Increase in population and limited resources have created a growing demand for a futuristic living environment where technology enables the efficient utilization and management of resources in order to increase quality of life. One characteristic of such a society, which is often referred to as a ‘Smart City’, is that the people are well informed about their physiological being as well as the environment around them, which makes them better equipped to handle crisis situations. There is a need, therefore, to develop wireless sensors which can provide early warnings and feedback during calamities such as floods, fires, and industrial leaks, and provide remote health care facilities. For these situations, low-cost sensor nodes with small form factors are required. For this purpose, the use of a low-cost, mass manufacturing technique such as inkjet printing can be beneficial due to its digitally controlled additive nature of depositing material on a variety of substrates. Inkjet printing can permit economical use of material on cheap flexible substrates that allows for the development of miniaturized freeform electronics. This thesis describes how low-cost, inkjet-printed, wireless sensors have been developed for real-time monitoring applications. A 3D buoyant mobile wireless sensor node has been demonstrated that can provide early warnings as well as real-time data for flood monitoring. This disposable paper-based module can communicate while floating in water up to a distance of 50 m, regardless of its orientation in the water. Moreover, fully inkjet-printed sensors have been developed to monitor temperature, humidity and gas levels for wireless environmental monitoring. The sensors are integrated and packaged using 3D inkjet printing technology. Finally, in order to demonstrate the benefits of such wireless sensor systems for health care applications, a low-cost, wearable, wireless sensing system has been developed for chronic wound monitoring. The system called ‘Smart Bandage’ can provide early warnings and long term data for medical diagnoses. These demonstrations show that inkjet printing can enable the development of low-cost wireless sensors that can be dispersed in the environment or worn on the human body to enable an internet of things (IoT), which can facilitate better and safer living.

An increasing number of devices, from coffee makers to electric kettles, are becoming connected to the Internet. These are all a part of the Internet of Things, or IoT. Each device generates unique network traffic and power consumption patterns. Until now, there has not been a comprehensive set of data that captures these traffic and power patterns. This thesis documents how we collected 10 to 15 weeks of network traffic and power consumption data from 15 different IoT devices and provides an analysis of a subset of 6 devices. Devices including an Amazon Echo Dot, Google Home Mini, and Google Chromecast were used on a regular basis and all of their network traffic and power consumption was logged to a MySQL database. The database currently contains 64 million packets and 71 gigabytes of data and is still growing in size as more data is collected 24/7 from each device. We show that it is possible to see when users are asking their smart speaker a question or whether the lights in their home are on or off based on power consumption and network traffic from the devices. These trends can be seen even if the data being sent is encrypted.

This thesis deal with database for smart homes, that is developing at FIT VUT in Brno. Thesis describes design and implementation of database for smart home in PostgreSQL and focus on speeding up queries and inserting to database. Designed database, on processor Intel Xeon E5410 with 12GB RAM and one hard drive, is capable to serve about 16000 homes. The thesis describes server application, which comunicate with user devices and provides data stored in database. Whole smart home system was thoroughly tested by real users. Several months of testing was made on alpha and beta versions. Server application is also integrated to system of continual integration Jenkins.

The indoor positioning system is a series of networking systems used to monitor/locate objects at indoor area as opposed that of GPS which does the same at outdoor. The increase in the popularity of the Internet of Things made the demand for Bluetooth Low Energy technology more and more essential due to their compatibility in the smartphones which makes it to access easier. The BLE’s reliable signal and accuracy in calculating the distance has a cutting edge on others in IPS. In this thesis, the Bluetooth Low Energy indoor positioning system was designed and implemented in the office area, and the positionofIoTdevicesweremonitored. OntheIoTdevices,thebeaconswereplaced. And thesebeaconswerecoveringtheofficearea. Thereceiver,smartphoneinourcase,recorded theReceivedSignalStrengthIndicationofthetransmittedsignalsfromthebeaconswithin the range of the signal and stored the collected data in a database. Two experiments have beenconducted. Oneisforbeaconsthatarestationaryandonethatismoving. Toevaluate these experiments, a few tests were performed to predict the position of beacons based on therecordedreceivedsignalstrength’s. Inthecaseofstationarybeacons, itoffersaccuracy range from 1 m to 5 m, and 3 m to 9.5 m in anticipating the position of each beacon in the case of moving beacon. This methodology was a mixture of fingerprinting and an algorithm of multilateration. Finally, the experiments show that the algorithm used provides the most accurate indoor position using BLE beacons that can be monitored through an Android-based application in real-time.

