A suitable environment for the growth of plants is the Greenhouse, that needs to be monitored by a continuous collection of data related to temperature, carbon dioxide concentration, humidity, illumination intensity using sensors, preferably in a wireless sensor network (WSN). Demand initiates various challenges for diversified applications of WSN in the field of IoT (Internet of Things). Network design in IoT based WSN faces challenges like limited energy capacity, hardware resources, and unreliable environment. Issues like cost and complexity can be limited by using sensors that are heterogeneous in nature. Since replacing or recharging of nodes in action is not possible, heterogeneity in terms of energy can overcome crucial issues like energy and lifetime. In this paper, an energy efficient routing process is discussed that considers three different sensor node categories namely normal, intermediate and advanced nodes. Also, the basic cluster head (CH) selection threshold value is modified considering important parameters like initial and residual energy with an optimum number of CHs in the network. When compared with routing algorithms like LEACH (Low Energy Adaptive Clustering Hierarchy) and SEP (Stable Election Protocol), the proposed model performs better for metrics like throughput, network stability and network lifetime for various scenarios.

The Internet of Things (IoT) is now permeating our daily lives, providing critical data for every decision. IoT architecture consists of multiple layers with unique functions and independent components. Each layer of IoT architecture requires different power sources and power delivery schemes. Therefore, different types of power management architectures are required for individual IoT components. Fortunately, advances in metal oxide semiconductor (MOS) technology have made it possible to implement a variety of high-performance power management architectures. These power management architectures should not only create the power rails required for IoT components but also serve additional functions depending on the application. The power management architecture of IoT devices needs to support sub-mW- or mW-scale power consumption. In addition, the power management architecture should be either fully integrated on a chip or miniaturized with few passive components to minimize the size of IoT devices. Building-scale data centers, on the other hand, need various power conversion stages. In this scenario, power conversion from an intermediate DC bus to many point of loads (PoL) requires a high conversion ratio DC-DC converter. Because each PoL draws enormous amounts of power, the power management architecture should withstand high currents and include protection circuitry to prevent damage. This thesis presents research on the design of power management architectures required by IoT devices and data centers. Chapter 2 presents the design and circuit techniques of power management architectures for IoT devices. Chapter 2 outlines a new methodology for co-designing an integrated switched-capacitor (SC) DC-DC converter and a load circuit in ultra-low-power IoT devices. This methodology enables the implementation of an area-efficient fully integrated IoT system-on-chip (SoC) while maintaining high power conversion efficiency (PCE). Chapter 3 presents a 10-output ultra-low-power single-inductor-multiple-output (SIMO) DC-DC buck converter with integrated output capacitors for sub-mW IoT SoCs. Featuring a continuous comparator-based output switch controller and a digital pulse-width modulation (PWM) controller for ultra-low feedback latency, this SIMO converter produces ten independent output voltages with high PCE. Lastly, in Chapter 4, an integrated programmable gate timing control and gate driver chip for an active-clamp forward converter (ACFC) Power Block for data center applications is developed. While the ACFC Power Block converts 12-48 V intermediate DC bus voltage to a digital PoL voltage, the gate timing control and driver chip can optimize PCE and reduce the system form factor.

, / El presente trabajo ofrece una propuesta de solución tecnológica al problema asociado a la violencia contra la mujer en el Perú. El proyecto realizo un diagnóstico del tiempo promedio de atención, en base a un estudio, de los principales canales de prevención y denuncia existentes. Del estudio realizado se pudo determinar que existen elevados tiempos de atención y asimetrías de información entre los diferentes actores del sistema de prevención y protección a la mujer. La solución propuesta, propone reducir al tiempo mínimo necesario para la atención y detención de un acto de violencia hacia la mujer brindando una reacción inmediata al evento. Complementando el proceso de proceso del proyecto hemos usamos componentes electrónicos de bajo costo y sistemas acordes y existentes en el mercado actual. La solución agrupa componentes tecnológicos propiamente configurados tanto en hardware como en software para realizar el seguimiento de la violencia, el cual se compone principalmente de la siguiente configuración: 1) Un dispositivo ‘wearable’, alertando a un grupo determinado de personas previamente agregadas, familiares, conocidos o personas dispuestas a ayudar, 2) Un sistema de comunicación, la alerta en tiempo real la cual que por medio de señales a una plataforma Cloud son distribuidos a una aplicación móvil y pagina web. La solución fue validada en un CEM ubicada en una comisaría de la policía nacional en la provincia del Callao, Distrito de Ventanilla. El proyecto busca contribuir con la disminución del tiempo de respuesta de ayuda a las mujeres víctimas y la asimetría de información en las organizaciones públicas que se ven involucradas. / This work offers a proposal for a technological solution to the problem associated with violence against women in Peru. The project made a diagnosis of the average attention time, based on a study, of the main existing prevention and reporting channels. From the study carried out, it was possible to determine that there are high attention times and information asymmetries between the different actors in the prevention and protection system for women. The proposed solution proposes to reduce to the minimum time necessary for the attention and arrest of an act of violence against women, providing an immediate reaction to the event. Complementing the process of the project process we have used low-cost electronic components and systems that are consistent and existing in the current market. The solution groups properly configured technological components in both hardware and software to track violence, which mainly consists of the following configuration: 1) A 'wearable' device, alerting a specific group of previously added people, family members , acquaintances or people willing to help, 2) A communication system, the alert in real time which through signals to a Cloud platform are distributed to a mobile application and website. The solution was validated at a CEM located in a national police station in the Callao province, Ventanilla District. The project seeks to contribute to reducing the response time to help women victims and the asymmetry of information in the public organizations that are involved. / Tesis

