ẢN SORBONNE UNIVERSITÉ CRÉATEURS DE FUTURS EUR ECOM DEPUIS 1257 Sophia Antipotis THESE DE DOCTORAT DE SORBONNE UNIVERSITE Spécialité Informatique et Réseaux Présentée par M. KIM-HUNG LE Pour obtenir le grade de DOCTEUR de SORBONNE UNIVERSITE Sujet de la thése Mécanismes d’interopérabilité pour les applications industrielles de I'Internet des Objets et la Ville Intelligente Soutenuve le 01 Avril 2019 Devant le jury composé de : Prof. Christian Bonnet, Professeur, Eurecom, Sophia - France Directeur de thése Prof. Paolo Papotti, Professeur, Eurecom, Sophia — France Directeur de thése Prof.
Karine Zeitouni, Professeur, Versailles - France Rapporteur Dr. Walid Dabbous, Directeur de Recherche, INRIA, Sophia - France Rapporteur Prof. Marcelo Dias de Amorim, Professeur, CNRS, Paris - France Examinateur M. Francois Hamon, Chercheur, Greencityzen, Marseille — France Examinateur S UNIVERSITE TM SORBONNE CREATEURS DE FUTURS DEPUIS 1257 INTEROPERATION MECHANISM FOR INDUSTRIAL INTERNET OF THINGS APPLICATIONS AND SMART CITY.
Kim-Hung Le A doctoral dissertation submitted to: Sorbonne University In Partial Fulfillment of the Requirements for the Degree of: Doctor of Philosophy Specialty : COMPUTER SCIENCE AND NETWORKING Academic Supervisors: Prof. Christian BONNET - Eurecom, Sophia - France Prof. Paolo PAPOTTI - Eurecom, Sophia - France Industrial Supervisor: M. Francois HAMON - Greencityzen, Marseille - France Acknowledgements First of all, I would like to extend my sincere thanks to my advisors Prof.
Christian Bonnet and Prof. Paolo Papotti for their valuable supports and brilliant ideas. Throughout this thesis, they have always spent the time in their busy schedule to guide and encourage my research activities. I also very much appreciate their competences that made this thesis work a success.
I would also like to thank M. Francois Hamon and M. Alexandre Boundone, my managers in Greencityzen company, who helped me shape my career and showed me how to transform my mistakes into skills. I really appreciate everything they helped me in both professional and personal life.
A special warm thank to M. Datta Soumya who helped me so much with his stimulating technical discussions and constructive publication reviewing. I am grateful to the commit- tee members of my jury, Prof. Marcelo Dias de Amorim, Prof.
Karine Zeitouni, and Dr. Walid Dabbous for their valuable inputs and time spent reading this thesis. I would like to express my appreciation to my colleagues and friends at Greencityzen and Eurecom, for all the unforgettable enjoyable moments and their helps. I also wish to extend my warmest thanks to all my friends in France and Vietnam for all the wonderful time we spend together.
Finally, last but not least, I want to express my special gratitude to my parents, my fiancee for their unconditional support, love and trust. They, together with another members in my big family, make my life full of kindness and happiness with their encouragement. Abstract With the rapid growth of Internet technologies as well as the explosion of connected ob- jects, Internet of Things (IoT) is considered an Internet revolution that positively affects several life aspects. However, in a large-scale deployment like a smart city scenario, billions of IoT devices generate a huge volume of data that must be processed.
The integration of ToT solutions and cloud computing, namely cloud-based IoT, is a crucial concept to meet these demands. In this context, the enormous storage and computation capabilities make the cloud-based IoT a valuable solution to deal with a large amount of IoT data. However, two major challenges of the cloud-based IoT are interoperability and reliability. They come from the fact that IoT is typically characterized by heterogeneous devices, with constraints in storage, processing and communication capabilities.
Moreover, there are no uniform standards in most IoT components such as devices, platforms, services, and applications. In this thesis, our main objective is to deal with the interoperability and reliability issues that arise from large-scale deployment|!| The proposed solutions spread over architectures, models, and algorithms, ultimately covering most of the layers of the oT architecture. At the communication layer, we introduce a method to interoperate heterogeneous IoT connections by using a connector concept. We then propose an error and change point detection algorithm powered by active learning to enhance IoT data reliability.
