IoT Enabled Smart Security Framework for 3D Printed Smart Home

Recently, smart home design using Internet of Things (IoT) technology has become a growing industry. Since security is the most important element of the smart home design, the project aims to design a 3D printed smart home with a focus on the security features that would meet the security design of futuristic real homes. The surveillance system of traditional smart home is separated from the door lock system. This project innovatively integrates and coordinates them through the facial recognition algorithms, which forms the entry system of this design. The overall system can be divided into two subsystems (parts), which are the sensing and actuation system (PART I) and the entry system (PART II). PART I includes various sensors and actuators to ensure the security of home, including combustible gas sensor, air quality sensor and temperature & humidity sensor. When anomalies are detected by sensors, actuators such as ventilator, buzzer and LEDs start to work. In PART II, the PIR motion sensor is utilized to detect the person to activate the facial recognition step. Facial recognition algorithm (LBPH algorithm) is implemented for person classification, which is used in selecting the duration of recording for the surveillance system. The surveillance system could select not to record for the occupants or different levels of recording for each occupant based on the confidence of recognition. The project outcomes a 3D printed smart home with a door lock system, a surveillance system, and a sensing & actuation network, which accomplishes the security features in perception and network layer of IoT system design

Image classification for edge-cloud setting: a comparison study for OCR application

The increasing number of smart devices has led to a rise in the complexity and volume of the image generated. Deep learning is an increasingly common approach for image classification, a fundamental task in many applications. Due to its high computational requirements, implementation in edge devices becomes challenging. Cloud computing serves as an enabler, allowing devices with limited resources to perform deep learning. For cloud computing, however, latency is an issue and is undesirable. Edge computing addresses the issue by redistributing data and tasks closer to the edge. Still, a suitable offloading strategy is required to ensure optimal performance with methods such as LeNet-5, OAHR, and Autoencoder (ANC) as feature extractors paired with different classifiers (such as artificial neural network (ANN) and support vector machine (SVM)). In this study, models are evaluated using a dataset representing Optical Character Recognition (OCR) task. The OCR application has recently been used in many task-offloading studies. The evaluation is based on the time performance and scoring criteria. In terms of time performance, a fully connected ANN using features from the ANC is faster by a factor of over 60 times compared to the fastest performing SVM. Moreover, scoring performance shows that the SVM is less prone to overfit in the case of a noisy or imbalanced dataset in comparison with ANN. So, adopting SVM in which the data distribution is unspecified will be wiser as there is a lower tendency to overfit. The training and inference time, however, are generally higher than ANN

Low-power techniques for wireless gas sensing network applications: pulsed light excitation with data extraction strategies

Aquesta tesi està enfocada en dues línies d'investigació. La primera aborda el desenvolupament d'una metodologia basada en llum polsada per modulació de sensors químic-resistius per a l'extracció d'informació del senyal transitòri, i la segona planteja la implementació d'una xarxa sense fils de sensors (WSN) basada en tecnologia LoRa per al monitoratge de la qualitat de l'aire (AQM) i la detecció d'esdeveniments de fuita de gasos. Aquest document està estructurat en quatre capítols organitzats de la següent manera: el Capítol 1 presenta l'estat de l'art, una introducció als mecanismes de millora de l'comportament dels sensors químic-resistius, així com una introducció a la implementació de xarxes sense fils de sensors per a la monitorització de la qualitat de l'aire; el Capítol 2 està compost pels dos articles publicats relacionats amb la metodologia basada en la modulació utilitzant llum polsada per a l'extracció d'informació del senyal transitòria de sensors químic-resistius; el Capítol 3 presenta l'article publicat relacionat amb la implementació d'una WSN per a AQM; el Capítol 4 presenta les conclusions derivades dels resultats obtinguts durant el desenvolupament de el projecte de tesi i les recomanacions per al treball futur associat a la continuïtat dels principals resultats d'aquesta tesiLa presente tesis está enfocada en dos líneas de investigación, La primera aborda el desarrollo de una metodología basada en luz pulsada para modulación de sensores químico-resistivos para la extracción de información de la señal transitoria; y la segunda plantea la implementación de una red inalámbrica de sensores (WSN) basada en tecnología LoRa para la monitorización de la calidad del aire (AQM) y la detección de eventos de fuga de gases. Este documento está estructurado en cuatro capítulos organizados de la siguiente forma: el Capítulo 1 presenta el estado del arte, una introducción a los mecanismos de mejora del comportamiento de los sensores químico-resistivos, así como una introducción a la implementación de redes inalámbricas de sensores para la monitorización de la calidad del aire; el Capítulo 2 está compuesto por los dos artículos publicados relacionados con la metodología basada en la modulación utilizando luz pulsada para la extracción de información de la señal transitoria de sensores químico-resistivos; el Capítulo 3 presenta el artículo publicado relacionado con la implementación de una WSN para AQM; el Capítulo 4 presenta las conclusiones derivadas de los resultados obtenidos durante el desarrollo de el proyecto de tesis y las recomendaciones para el trabajo futuro asociado a la continuidad de los principales resultados de esta tesis.The present thesis project is focused in two different yet related research lines. The first one addresses the development of a pulsed light-based chemiresistive sensor modulation methodology for transient information extraction. The second research line developed deals with the implementation of a LoRa-based portable, scalable, low-cost, and low power Wireless Sensor Network (WSN) for Air Quality Monitoring (AQM) and gas leakage events detection. This document is structured in four Chapters organized as follows: Chapter 1 presents the state of the art, an introduction to sensing performance enhancement and transient data extraction methods, as well as an introduction to the implementation of WSN for AQM; Chapter 2 is composed of the two published paper related to the pulsed light modulation methodology for transient information extraction; Chapter 3 presents the published paper related to the implementation of a LoRa-based WSN for AQM; Chapter 4 states the conclusions derived from the results obtained during this thesis project and the recommendations for the future work associated to the continuity of this thesis findings

