A l’ère de la troisième révolution internet, celle de l’internet des objets (IoT), beaucoup d’espoir sont tournés vers les technologies numériques pour développer les économies et notamment dynamiser le commerce local. Dans les pays du Maghreb, en Tunisie notamment, ses espoirs sont tournés du côté de la demande. Plus précisément, à l’issu du printemps arabe, et en raison du rôle qu’ont joué les réseaux sociaux (Facebook notamment) pour mobiliser les jeunes à manifester dans les rues de Tunis, le nouveau ministère du commerce fonde ses espoirs dans cette population jeune pour développer le commerce électronique. Partant de ce constat, cette thèse propose d’étudier si les jeunes tunisiens sont les early adopters qui permettront de diffuser le commerce électronique en Tunisie. Pour cela, la problématique de recherche porte sur les facteurs d’adoption du commerce électronique par les jeunes en Tunisie. Pour répondre à cette question de recherche, la thèse propose dans un premier temps de dresser l’état des lieux de la diffusion de l’internet au sein d’un population de jeunes étudiants. Préalablement, elle propose dans la première partie de la thèse un état des lieux de la couverture du territoire en technologies d’accès aux services numériques. Cet état des lieux est discuté à l’appui d’une revue de la littérature sur la fracture numérique. Dans un deuxième temps, elle propose une analyse empirique et exploratoire menée auprès d’un échantillon de 400 étudiants tunisiens environ. Cette étude porte sur les facteurs d’adoption de l’internet et des services marchands (commerce électronique). Structurée en deux parties, le premier volet de l’enquête est exploité à l’aide de statistiques économétriques et le deuxième volet mobilise les méthodes d’analyse d’équations structurelles. Les résultats de la thèse montrent que si l’usage de l’internet est relativement bien diffusé auprès des jeunes tunisiens, la disposition à adopter le commerce électronique doit en revanche être encouragée. Pour cela, un modèle d’adoption est proposé en deuxième partie de la thèse. Il met en évidence quels facteurs favorisent ou inhibent l’adoption du commerce électronique en Tunisie. Les résultats de ce modèle et des travaux menés dans la partie 1 permettent dans la partie conclusive de la thèse de formuler différentes recommandations à l’égard des entreprises engagées dans un projet de mise en ligne de leur activité commerciale d’une part, et d’autre part envers les acteurs publics pour favoriser l’adoption des services internet marchands en Tunisie. / In the era of the third Internet revolution, that of the Internet of Things (IoT), a lot of hope is turned to digital technologies to develop economies and boost local trade. In the Maghreb countries, especially in Tunisia, those hope are turned on the demand side. More specifically, at the end of the Arab Spring, and because of the role played by social networks (Facebook in particular) to mobilize young people to demonstrate in the streets of Tunis, the new Ministry of Commerce hopes in this population young to develop e-commerce. Based on this observation, this thesis proposes to study whether young Tunisians are the early adopters who will disseminate e-commerce in Tunisia. For this, the research problematic focuses on the factors of adoption of e-commerce by young people in Tunisia. To answer this research question, the thesis proposes at first to draw up the inventory of the diffusion of the internet within a population of young students. Before doing so, we propose in the first part of the thesis an inventory of the technologies territory’s coverage of the access to digital services. This inventory is discussed in support of a review of the literature on the digital divide. In a second step, she proposes an empirical and exploratory analysis conducted with a sample of about 400 Tunisian students. This study focuses on the adoption factors of the Internet and market services (e-commerce). Structured in two parts, the first part of the survey is exploited using econometric statistics and the second part uses structural equation analysis methods. The results of the thesis show that while the use of the internet is relatively well distributed to young Tunisians, the willingness to adopt e-commerce should be encouraged. For this, an adoption model is proposed in the second part of the thesis. It highlights what factors encourages or inhibit the adoption of e-commerce in Tunisia. The results of this model and the work carried out in Part 1 allow in the concluding part of the thesis to make various recommendations to companies engaged in a project to put their commercial activity online on one hand, and on the other hand towards the public actors to encourage the adoption of the internet marketing services in Tunisia.

