Internet of Things (IoT) is a revolutionary paradigm approaching both industries and consumers everyday life. It refers to a network of addressable physical objects that contain embedded sensing, communication and actuating technologies, to sense and interact with the environment where being deployed. It can be considered as a modern expression of Mark Weiser's vision of ubiquitous computing where tiny networked computers become part of everyday objects, fusing together the virtual world and the physical word.
Recent advances in hardware solutions have led to the emergence of powerful wireless IoT systems that are entirely energy-autonomous. These systems extract energy from their environment and operate intermittently, only as power is available. Battery-less sensors present an opportunity for the pervasive wide-spread of remote sensor deployments that require little maintenance and have low cost. As the number of IoT endpoint grows -- industry forecast trillions of connected smart devices in the next few years -- new challenges to program, manage and maintain such a huge number of connected devices are emerging. Web technologies can significantly ease this process by providing well-known patterns and tools - like cloud computing - for developers and users. However, the existing solutions are often too heavyweight or unfeasible for highly resource-constrained IoT devices.
This dissertation presents a comprehensive analysis of two of the biggest problems that the IoT is currently facing: R1) How are we going to provide connectivity to all these devices? R2) How can we improve the quality of service provided by these tiny autonomous motes that rely only on limited energy scavenged from the environment?
The first contribution is the study and deployment of a Low-Power Wide-Area-Network as a feasible solution to provide connectivity to all the expected IoT devices to be deployed in the following years. The proposed technology offers a novel communication paradigm to address discrete IoT applications, like long-range (i.e., kilometers) at low-power (i.e., tens of mW). Moreover, results highlight the effectiveness of the technology also in the industrial environment thanks to the high immunity to external noises.
In the second contribution, we focus on smart metering presenting the design of three smart energy meters targeted to different scenarios. The first design presents an innovative, cost-effective smart meter with embedded non-intrusive load monitoring capabilities intended for the domestic sector. This system shows an innovative approach to provide useful feedback to reduce and optimize household energy consumption. We then present a battery-free non-intrusive power meter targeted for low-cost energy monitoring applications that lower both installation cost due to the non-intrusive approach and maintenance costs associated to battery replacement. Finally, we present an energy autonomous smart sensor with load recognition capability that dynamically adapts and reconfigures its processing pipeline to the sensed energy consumption. This enables the sensor to be energy neutral, while still providing power consumption information every 5 minutes.
In the third contribution, we focus on the study of low-power visual edge processing and edge machine learning for the IoT. Two different implementations are presented. The first one discusses an energy-neutral IoT device for precision agriculture, while the second one presents a battery-less long-range visual IoT system, both leveraging on deep learning algorithms to avoid unnecessary wireless data communication. We show that there is a clear benefit from implementing a first layer of data processing directly in-situ where the data is acquired, providing a higher quality of service to the implemented application.
| Identifer | oai:union.ndltd.org:unitn.it/oai:iris.unitn.it:11572/274371 |
| Date | 17 September 2020 |
| Creators | Nardello, Matteo |
| Contributors | Nardello, Matteo, Brunelli, Davide |
| Publisher | Università degli studi di Trento, place:Trento |
| Source Sets | Università di Trento |
| Language | English |
| Detected Language | English |
| Type | info:eu-repo/semantics/doctoralThesis |
| Rights | info:eu-repo/semantics/openAccess |
| Relation | firstpage:1, lastpage:168, numberofpages:168 |
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