To maximize usable knowledge from this cleaned data and make it more interoperable, we introduce a virtual sensor framework that simplifies creating and configuring virtual sensors with programmable operators. Furthermore, we provide a novel descriptive language, which semantically describes groups of Things. To ensure the device reliability, we propose an algorithm that minimizes energy consumption by real-time estimating the optimal data collection. The efficiency of our proposals has been practically demonstrated in a cloud- based IoT platform of a start-up company.
1A huge number of devices including various device types are deployed over a large geographical area. Contents Acknow lec igements| [Abstract] (Contents| [List of Figures} eee List of Tables).1 Motivation and Problem Statement|.2 Thesis Contributions and Outline I Background Analysis| 2 Reference Technologies] 2.1 Internet of Things Overview and Related Concepts] .2 Web of Things and Semantic Web of Things 2.3 Massive Internet of Things}.4 Interoperability in loT|.3 Data Outlicrs Detection] [2.6 Energy-efficiency in ToT Devices} 2.1 _Energy-efficiency Definition] [2.2 Device Energy Consumption] .1 Interoperability in loTÏ 3. iv Contents II Interoperation in IoT| 31 4_ Ân Industrial IoT Framework to Interoperate IoT Device Connections using Connectors| 4. 3 ToT Framework for Connectoi 6_ Deployment [£4 Evaluation].
5 A Scalable IoT Framework to Maximize Data Knowledge using Virtual Sensor} 42 Thiodicio].3_— Virtual Sensor Framework] 45 45 46 ö. A fe ee 52 6_ WoT-AD: A Descriptive Language for Group of Things] 53 [G1 Thưoduetion]l.2 Web of Things Framework].3 WoT Asset Description]. eee ee 58 58 59 61 62 IIT Reliability in IoT| 63 7 An Active Learning Method for Errors and Events Detection] 65 7.4 Tnverse Nearest Neighbor] Contents v Detection using Active Learning] .2 nomaly Candidate Estimation| 73 73 7.4 Effectiveness of Active Learning] 83 ff n Q 84 [7-6.6__Enhancing Repairing Qualt].7 Comparison of Quality] 86 (7. 87 8 An Energy Efficient Sampling Algorithm 90 [8.2 gorit hm ETX len 93 94 S13 valuative 95 Bad Results} 96 vs ao 9 {9 Conclusions and Outlook) (9.2 Perspectives and Future work] A Résumé de la Thése en Frangais [A.2 Travaux connexes at défis| utilisant des capteurs virtuels| [A.3 Un langage descriptif pour un groupe d objet: A Fiabilité dans TIoT].
ee vi Contents A.1 Une méthode d’apprentissage actif pour la détection anomalies] .2- Un algorithme d’échantillonnage économe en énergie |. - „„„ T16 118 B_ List of Publicationsi 120 [C Tndexes] 121 IBibliography] 124 List of Figures 1.1 Our contributions positioned in the IoT functional view|.1 The Evolution of Internet of Things solutions.2 The cloud computing overview].3 The levels of interoperability.4 The platform iteroperability 19 4.1 The architecture overview of our ramework.3 The framework in operation| 39 [fa The operational Uingram] 39 [4.5 Connector generation performance} 41 46 AT 49 5.4 The generating high-level mformation process].5 The framework’s performance].6 The effect of our enhancement in scalability and performance]. SS Vee WN Mi <M. 57 [62 interaction The WoT-AD section.3 The Asset_architecture overview] .4 The operation model overviewW.1 An example of IoT data (top plot) and detection results for four algorithms| 66 7.2 Comparing Inverse Nearest Neighbor (INN) and K-Nearest Neighbor (KNN).3 n Example of INN].4 Ân example of Synthetic datasets].6 Comparing the query benefit over varying anomaly and change point per- centages in datasets].