In recent years, advances in information technology have led to an increasing number of devices (or things) being connected to the internet; the resulting data can be used by applications to acquire new knowledge. The Internet of Things (IoT) (a network of computing devices that have the ability to interact with their environment without requiring user interaction) and big data (a field that deals with the exponentially increasing rate of data creation, which is a challenge for the cloud in its current state and for standard data analysis technologies) have become hot topics. With all this data being produced, new applications such as predictive maintenance are possible. One such application is monitoring a fleet of vehicles in real-time to predict their remaining useful life, which could help companies lower their fleet management costs by reducing their fleet's average vehicle downtime. Consensus self-organized models (COSMO) approach is an example of a predictive maintenance system for a fleet of public transport buses, which attempts to diagnose faulty buses that deviate from the rest of the bus fleet. The present work proposes a novel IoT-based architecture for predictive maintenance that consists of three primary nodes: namely, the vehicle node (VN), the server leader node (SLN), and the root node (RN). The VN represents the vehicle and performs lightweight data acquisition, data analytics, and data storage. The VN is connected to the fleet via its wireless internet connection. The SLN is responsible for managing a region of vehicles, and it performs more heavy-duty data storage, fleet-wide analytics, and networking. The RN is the central point of administration for the entire system. It controls the entire fleet and provides the application interface to the fleet system. A minimally viable prototype (MVP) of the proposed architecture was implemented and deployed to a garage of the Soci\'et\'e de Transport de l'Outaouais (STO), Gatineau, Canada. The VN in the MVP was implemented using a Raspberry Pi, which acquired sensor data from a STO hybrid bus by reading from a J1939 network, the SLN was implemented using a laptop, and the RN was deployed using meshcentral.com. The goal of the MVP was to perform predictive maintenance for the STO to help reduce their fleet management costs. The present work also proposes a fleet-wide unsupervised dynamic sensor selection algorithm, which attempts to improve the sensor selection performed by the COSMO approach. I named this algorithm the improved consensus self-organized models (ICOSMO) approach. To analyze the performance of ICOSMO, a fleet simulation was implemented. The J1939 data gathered from a STO hybrid bus, which was acquired using the MVP, was used to generate synthetic data to simulate vehicles, faults, and repairs. The deviation detection of the COSMO and ICOSMO approach was applied to the synthetic sensor data. The simulation results were used to compare the performance of the COSMO and ICOSMO approach. Results revealed that in general ICOSMO improved the accuracy of COSMO when COSMO was not performing optimally; that is, in the following situations: a) when the histogram distance chosen by COSMO was a poor choice, b) in an environment with relatively high sensor white noise, and c) when COSMO selected poor sensors. On average ICOSMO only rarely reduced the accuracy of COSMO, which is promising since it suggests deploying ICOSMO as a predictive maintenance system should perform just as well or better than COSMO . More experiments are required to better understand the performance of ICOSMO. The goal is to eventually deploy ICOSMO to the MVP.