The Industrial Internet of Things (IIoT) plays a critical role in modern industrial systems, contributing to increased efficiency, productivity, and innovation. However,its rapid evolution and the complexity of devices pose significant challenges to digital forensics readiness (DFR). This thesis aims to provide a set of guidelines forimplementing DFR within IIoT environments, addressing challenges such as datacollection and logging, device and data identification, verification, security, analysis,and reporting. The framework was developed through rigorous research processesand guided by expert interviews and a final survey, adhering to design science principles. Although the study’s outcomes are subject to some limitations, such as a smallnumber of experts for evaluation, the research contributes to a significant gap in theexisting literature by providing a robust, adaptable, and comprehensive guide to DFRin IIoT. Offering a foundation for future research to build upon, enhance DFR, andaddressing emerging IIoT technologies and scenarios.

A rising level of industrialization and advances in Industry 4.0 have resulted in Industrial Internet of Things (IIoT) gaining immense significance in today’s industrial automation systems. IIoT promises to achieve improved productivity, reliability, and revenues by connecting time-constrained embedded systems to “the Internet”. New opportunities bring with them challenges, and in particular for industrial networks, massively interconnected IIoT devices communicating in real-time,  require synchronized operation of devices for the ordering of information collected throughout a  network. Thus,   a   time or clock synchronization service that aligns the devices’ clocks in the network to ensure accurate timestamping and orderly event executions, has gained great importance. Achieving adequate clock synchronization in the industrial domain is challenging due to heterogeneous communication networks and exposure to harsh environmental conditions bringing interference to the communication networks. The investigative study based on existing literature and the envisioned architecture of the future industrial automation system unveils that the key requirements for future industrial networks are to have a cost-effective, accurate, scalable, secured, easy to deploy and maintain clock synchronization solution. Today’s industrial automation systems employ clock synchronization solutions from a wide plethora of hardware and software based solutions. The most economical, highly scalable, maintainable software-based clock synchronization means are best candidates for the identified future requirements as their lack in accuracy compared to hardware solutions could be compensated by predictive software strategies.  Thus, the thesis’s overall goal is to enhance the accuracy of software-based clock synchronization in heterogeneous industrial networks using predictable software strategies. The first step towards developing an accurate clock synchronization for heterogeneous industrial networks with real-time requirements is to investigate communication parameters affecting time synchronization accuracy. Towards this goal, we investigated actual industrial network data for packet delay profiles and their impact on clock synchronization performance.  We further analyzed wired and wireless local area networks to identify key network parameters for clock synchronization and proposed an enhanced clock synchronization algorithm CoSiNeT for field IoT devices in industrial networks. CoSiNeT matches well with state-of-the-practice SNTP and state-of-the-art method SPoT in good network conditions in terms of accuracy and precision;  however,  it outperforms them in scenarios with degrading network conditions.

The increase in popularity for wearable technologies (see: Apple Watch and Microsoft Band) has opened the door for an Internet of Things solution to healthcare. One of the most prevalent healthcare problems today is the poor survival rate of out-of hospital sudden cardiac arrests (9.5% on 360,000 cases in the USA in 2013). It has been proven that heart rate derived features can give an early indicator of sudden cardiac arrest, and that providing an early warning has the potential to save many lives. Many of these new wearable devices are capable of providing this warning through their heart rate sensors. This thesis paper introduces a prospective dataset of physical activity heart rates collected via Microsoft Band. This dataset is indicative of the heart rates that would be observed in the proposed Internet of Things solution. This dataset is combined with public heart rate datasets to provide a dataset larger than many of the ones used in related works and more indicative of out-of-hospital heart rates. This paper introduces the use of LogitBoost as a classifier for sudden cardiac arrest prediction. Using this technique, a five minute warning of sudden cardiac arrest is provided with 96.36% accuracy and F-score of 0.9375. These results are better than existing solutions that only include in-hospital data.

