Publication
An approach to the unified management of heterogeneous IoT environments
| Summary: | The Internet of Things (IoT) can be defined as an extension of computer networks and the Internet, to a myriad of both smart and connected devices labelled "things". Traditionally, wireless sensors/actuators form the founding block of the IoT’s perception layer, which core functions are sensing the environment and actuate on it. Thus, IoT systems were initially developed upon hardware electronic components for data acquisition and interaction with the target environment. Later, virtual sensors (also known as Software-Based Sensors (SBSs)) entered into play, consisting of software modules that offer highlevel Application Programming Interfaces (APIs) that could be easily integrated into IoT products and services. A more recent trend in literature for massive data acquisition considers "social sensing", a type of sensing in which people are regarded as data producers, or Human-Based Sensors (HBSs), via their activities in Online Social Networks (OSNs). Having a variety of new ways of collecting data may not be effective if we cannot ensure essential and specific management capabilities of the heterogeneity, in types and forms, of the entities in the perception layer. Our definition of Device Management (DM) relies on the Y.2060 recommendation by the International Telecommunication Union - Telecommunication Standardization Sector (ITUT). DM covers all the set of operations that an Information Technologies (IT) system needs to monitor and interact with the managed entities. The aim of DM is to make sure that the applications running on a given device operate well. Hence, DM must also be extended to all types of "resources" that can be explored in the perception layer, either physical, virtual or social. It is, therefore, crucial to develop management platforms that consider such heterogeneity in "devices", in a unified way and, additionally, that are based on open standards. On the one hand, such platforms have the potential to explore the breadth in the definition of the devices that interact with both physical and virtual worlds. On the other hand, such a solution guarantees interoperability of the management solutions. Typically, the management of IoT devices mainly focuses on well-established electronic-based entities. Well, one may want to change the algorithm of a virtual sensor, on-the-fly, or prevent a social sensor from feeding the backend for a while. In our research, we tried to overcome the challenge of a unified managed for both traditional and new "devices" in the IoT. We leveraged an IoT middleware as the central component to provide the flexibility and scalability of our proposed architecture. Based on a comprehensive survey, we adopted the Future Internet - Ware (FIWARE) and Lightweight Machine-To-Machine (LwM2M), as the middleware and management protocol, respectively, for the applied case studies of the generic architecture we proposed. We also developed two use-cases, namely IoT Student Advisor and Best Lifestyle Analyzer (ISABELA) and 5GOpenclasses, through which we have shown that the management of the referred three types of sensing is feasible from both functional and performance points of view. With the help of our research team, I actively conducted the design and implementation phases of both use-cases to test the models proposed in this thesis. Thus, I will present the degree of success in achieving such a goal and will mention the research opportunities that we opened for future work. |
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| Main Authors: | Armando, Ngombo |
| Subject: | IoT Management Heterogeneity FIWIRE LwM2M ISABELA 5GOpenclasses Heterogeneidade Gestão |
| Year: | 2020 |
| Country: | Portugal |
| Document type: | doctoral thesis |
| Access type: | open access |
| Associated institution: | Universidade de Coimbra |
| Language: | English |
| Origin: | Estudo Geral - Universidade de Coimbra |
| Summary: | The Internet of Things (IoT) can be defined as an extension of computer networks and the Internet, to a myriad of both smart and connected devices labelled "things". Traditionally, wireless sensors/actuators form the founding block of the IoT’s perception layer, which core functions are sensing the environment and actuate on it. Thus, IoT systems were initially developed upon hardware electronic components for data acquisition and interaction with the target environment. Later, virtual sensors (also known as Software-Based Sensors (SBSs)) entered into play, consisting of software modules that offer highlevel Application Programming Interfaces (APIs) that could be easily integrated into IoT products and services. A more recent trend in literature for massive data acquisition considers "social sensing", a type of sensing in which people are regarded as data producers, or Human-Based Sensors (HBSs), via their activities in Online Social Networks (OSNs). Having a variety of new ways of collecting data may not be effective if we cannot ensure essential and specific management capabilities of the heterogeneity, in types and forms, of the entities in the perception layer. Our definition of Device Management (DM) relies on the Y.2060 recommendation by the International Telecommunication Union - Telecommunication Standardization Sector (ITUT). DM covers all the set of operations that an Information Technologies (IT) system needs to monitor and interact with the managed entities. The aim of DM is to make sure that the applications running on a given device operate well. Hence, DM must also be extended to all types of "resources" that can be explored in the perception layer, either physical, virtual or social. It is, therefore, crucial to develop management platforms that consider such heterogeneity in "devices", in a unified way and, additionally, that are based on open standards. On the one hand, such platforms have the potential to explore the breadth in the definition of the devices that interact with both physical and virtual worlds. On the other hand, such a solution guarantees interoperability of the management solutions. Typically, the management of IoT devices mainly focuses on well-established electronic-based entities. Well, one may want to change the algorithm of a virtual sensor, on-the-fly, or prevent a social sensor from feeding the backend for a while. In our research, we tried to overcome the challenge of a unified managed for both traditional and new "devices" in the IoT. We leveraged an IoT middleware as the central component to provide the flexibility and scalability of our proposed architecture. Based on a comprehensive survey, we adopted the Future Internet - Ware (FIWARE) and Lightweight Machine-To-Machine (LwM2M), as the middleware and management protocol, respectively, for the applied case studies of the generic architecture we proposed. We also developed two use-cases, namely IoT Student Advisor and Best Lifestyle Analyzer (ISABELA) and 5GOpenclasses, through which we have shown that the management of the referred three types of sensing is feasible from both functional and performance points of view. With the help of our research team, I actively conducted the design and implementation phases of both use-cases to test the models proposed in this thesis. Thus, I will present the degree of success in achieving such a goal and will mention the research opportunities that we opened for future work. |
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