L’Internet des Objets (IoT) est envisagé pour interconnecter des objets communicants et autonomes au sein du même réseau, qui peut être le réseau Internet ou un réseau de communication sans fil. Les objets autonomes qui composent les réseaux IoT possèdent des caractéristiques très différentes, que ce soit en terme d’application, de connectivité, de puissance de calcul, de mobilité ou encore de consommation de puissance. Le fait que tant d’objets hétérogènes partagent un même réseau soulève de nombreux défis tels que : l’identification des objets, l’efficacité énergétique, le contrôle des interférences du réseau, la latence ou encore la fiabilité des communications. La densification du réseau couplée à la limitation des ressources spectrales (partagées entre les objets) et à l’efficacité énergétique obligent les objets à optimiser l’utilisation des ressources fréquentielles et de puissance de transmission. De plus, la mobilité des objets au sein du réseau ainsi que la grande variabilité de leur comportement changent la dynamique du réseau qui devient imprévisible. Dans ce contexte, il devient difficile pour les objets d’utiliser des algorithmes d’allocation de ressources classiques, qui se basent sur une connaissance parfaite ou statistique du réseau. Afin de transmettre de manière efficace, il est impératif de développer de nouveaux algorithmes d’allocation de ressources qui sont en mesure de s’adapter aux évolutions du réseau. Pour cela, nous allons utiliser des outils d’optimisation en ligne et des techniques d’apprentissage. Dans ce cadre nous allons exploiter la notion du regret qui permet de comparer l’efficacité d’une allocation de puissance dynamique à la meilleure allocation de puissance fixe calculée à posteriori. Nous allons aussi utiliser la notion de non-regret qui garantit que l’allocation de puissance dynamique donne des résultats asymptotiquement optimaux . Dans cette thèse, nous nous sommes concentrés sur le problème de minimisation de puissance sous contrainte de débit. Ce type de problème permet de garantir une certaine efficacité énergétique tout en assurant une qualité de service minimale des communications. De plus, nous considérons des réseaux de type IoT et ne faisons donc aucune hypothèse quant aux évolutions du réseau. Un des objectifs majeurs de cette thèse est la réduction de la quantité d’information nécessaire à la détermination de l’allocation de puissance dynamique. Pour résoudre ce problème, nous avons proposé des algorithmes inspirés du problème du bandit manchot, problème classique de l’apprentissage statistique. Nous avons montré que ces algorithmes sont efficaces en terme du regret lorsque l’objet a accès à un vecteur, le gradient ou l’estimateur non-biaisé du gradient, comme feedback d’information. Afin de réduire d’avantage la quantité d’information reçue par l’objet, nous avons proposé une méthode de construction d’un estimateur du gradient basé uniquement sur une information scalaire. En utilisant cet estimateur nous avons présenté un algorithme efficace d’allocation de puissance. / One of the key challenges in Internet of Things (IoT) networks is to connect numerous, heterogeneous andautonomous devices. These devices have different types of characteristics in terms of: application, computational power, connectivity, mobility or power consumption. These characteristics give rise to challenges concerning resource allocation such as: a) these devices operate in a highly dynamic and unpredictable environments; b) the lack of sufficient information at the device end; c) the interference control due to the large number of devices in the network. The fact that the network is highly dynamic and unpredictable implies that existing solutions for resource allocation are no longer relevant because classical solutions require a perfect or statistical knowledge of the network. To address these issues, we use tools from online optimization and machine learning. In the online optimization framework, the device only needs to have strictly causal information to define its online policy. In order to evaluate the performance of a given online policy, the most commonly used notion is that of the regret, which compares its performance in terms of loss with a benchmark policy, i.e., the best fixed strategy computed in hindsight. Otherwise stated, the regret measures the performance gap between an online policy and the best mean optimal solution over a fixed horizon. In this thesis, we focus on an online power minimization problem under rate constraints in a dynamic IoT network. To address this issue, we propose a regret-based formulation that accounts for arbitrary network dynamics, using techniques used to solve the multi-armed bandit problem. This allows us to derive an online power allocation policy which is provably capable of adapting to such changes, while relying solely on strictly causal feedback. In so doing, we identify an important tradeoff between the amount of feedback available at the transmitter side and the resulting system performance. We first study the case in which the device has access to a vector, either the gradient or an unbiased estimated of the gradient, as information feedback. To limit the feedback exchange in the network our goal is to reduce it as mush as possible. Therefore, we study the case in which the device has access to only a loss-based information (scalar feedback). In this case, we propose a second online algorithm to determine an efficient and adaptative power allocation policy.

Radiofrequency spectrum is a finite resource that consists of the frequencies in the range 3 kHz to 300 GHz. It is used for wireless communication and supports several applications and services. Whether it is at the personal, community or society level, and whether it is for applications in consumer electronics, building management, smart utility networks, intelligent driving systems, the Internet of Things, industrial automation and so on, the demand for wireless communication is increasing continuously. Together with this increase in demand, there is an increase in the quality of service requirements in terms of throughput, and the reliability and availability of wireless services. Industrial wireless sensor networks, for example, operate in environments that are usually harsh and time varying. The frequency spectrum that is utilised by industrial wireless protocols such as WirelessHART and ISA 100.11a, is also used by many other wireless technologies, and with wireless applications growing rapidly, it is possible that multiple heterogeneous wireless systems will need to operate in overlapping spatiotemporal regions in the future. Increased radiofrequency interference affects connectivity and reduces communication link quality. This affects reliability and latency negatively, both of which are core quality service requirements. Getting multiple heterogeneous radio systems to co-exist harmoniously in shared spectrum is challenging. Traditionally, this has been achieved by granting network operators exclusive rights that allow them to access parts of the spectrum assigned to them and hence the problems of co-existence and limited spectrum could be ignored. Design time multi-access techniques have also been used. At present, however, it has become necessary to use spectrum more efficiently, to facilitate the further growth of wireless communication. This can be achieved in a number of ways. Firstly, the policy that governs the regulation of radiofrequency spectrum must be updated to accommodate flexible, dynamic spectrum access. Secondly, new techniques for multiple-access and spectrum sharing should be devised. A revolutionary new communication paradigm is required, and one such paradigm has recently emerged in the form of Cognitive Radio technology. Traditional methods to sharing spectrum assume that radios in a wireless network work together in an unchanging environment. Cognitive radios, on the other hand, can sense, learn and adapt. In cognitive radio networks, the interactions between users are taken into account, in order for adjustments to be made to suit the prevailing radio environment. In this thesis, the problem of spectrum scarcity and coexistence is addressed using cognitive radio techniques, to ensure more efficient use of radio-frequency spectrum. An introduction to cognitive radio networks is given, covering cognitive radio fundamentals, spectrum sensing, dynamic spectrum management, game theoretic approaches to spectrum sharing and security in cognitive radio networks. A focus is placed on wireless industrial networks as a challenging test case for cognitive radio. A study on spectrum management policy is conducted, together with an investigation into the current state of radio-frequency spectrum utilisation, to uncover real and artificial cases of spectrum scarcity. A novel cognitive radio protocol is developed together with an open source test bed for it. Finally, a game theoretic dynamic spectrum access algorithm is developed that can provide scalable, fast convergence spectrum sharing in cognitive radio networks. This work is a humble contribution to the advancement of wireless communication. / Thesis (PhD)--University of Pretoria, 2016. / Centre for Telecommunication Engineering for the Information Society / Electrical, Electronic and Computer Engineering / PhD / Unrestricted