No / Abstract: Internet of Things (IoT) aims to enable the interconnection of a large number of smart devices (things) using a combination of networks and computing technologies. But an influx of interconnected things makes a greater demand on the underlying communication networks and affects the quality of service (QoS). This paper investigates into the QoS of delay sensitive things and the corresponding traffic they generate over the network. Things such as security alarms, cameras, etc, generate delay sensitive information that must be communicated in a real time. Such things have heterogeneous features with limited buffer capacity, storage and processing power. Thus the most commonly used Best Effort service model cannot be an attractive mechanism to treat delay sensitive traffic. This paper proposes a cost-effective analytical model for a finite capacity queueing system with pre-emptive resume service priority and push-out buffer management scheme. Based on the analytical model various simulation results are generated in order to analyse the mean queue length and the blocking probability of high and low priority traffic for system with various capacities.

No / The merger of the Internet of Things (IoT) with cloud computing has given birth to a Web of Things (WoT) which hosts heterogeneous and rapidly varying data. Traditional access control mechanisms such as Role-Based Access schemes are no longer suitable for modelling access control on such a large and dynamic scale as the actors may also change all the time. For such a dynamic mix of applications, data and actors, a more distributed and flexible model is required. Token-Based Access Control is one such scheme which can easily model and comfortably handle interactions with big data in the cloud and enable provisioning of access to fine levels of granularity. However, simple token access models quickly become hard to manage in the face of a rapidly growing repository. This paper proposes a novel token access model based on a cascading permissions policy model which can easily control interactivity with big data without becoming a menace to manage and administer.

The Internet of Things (IoT) is an important and expanding technology used for a large variety of applications to monitor and automate processes. The aim of this thesis is to present a way to find and assign locally unique IDs to access points (APs) in enormous wireless IoT systems where mobile tags are traversing the network and communicating with multiple APs simultaneously. This is done in order to improve the robustness of the system and increase the battery time of the tags. The resulting algorithm is based on transforming the problem into a graph coloring problem and solving it using approximate methods. Two metaheuristics: Simulated annealing and tabu search were implemented and compared for this purpose. Both of these showed similar results and neither was clearly superior to the other. Furthermore, the presented algorithm can also exclude nodes from the coloring based on the results in order to ensure a proper solution that also satisfies a robustness criterion. A metric was also created in order for a user to intuitively evaluate the quality of a given solution. The algorithm was tested and evaluated on a system of 222 APs for which it produced good results.

Internet of Things (IoT) has weaknesses due to the vulnerabilities in the wireless medium and massively interconnected nodes that form an extensive attack surface for adversaries. It is essential to ensure security including IoT networks and applications. The thesis focus on three streams in IoT scenario, including fake task attack detection in Mobile Crowdsensing (MCS), blockchain technique-integrated system security and privacy protection in MCS, and network intrusion detection in IoT. In this thesis, to begin, in order to detect fake tasks in MCS with promising performance, a detailed analysis is provided by modeling a deep belief network (DBN) when the available sensory data is scarce for analysis. With oversampling to cope with the class imbalance challenge, a Principal Component Analysis (PCA) module is implemented prior to the DBN and weights of various features of sensing tasks are analyzed under varying inputs. Additionally, an ensemble learning-based solution is proposed for MCS platforms to mitigate illegitimate tasks. Meanwhile, a k-means-based classification is integrated with the proposed ensemble method to extract region-specific features as input to the machine learning-based fake task detection. A novel approach that is based on horizontal Federated Learning (FL) is proposed to identify fake tasks that contain a number of independent detection devices and an aggregation entity. Moreover, the submitted tasks are collected and managed conventionally by a centralized MCS platform. A centralized MCS platform is not safe enough to protect and prevent tampering sensing tasks since it confronts the single point of failure which reduces the effectiveness and robustness of MCS system. In order to address the centralized issue and identify fake tasks, a blockchain-based decentralized MCS is designed. Integration of blockchain into MCS enables a decentralized framework. The distributed nature of a blockchain chain prevents sensing tasks from being tampered. The blockchain network uses a Practical Byzantine Fault Tolerance (PBFT) consensus that can tolerate 1/3 faulty nodes, making the implemented MCS system robust and sturdy. Lastly, Machine Learning (ML)-based frameworks are widely investigated to identity attacks in IoT networks, namely Network Intrusion Detection System (NIDS). ML models perform divergent detection performance in each class, so it is challenging to select one ML model applicable to all classes prediction. With this in mind, an innovative ensemble learning framework is proposed, two ensemble learning approaches, including All Predict Wisest Decides (APWD) and Predictor Of the Lowest Cost (POLC), are proposed based on the training of numerous ML models. According to the individual model outcomes, a wise model performing the best detection performance (e.g., F1 score) or contributing the lowest cost is determined. Moreover, an innovated ML-based framework is introduced, combining NIDS and host-based intrusion detection system (HIDS). The presented framework eliminates NIDS restrictions via observing the entire traffic information in host resources (e.g., logs, files, folders).