative ways forward to user consent. The first approach focuses on improving consent, while the second approach aims to shift the focus away from consent by placing accountability on controllers. While both of these alternatives have appeal, they do not come without challenges. Therefore, more research is needed in the field of IoT and data protection.

Internet of Things (IoT) has the potential to transform many domains of human activity, enabled by the collection of data from the physical world at a massive scale. As the projected growth of IoT data exceeds that of available network capacity, transferring it to centralized cloud data centers is infeasible. Edge computing aims to solve this problem by processing data at the edge of the network, enabling applications with specialized requirements that cloud computing cannot meet. The current market of platforms that support building IoT applications is very fragmented, with offerings available from hundreds of companies with no common architecture. This threatens the realization of IoT's potential: with more interoperability, a new class of applications that combine the collected data and use it in new ways could emerge. In this thesis, promising IoT platforms for edge computing are surveyed. First, an understanding of current challenges in the field is gained through studying the available literature on the topic. Second, IoT edge platforms having the most potential to meet these challenges are chosen and reviewed for their capabilities. Finally, the platforms are compared against each other, with a focus on their potential to meet the challenges learned in the first part. The work shows that AWS IoT for the edge and Microsoft Azure IoT Edge have mature feature sets. However, these platforms are tied to their respective cloud platforms, limiting interoperability and the possibility of switching providers. On the other hand, open source EdgeX Foundry and KubeEdge have the potential for more standardization and interoperability in IoT but are limited in functionality for building practical IoT applications.

Markkinat digitalisoituvat globaalisti kiihtyvällä vauhdilla. EU:n standardointistrategiassa (2022) korostetaan standardien merkitystä digitaalisten sisämarkkinoiden toimivuuden taustalla. Digitaalisten sisämarkkinoiden yhtenä painopistealueena EU:ssa on yhteentoimiviin tieto- ja viestintätekniikoihin perustuva esineiden internet (Internet of Things, IoT). Yhteentoimivuutta tukevilla standardeilla on tärkeä merkitys alalla. Komissio on katsonut niillä mahdollistettavan saumatonta vuorovaikutusta esineiden välillä laitteen valmistajasta riippumatta. Komission mukaan laajamittainen yhteentoimivuus on edellytyksenä alan mittavan taloudellisen potentiaalin esiin tuomisessa. IoT-alan yhteentoimivuutta tukevaa standardointia tarkastellaan tutkielmassa EU:n kilpailuoikeuden näkökulmasta. Kilpailuoikeudessa huomioidaan, että standardoinnista saattaa myös seurata kilpailua rajoittavia vaikutuksia kilpailevien yritysten tehdessä yhteistyötä. Erityisesti markkinoita sulkevia vaikutuksia voi seurata standardeista, joihin sisältyy niiden käytön kannalta essentiaalisia patentteja (standardiessentiaalipatentti, SEP). Tutkielmassa tarkastelun keskiössä on erityisesti IoT:n sektoreista kuluttajien IoT. Komission julkaisemat raportit kuluttajien IoT:n toimialatutkimuksesta, erityisesti loppuraportti keväältä 2022, toimivat keskeisenä lähteenä alan ominaispiirteiden ja standardoinnin tarkastelussa. Tutkielmassa keskitytään eurooppalaiseen standardointiin huomioiden kuitenkin IoT:n kansainvälinen luonne. Tutkielmassa analysoidaan, missä määrin kilpailusäännöt ovat sovellettavissa eurooppalaisiin standardointiorganisaatioihin tai niissä standardointiin osallistuviin yrityksiin. Tutkielmassa edelleen tarkastellaan EU:n kilpailuoikeuden asettamia kriteerejä vapaasta osallistumisesta standardointiin sekä standardin käytettävyydestä kohtuullisin ehdoin. Tutkielmassa analysoidaan SEUT 101 artiklan soveltamista tilanteissa, joissa standardointiorganisaatiot eivät noudata näitä kriteerejä. Vuonna 2023 uudistettujen horisontaalisten suuntaviivojen standardointisopimuksia koskeva luku toimii keskeisenä lähteenä kilpailuoikeudelliselle analyysille. Lisäksi SEP-haltijan lisensointimenettelyjä analysoidaan erityisesti relevantin IoT:hen liittyvän kilpailuoikeudellisen oikeuskäytännön perusteella. Johtopäätöksinä tutkielmassa todetaan ensinnäkin, että kilpailuoikeus soveltuu standardointiin osallistuviin yrityksiin eurooppalaisissa standardointiorganisaatioissa. Toiseksi tutkielmassa havaitaan, että markkinoille potentiaalisesti merkittävistä standardeista ollessa kyse, avoimuuden rajoitukset standardointiin osallistumisessa tai standardin käytössä todennäköisesti rajoittavat kilpailua SEUT 101 artiklan 1 kohdan tavalla. SEUT 101 artiklan 3 kohdan soveltamista arvioitaessa erityisesti painottuu tällaisten standardien käytettävyys. IoT-alan näkökulmasta on tärkeää, että käytettävyyttä suojataan kilpailuoikeudessa tällaisten standardien osalta. Uudistetuissa suuntaviivoissa on toisaalta joustavoitettu standardointiin osallistumisen rajoittamista standardoinnin tehostamiseksi. Muutos heijastaa EU:n standardointipolitiikassa esille tuotua tarvetta eurooppalaisen standardoinnin nopeuttamiseen ja sitä voidaan pitää perusteltuna IoT:n nopea kehitys huomioiden. Kolmanneksi tutkielmassa todetaan, että SEUT 102 artiklalla standardien saatavuutta turvataan tilanteessa, jossa essentiaalipatentin haltija ei ole etukäteisestä sitoumuksestaan huolimatta halukas lisensoimaan FRAND-ehdoin vaan nostaa kieltokanteen. Tarkastellun oikeuskäytännön perusteella jää kuitenkin epäselväksi, turvataanko standardien saatavuutta IoT:n näkökulmasta riittävästi SEUT 102 artiklan keinoin. Epäselvyyksiä liittyy myös lisensointivelvoitteen laajuuteen ja patenttien arvon määritykseen IoT-alalla. Tutkielmassa huomioidaan lopuksi, että EU:ssa suunnitteilla oleva kilpailuoikeutta täydentävä sääntely voi jatkossa kuitenkin osaltaan edistää standardien saatavuutta IoT-alalla kohtuullisin ehdoin.