Endpoint devices are integral in the realisation of any industrial cyber-physical system (ICPS) application. As part of the work of promoting safer and more secure industrial Internet of Things (IIoT) networks and devices, the Industrial Internet Consortium (IIC) and the OpenFog Consortium have developed security framework specifications detailing security techniques and technologies that should be employed during the design of an IIoT network. Previous work in establishing cryptographic services on platforms intended for wireless sensor networks (WSN) and the Internet of Things (IoT) has concluded that security mechanisms cannot be implemented using software libraries owing to the lack of memory and processing resources, the longevity requirements of the processor platforms, and the hard real-time requirements of industrial operations. Over a decade has passed since this body of knowledge was created, however, and IoT processors have seen a vast improvement in the available operating and memory resources while maintaining minimal power consumption. This study aims to update the body of knowledge regarding the provision of security services on an IoT platform by conducting a detailed analysis regarding the performance of new generation IoT platforms when running software cryptographic services. The research considers execution time, power consumption and memory occupation and works towards a general, implementable design of a secure, IIoT edge device. This is realised by identifying security features recommended for IIoT endpoint devices; identifying currently available security standards and technologies for the IIoT; and highlighting the trade-offs that the application of security will have on device size, performance, memory requirements and monetary cost. / Dissertation (MSc)--University of Pretoria, 2017. / Electrical, Electronic and Computer Engineering / MSc / Unrestricted

The evolution of the internet of things (IoT) opens the market opportunity for organic photovoltaic cells, especially for indoor applications where the lifetime of the organic cells is longer than outdoor. For example, IoT requires off-grid energy sources for many devices with low power consumption. In this work, new materials were tested as candidate components in the active layer of printed organic photovoltaics by fabrication of devices. The initial electrical performance of these devices and their stability over time were investigated by measurements of the current-voltage characteristics. Three selected active layers were further investigated with atomic force microscopy (AFM) measurements. The current-voltage measurements showed that the addition of a solvent additive to the active layer ink affects the initial electrical performance as well as the stability of the devices. The AFM measurements showed that the surface topography of the active layer was affected by the sort of solvent additive that was used. Three new electron acceptor material and two solvent additives showed promising electrical performance and stability.

Thesis (MTech (Business Information Systems))--Cape Peninsula University of Technology, 2019 / The internet, mobile communication, social media, and other digital services have integrated so much into our daily lives and businesses alike. Companies facing digital transformation experience this as exceptionally challenging. While there are several studies that state the importance of digital transformation and how it influences current and future businesses, there is little academic literature available on factors that affect the success or failure of digital transformation in companies. It is unclear what factors affect digital transformation in an established telecommunications company. The aim of this study was therefore to explore the factors that affect digital transformation in a telecommunications company in South Africa, and to what extent. One primary research question was posed, namely: “What factors affect digital transformation in a telecommunications company in South Africa?” To answer the question, a study was conducted at a telecommunications company in South Africa. The researcher adopted a subjective ontological and interpretivist epistemological stance, as the data collected from the participants’ perspective were interpreted to make claims about the truth, and because there are many ways of looking at the phenomena. An inductive approach was selected to enable the researcher to gain in-depth insight into the views and perspective of factors that influence digital transformation in the specific company. The explorative research strategy was used to gain an understanding of the underlying views, reasons, opinions, and thoughts of the 15 participants by means of semi-structured interviews. The participants were made aware that they do not have to answer any question if they are uncomfortable, and they could withdraw their answers at any time. The data collected were transcribed, summarised, and categorised to provide a clear understanding of the data. For this study, 36 findings were identified. From this research, it was inter alia concluded that successful digital transformation of companies depends on how Management drives digital transformation, and the benefits of new digital technologies should be carefully considered when planning to implement digital transformation.