Creating a toolbox for IoT device behaviour analysis using data mining

Abstract. Plenty of devices are connected to the Internet and the number is growing. A lot of data can be extracted from those devices. Using data mining approach that data can be transformed into valuable information. This work analyses data sources and devices in the Internet of Things ecosystem developed by Arm ltd. The ecosystem includes Mbed OS operating system for embedded devices and Pelion Cloud for device management. Data sources available in the ecosystem are mapped and analysed. A toolbox is created for analysing the data with the goal of separating differently behaving devices into separate clusters. Methods used are machine learning based. The system utilises events generated by Pelion and memory usage data gathered on devices. Combining the two data sources produces temporal data describing operations of each device. Using Hidden Markov Models that data is transformed into a similarity matrix describing similarity of devices behaviour. The matrix is then analysed using clustering methods with the purpose of separating devices into groups by behaviour. Dimensionality reduction methods are applied to data and the results are visualised. The test dataset used in this work was small, only 10 devices. The results show some promise and warrant a follow-up study using a larger dataset to further improve the toolbox.Työkalun luonti IoT-laitteiden käyttäytymisen analysointiin datan rikastusmenetelmillä. Tiivistelmä. Internetiin on kytkettynä valtava määrä laitteita ja niitä kytketään jatkuvasti lisää. Kytketyistä laitteista voidaan kerätä suuri määrä dataa. Datan louhintamenetelmillä tuo data voidaan muuntaa arvokkaaksi tiedoksi. Tässä työssä tutkitaan datalähteitä ja laitteita ohjelmistoyhtiö Arm ltd:n kehittämässä esineiden internetin ekosysteemissä. Ekosysteemiin kuuluu Mbed OS käyttöjärjestelmä sulautetuille laitteille ja Pelion Cloud palvelu laitteiden hallintaan. Ekosysteemissä saatavilla olevat datalähteet kartoitetaan ja analysoidaan. Työssä rakennetaan työkalu, jonka tarkoituksena on tunnistaa laitteet, joiden toiminta eroaa muista vastaavista laitteista. Käytetyt menetelmät ovat koneoppimispohjaisia. Työkalu hyödyntää tapahtumia, jotka tallennetaan Pelioniin laitteiden elinkaaren aikana ja muistin käyttömääriä, jotka on kerätty laitteilta. Yhdistämällä nämä datalähteet syntyy aikajana, joka kuvaa laitteen toimintaa. Käyttämällä piilotettuja Markovin malleja aikajana muunnetaan matriisiksi, joka kuvaa laitteiden käyttäytymisen samankaltaisuutta. Ryhmittelymenetelmiä käytetään matriisin analysointiin, tavoitteena jakaa laitteet ryhmiin käyttäytymisen samankaltaisuuden perusteella. Datan ulotteisuutta pienennetään siihen soveltuvilla menetelmillä. Tämän jälkeen tulos visualisoidaan. Testidatan määrä työssä oli pieni, vain 10 laitetta. Tulokset osoittavat jonkin verran lupausta menetelmien toimivuudesta ja oikeuttavat työkalun jatkotutkimuksen isommalla datamäärällä

Internet of Things-aided Smart Grid: Technologies, Architectures, Applications, Prototypes, and Future Research Directions

Traditional power grids are being transformed into Smart Grids (SGs) to address the issues in existing power system due to uni-directional information flow, energy wastage, growing energy demand, reliability and security. SGs offer bi-directional energy flow between service providers and consumers, involving power generation, transmission, distribution and utilization systems. SGs employ various devices for the monitoring, analysis and control of the grid, deployed at power plants, distribution centers and in consumers' premises in a very large number. Hence, an SG requires connectivity, automation and the tracking of such devices. This is achieved with the help of Internet of Things (IoT). IoT helps SG systems to support various network functions throughout the generation, transmission, distribution and consumption of energy by incorporating IoT devices (such as sensors, actuators and smart meters), as well as by providing the connectivity, automation and tracking for such devices. In this paper, we provide a comprehensive survey on IoT-aided SG systems, which includes the existing architectures, applications and prototypes of IoT-aided SG systems. This survey also highlights the open issues, challenges and future research directions for IoT-aided SG systems