Resource Allocation Framework in Fog Computing for the Internet of Things Environments

Fog computing plays a pivotal role in the Internet of Things (IoT) ecosystem because of its ability to support delay-sensitive tasks, bringing resources from cloud servers closer to the “ground” and support IoT devices that are resource-constrained. Although fog computing offers some benefits such as quick response to requests, geo-distributed data processing and data processing in the proximity of the IoT devices, the exponential increase of IoT devices and large volumes of data being generated has led to a new set of challenges. One such problem is the allocation of resources to IoT tasks to match their computational needs and quality of service (QoS) requirements, whilst meeting both task deadlines and user expectations. Most proposed solutions in existing works suggest task offloading mechanisms where IoT devices would offload their tasks randomly to the fog layer or cloud layer. This helps in minimizing the communication delay; however, most tasks would end up missing their deadlines as many delays are experienced during offloading. This study proposes and introduces a Resource Allocation Scheduler (RAS) at the IoT-Fog gateway, whose goal is to decide where and when a task is to be offloaded, either to the fog layer, or the cloud layer based on their priority needs, computational needs and QoS requirements. The aim directly places work within the communication networks domain, in the transport layer of the Open Systems Interconnection (OSI) model. As such, this study follows the four phases of the top-down approach because of its reusability characteristics. To validate and test the efficiency and effectiveness of the RAS, the fog framework was implemented and evaluated in a simulated smart home setup. The essential metrics that were used to check if round-trip time was minimized are the queuing time, offloading time and throughput for QoS. The results showed that the RAS helps to reduce the round-trip time, increases throughput and leads to improved QoS. Furthermore, the approach addressed the starvation problem, a phenomenon that tends to affect low priority tasks. Most importantly, the results provides evidence that if resource allocation and assignment are appropriately done, round-trip time can be reduced and QoS can be improved in fog computing. The significant contribution of this research is the novel framework which minimizes round-trip time, addresses the starvation problem and improves QoS. Moreover, a literature reviewed paper which was regarded by reviewers as the first, as far as QoS in fog computing is concerned was produced

Resource Allocation Framework in Fog Computing for the Internet of Things Environments

Fog computing plays a pivotal role in the Internet of Things (IoT) ecosystem because of its ability to support delay-sensitive tasks, bringing resources from cloud servers closer to the “ground” and support IoT devices that are resource-constrained. Although fog computing offers some benefits such as quick response to requests, geo-distributed data processing and data processing in the proximity of the IoT devices, the exponential increase of IoT devices and large volumes of data being generated has led to a new set of challenges. One such problem is the allocation of resources to IoT tasks to match their computational needs and quality of service (QoS) requirements, whilst meeting both task deadlines and user expectations. Most proposed solutions in existing works suggest task offloading mechanisms where IoT devices would offload their tasks randomly to the fog layer or cloud layer. This helps in minimizing the communication delay; however, most tasks would end up missing their deadlines as many delays are experienced during offloading. This study proposes and introduces a Resource Allocation Scheduler (RAS) at the IoT-Fog gateway, whose goal is to decide where and when a task is to be offloaded, either to the fog layer, or the cloud layer based on their priority needs, computational needs and QoS requirements. The aim directly places work within the communication networks domain, in the transport layer of the Open Systems Interconnection (OSI) model. As such, this study follows the four phases of the top-down approach because of its reusability characteristics. To validate and test the efficiency and effectiveness of the RAS, the fog framework was implemented and evaluated in a simulated smart home setup. The essential metrics that were used to check if round-trip time was minimized are the queuing time, offloading time and throughput for QoS. The results showed that the RAS helps to reduce the round-trip time, increases throughput and leads to improved QoS. Furthermore, the approach addressed the starvation problem, a phenomenon that tends to affect low priority tasks. Most importantly, the results provides evidence that if resource allocation and assignment are appropriately done, round-trip time can be reduced and QoS can be improved in fog computing. The significant contribution of this research is the novel framework which minimizes round-trip time, addresses the starvation problem and improves QoS. Moreover, a literature reviewed paper which was regarded by reviewers as the first, as far as QoS in fog computing is concerned was produced