ee 81 =" Varying the percentages of anomaly and change points over synthetic datasets. From left to right, the two plots show: (a) Anomaly detecti Change pomt detection quality].8 Varying confidence settings: (a) Anomaly and change point detection accu- racy; (b) The number of query).10 Comparing the ei iveness of INN and KNN in two cases: before and af- ter performing active learning. From left to right, the two plots show: (a) Anomaly detection quality over Yahoo datasets; (b) Anomaly and change point detection quality over Synt hetic datasets|.11 An optimization of IMR.12 Comparing de ver all sets.) ee viii List of Figures over all datasets] ©.1 Varying user desires over DO datasets with window size 8.2 Varying user desires over loT datasets with window size 8.3 ‘arying user desires an window sizes over atasets.4 Varying user desires and window sizes over loi datascts| [Ã;T TParehiteeturc du framework] [A.2_Apergu de Tarchitecture du framework] A.3Apergu du modele dactif ou Asi |A.4_ Apergu de l'achitecture de l'actif ou Asset]. List of Tables CABD’s results over percentage Glossary List of Abbreviations and Acronyms 3GPP 3rd Generation Partnership Project 5G Fifth Generation API Application Programming Interface AR Auto Regression ARX Auto Regressive with Exogenous Input CEB Cloud Edge Beneath CoAP Constrained Application Protocol CoRE Constrained RESTful environment css Cascading Style Sheets DDL Device Description Language DNS Domain Name Server EWMA Exponentially Eighted Moving Average EXI Efficient XML Interchange GSN Global Sensor Netwrok HTML Hypertext Markup Language TaaS Infrastructures as a Servi IEEE Infrastructures as a Service IERC International Energy Research Centre IETF Internet Engineering Task Force IMR Iterative Minimum Repairing IoE Internet of Everythings loT Tnternet of Things IoT-VN Internet of Things Virtual Network IP Internet Protocol ITU Committed to Connecting the World JSON JavaScript Object Notation LDF Logical Data Flow LLAP Lightweight Local Automation Protocol LoRa Short for Long Range LPWAN Low Power Wide Area Network mDNS Multicast Domain Name Server MIoT Massive Internet of Things MQTT Message Queuing Telemetry Transport Glossary xi NanoIP Nano Internet Protocol NB-IOT Narrow Band Internet of Things NFV Network Function Virtual- ization NIST National Institute of Standards and Technology OASA Online Adaptive Sampling Algorithm OGC Open Geospatial Consortium OSGi Open Service Gateway Initiative OSI Open Systems Interconnection Model PaaS Platform as a Service RFID Radio-frequency Identification SASL Simple Authentication and Security Layer SDN Software Defined Networking SGS Semantic Gateway as a Service SMA Simple Moving Average SOA Service Oriented Architecture SSW Semantic Sensor Web sVSF Scalable Virtual Sensor Framework SWoT Semantic Web of Things TSMP Time Synchronized Mesh Protocol UNB Ultra Narrow Band UPnP Universal Plug and Play URL Uniform Resource Locator VoIP Voice over Internet Protocol VS Virtual Sensor VSE Virtual Sensor Editor W3C World Wide Web Consortium WoT Web of Things WoT-AD Web of Things Asset Description WoT-TD Web of Things Things Description WSN Wireless Sensor Network XML Extensible Markup Language XMPP Extensible Messaging and Presence Protocol 1 Introduction 1.1 Motivation and Problem Statement Internet of Things (IoT), also known as Internet of Everything (IoE), is a novel paradigm that rapidly gains vast attention in the Internet era.
The essential idea of IoT is the inter-networking of variety of “ Things” through unique addressing schemes, where “Things” represent precisely identifiable objects [I] - such sensors, actuators, connected tags, and mobile phones. The IoT aims to provide a smart environment by bringing the Things from the physical world into the digital world. In other words, IoT is not only connecting the Things by using the Internet but it is also enabling data exchange []. Ideally, everyone can update in real-time the information or status of any Thing via IoT applications and services.
If necessary, adaptive decisions according to predefined schemes are sent to con- trol the Things. For example, in a smart office scenario, the temperature collected from the sensors and actuators in the office is sent to a collection point (e., gateway, central server), where the office workers could easily access via applications and services. If the temperature is higher than a defined threshold, a command will be automatically sent to the cor sponding actuator to turn on the air conditioner. It is not surprising that IoT is considered as a current revolution of the Internet that posi- tively affects several real-life aspects.
From an individual user view, the IoT enhances the life quality by offering several intelligent services. A typical example of such services is an intelligent transportation system. Vehicles and roads equipped with sensors and actuators could provide detailed information about the surrounding context to help drivers better navigate and thus improve safety [3].