Demystifying Internet of Things Security

Break down the misconceptions of the Internet of Things by examining the different security building blocks available in Intel Architecture (IA) based IoT platforms. This open access book reviews the threat pyramid, secure boot, chain of trust, and the SW stack leading up to defense-in-depth. The IoT presents unique challenges in implementing security and Intel has both CPU and Isolated Security Engine capabilities to simplify it. This book explores the challenges to secure these devices to make them immune to different threats originating from within and outside the network. The requirements and robustness rules to protect the assets vary greatly and there is no single blanket solution approach to implement security. Demystifying Internet of Things Security provides clarity to industry professionals and provides and overview of different security solutions What You'll Learn Secure devices, immunizing them against different threats originating from inside and outside the network Gather an overview of the different security building blocks available in Intel Architecture (IA) based IoT platforms Understand the threat pyramid, secure boot, chain of trust, and the software stack leading up to defense-in-depth Who This Book Is For Strategists, developers, architects, and managers in the embedded and Internet of Things (IoT) space trying to understand and implement the security in the IoT devices/platforms

Semantic reasoning on the edge of internet of things

Abstract. The Internet of Things (IoT) is a paradigm where physical objects are connected with each other with identifying, sensing, networking and processing capabilities over the Internet. Millions of new devices will be added into IoT network thus generating huge amount of data. How to represent, store, interconnect, search, and organize information generated by IoT devices become a challenge. Semantic technologies could play an important role by encoding meaning into data to enable a computer system to possess knowledge and reasoning. The vast amount of devices and data are also challenges. Edge Computing reduces both network latency and resource consumptions by deploying services and distributing computing tasks from the core network to the edge. We recognize four challenges from IoT systems. First the centralized server may generate long latency because of physical distances. Second concern is that the resource-constrained IoT devices have limited computing ability in processing heavy tasks. Third, the data generated by heterogeneous devices can hardly be understood and utilized by other devices or systems. Our research focuses on these challenges and provide a solution based on Edge computing and semantic technologies. We utilize Edge computing and semantic reasoning into IoT. Edge computing distributes tasks to the reasoning devices, which we call the Edge nodes. They are close to the terminal devices and provide services. The newly added resources could balance the workload of the systems and improve the computing capability. We annotate meaning into the data with Resource Description Framework thus providing an approach for heterogeneous machines to understand and utilize the data. We use semantic reasoning as a general purpose intelligent processing method. The thesis work focuses on studying semantic reasoning performance in IoT system with Edge computing paradigm. We develop an Edge based IoT system with semantic technologies. The system deploys semantic reasoning services on Edge nodes. Based on IoT system, we design five experiments to evaluate the performance of the integrated IoT system. We demonstrate how could the Edge computing paradigm facilitate IoT in terms of data transforming, semantic reasoning and service experience. We analyze how to improve the performance by properly distributing the task for Cloud and Edge nodes. The thesis work result shows that the Edge computing could improve the performance of the semantic reasoning in IoT

Building the Future Internet through FIRE

The Internet as we know it today is the result of a continuous activity for improving network communications, end user services, computational processes and also information technology infrastructures. The Internet has become a critical infrastructure for the human-being by offering complex networking services and end-user applications that all together have transformed all aspects, mainly economical, of our lives. Recently, with the advent of new paradigms and the progress in wireless technology, sensor networks and information systems and also the inexorable shift towards everything connected paradigm, first as known as the Internet of Things and lately envisioning into the Internet of Everything, a data-driven society has been created. In a data-driven society, productivity, knowledge, and experience are dependent on increasingly open, dynamic, interdependent and complex Internet services. The challenge for the Internet of the Future design is to build robust enabling technologies, implement and deploy adaptive systems, to create business opportunities considering increasing uncertainties and emergent systemic behaviors where humans and machines seamlessly cooperate