Ubiquitous sensing is transforming our societies and how we interact with our surrounding envi- ronment; sensors provide large streams of data while machine learning techniques and artificial intelligence provide the tools needed to generate insights from the data. These developments have taken place in almost every industry sector with topics such as smart cities and smart buildings becoming key topical issues as societies seek more sustainable ways of living. Smart buildings are the main context of this thesis. These are buildings equipped with various sensors used to collect data from the surrounding environment allowing the building to adapt itself and increasing its operational efficiency. Previously, most efforts in realizing smart buildings have focused on energy management and au- tomation where the goal is to improve costs associated with heating, ventilation, and air condi- tioning. A less studied area involves smart buildings and their indoor environments especially relative to sub-spaces within a building. Increased developments in low-cost sensor technologies have created new opportunities to sense indoor environments in more granular ways that provide new possibilities to model finer attributes of spaces within a building. This thesis focuses on modeling indoor environment data obtained from a multipurpose building that serves primarily as a school. The aim is to explore the quality of the indoor environment relative to regulatory guidelines and also exploring suitable predictive models for thermal comfort and indoor air quality. Additionally, design science methodology is applied in the creation of a proof of concept software system. This system is aimed at demonstrating the use of Web APIs to provide sensor data to clients that may use the data to render analytics among other insights to a building’s stakeholders. Overall, the main technical contributions of this thesis are twofold: (i) a potential web-application design for indoor air quality IoT data and (ii) an exposition of modeling of indoor air quality data based on a variety of sensors and multiple spaces within the same building. Results indicate a software-based tool that supports monitoring the indoor environment of a building would be beneficial in maintaining the correct levels of various indoor parameters. Further, modeling data from different spaces within the building shows a need for heterogeneous models to predict variables in these spaces. This implies parameters used to predict thermal comfort and air quality are different in varying spaces especially where the spaces differ in size, indoor climate control settings, and other attributes such as occupancy control.

The number of IoT and sensor devices is expected to reach 25 billion by 2030. Many IoT appli- cations, such as connected vehicle and smart factory that require high availability, scalability, low latency, and security have appeared in the world. There have been many attempts to use cloud computing for IoT applications, but the mentioned requirements cannot be ensured in cloud environments. To solve this problem, edge computing has appeared in the world. In edge environments, containerization technology is useful to deploy apps with limited resources. In this thesis, two types of high available Kubernetes architecture (2 nodes with an external DB and 3 nodes with embedded DB) were surveyed and implemented using K3s distribution that is suitable for edges. By having a few experiments with the implemented K3s clusters, this thesis shows that the K3s clusters can provide high availability and scalability. We discuss the limitations of the implementations and provide possible solutions too. In addition, we provide the resource usages of each cluster in terms of CPU, RAM, and disk. Both clusters need only less than 10% CPU and about 500MB RAM on average. However, we could see that the 3 nodes cluster with embedded DB uses more resources than the 2 nodes + external DB cluster when changing the status of clusters. Finally, we show that the implemented K3s clusters are suitable for many IoT applications such as connected vehicle and smart factory. If an application that needs high availability and scalability has to be deployed in edge environments, the K3s clusters can provide good solutions to achieve the goals of the applications. The 2 nodes + external DB cluster is suitable for the applications where the amount of data fluctuate often, or where there is a stable connection with the external DB. On the other hand, the 3 nodes cluster will be suitable for the applications that need high availability of the database even in poor internet connection. ACM Computing Classification System (CCS) Computer systems organization → Embedded and cyber-physical systems Human-centered computing → Ubiquitous and mobile computing