Monitoraggio strutturale e ambientale con il Web delle cose

Structural health and Environmental monitoring are recently benefiting from the advancement in the digital industry. Thanks to the emergence of the Internet of Things (IoT) paradigm, monitoring systems are increasing their functionalities and reducing development costs. However, they are affected by a strong fragmentation in the solution proposed and technologies employed. This stale the overall benefits of the adoption of IoT frameworks or IoT devices since it limits the reusability and portability of the chosen platform. As in other IoT contexts, also the structural health and environmental monitoring domain is suffering from the negative effects of, what is called, an interoperability problem. Recently the World Wide Web Consortium (W3C) is joining the race in the definition of a standard for IoT unifying different solutions below a single paradigm. This new shift in the industry is called Web of Things or in short WoT. Together with other W3C technologies of the Semantic Web, the Web of Things unifies different protocols and data models thanks to a descriptive machine-understandable document called the Thing Description. This work wants to explore how this new paradigm can improve the quality of structural health and environmental monitoring applications. The goal is to provide a monitoring infrastructure solely based on WoT and Semantic technologies. The architecture is later tested and applied on two concrete use-cases taken from the industrial structural monitoring and the smart farming domains. Finally, this thesis proposes a layered structure for organizing the knowledge design of the two applications, and it provides evaluation comments on the results obtained.Le pratiche di monitoraggio strutturale e dell'ambiente stanno recemente beneficiando degli avanzamenti nella industria digitale. Grazie alla nascita di tecnologie basate sull'Internet of Things (IoT), i sistemi di monitoraggio hanno migliorato le loro funzionalità base e ridotto i costi di svilippo. Nonostante ciò, queste soluzioni hardware e software sono affette da una forte fragmentazione sia riguardo ai tipi dispositivo sia alle tecnologie usate. Questa fenomeno fa si che i benifici ottenuti utilizzando tecnologie IoT si riducano poichè spesso tali soluzioni mancano di portabilità e adattabilità. Come in altri contesti IoT, anche nel monitoraggio strutturale e ambintale possiamo incorre nel problema tipico della mancanza di interoperabilità tra diverse piattaforme. Recemenete il World Wide Web Consortium (W3C) ha iniziato a lavorare ad uno standard per unificare le maggiori tecnologie IoT sotto un unico paradigma. Questo nuova corrente è chiamata il Web of Things o in breve WoT. Assieme ad altre tecnologie del W3C come il Semantic Web, il Web of Things astrae differenti protocolli e middleware grazie ad un documento descritivo interpretabili dalle macchine chiamato Thing Description. Questo documento vuole esplorare come questo nuovo paradigma influenzi il mondo del monitoraggio strutturale e ambientale. In particolare vuole verificare se l'utilizzo di tecnologie puramente basate su WoT e Semantic Web possa migliorare la portabilità di un applicazione di monitoraggio. In concreto propone un architetuttura software poi implementata in due casi d'uso reali presi dal mondo dello smart farming e monitoraggio di strutture industriali. Infine, la tesi, propone un organizzazione a layer del modello dei dati e una valutazione dei risultati ottenuti

Building the Future Internet through FIRE

The Internet as we know it today is the result of a continuous activity for improving network communications, end user services, computational processes and also information technology infrastructures. The Internet has become a critical infrastructure for the human-being by offering complex networking services and end-user applications that all together have transformed all aspects, mainly economical, of our lives. Recently, with the advent of new paradigms and the progress in wireless technology, sensor networks and information systems and also the inexorable shift towards everything connected paradigm, first as known as the Internet of Things and lately envisioning into the Internet of Everything, a data-driven society has been created. In a data-driven society, productivity, knowledge, and experience are dependent on increasingly open, dynamic, interdependent and complex Internet services. The challenge for the Internet of the Future design is to build robust enabling technologies, implement and deploy adaptive systems, to create business opportunities considering increasing uncertainties and emergent systemic behaviors where humans and machines seamlessly cooperate

PIS: IoT & Industry 4.0 Challenges

International audienceIn the era of Industry 4.0, digital manufacturing is evolving into smart manufacturing. This evolution impacts companies in three main areas: organization, people, and technologies. This chapter analyzes the Internet of Things (IoT) and Cyber-Physical Systems (CPS)—key technologies transforming the physical world into a digitalized physical world. IoT and CPS provide factories with sensing capabilities, perform data and context capture and allow them to act/react to optimize the value chain. We survey the recent state-of-the-art development of the Industrial Internet of Things (IIoT)—also known as IoT and CPS in the context of Industry 4.0, from a protocol, architecture, and standard point-of-view. We also explore key challenges and future research directions for extensive industrial adoption of these technologies

Internet of Things From Hype to Reality

The Internet of Things (IoT) has gained significant mindshare, let alone attention, in academia and the industry especially over the past few years. The reasons behind this interest are the potential capabilities that IoT promises to offer. On the personal level, it paints a picture of a future world where all the things in our ambient environment are connected to the Internet and seamlessly communicate with each other to operate intelligently. The ultimate goal is to enable objects around us to efficiently sense our surroundings, inexpensively communicate, and ultimately create a better environment for us: one where everyday objects act based on what we need and like without explicit instructions