Prenons en exemple une salle de classe composée d’un professeur et de nombreux élèves, lorsque trop d’élèves s’adressent en même temps au professeur ce dernier n’est plus en mesure de comprendre les paroles transmisses par les élèves. Cette illustration s’étend évidemment aux systèmes de communications sans fil (la 4G par exemple). Dans ces systèmes, les terminaux (les élèves par analogie) transmettent sur un canal, nommé canal en accès aléatoire, des messages qui sont potentiellement réceptionnés par la station de base (le professeur par analogie). Ces canaux ne sont habituellement pas surchargés car leur capacité (nombre de messages reçus par seconde) est tellement importante qu’il est très complexe de surcharger le canal. L’émergence de l’Internet des objets où des milliards de petits objets devraient être déployés partout dans le monde a changé la donne. Étant donné leur nombre et leur type de trafic, ces derniers peuvent surcharger les canaux en accès aléatoire. Ainsi, le sujet : « contrôle de charge des canaux en accès aléatoire » a connu un gain d’intérêts ces dernières années. Dans cette thèse nous avons développé des algorithmes de contrôle de charge permettant d’éviter qu’une station de base soit surchargée. Cela est très utile pour les opérateurs Télécoms, ils sont désormais certains qu’il y n’y aura pas de perte de service à cause de ces surcharges. Tous les principes développés dans cette thèse seront intégrés dans un futur proche aux produits IoT d’Airbus. / Take for example a classroom composed of a teacher and many students. When too many students address at the same time to the teacher, the latter is no longer able to understand the words transmitted by students. This illustration obviously extends to wireless communication systems (the 4G for example). In these systems, terminals (students by analogy) transmit on a channel, named random access channel, messages that are potentially received by the base station (the teacher by analogy). These channels are usually not overloaded because their capacity (number of messages received per second) is so important that it is very complex to overload the channel. The emergence of the Internet of Things, where billions of small objects should be deployed around the world, has changed the game. Due to their number and type of traffic, they can overload random access channels. Therefore, the subject: "load control of random access channels" has seen an increase in interest in recent years. In this PhD we have developed load control algorithms to prevent a base station from being overloaded. This is very useful for telecom operators, they are now certain that there will be no loss of service because of these overloads. All the principles developed in this thesis will be integrated in the near future within Airbus IoT products

Philosophiae Doctor - PhD / Cloud Computing enables users achieve ubiquitous on-demand , and convenient access to a variety of shared computing resources, such as serves network, storage ,applications and more. As a business model, Cloud Computing has been openly welcomed by users and has become one of the research hotspots in the field of information and communication technology. This is because it provides users with on-demand customization and pay-per-use resource acquisition methods.

The Internet of Things (IoT) computing paradigm has connected smart objects “things” and has brought new services at the proximity of the user. Edge Computing, a natural evolution of the traditional IoT, has been proposed to deal with the ever-increasing (i) number of IoT devices and (ii) the amount of data traffic that is produced by the IoT endpoints. EC promises to significantly reduce the unwanted latency that is imposed by the multi-hop communication delays and suggests that instead of uploading all the data to the remote cloud for further processing, it is beneficial to perform computation at the “edge” of the network, close to where the data is produced. However, bringing computation at the edge level has created numerous challenges as edge devices struggle to keep up with the growing application requirements (e.g. Neural Networks, or video-based analytics). In this thesis, we adopt the EC paradigm and we aim at addressing the open challenges. Our goal is to bridge the performance gap that is caused by the increased requirements of the IoT applications with respect to the IoT platform capabilities and provide latency- and energy-efficient computation at the edge level. Our first step is to study the performance of IoT applications that are based on Deep Neural Networks (DNNs). The exploding need to deploy DNN-based applications on resource-constrained edge devices has created several challenges, mainly due to the complex nature of DNNs. DNNs are becoming deeper and wider in order to fulfill users expectations for high accuracy, while they also become power hungry. For instance, executing a DNN on an edge device can drain the battery within minutes. Our solution to make DNNs more energy and inference friendly is to propose hardware-aware method that re-designs a given DNN architecture. Instead of proxy metrics, we measure the DNN performance on real edge devices and we capture their energy and inference time. Our method manages to find alternative DNN architectures that consume up to 78.82% less energy and are up to35.71% faster than the reference networks. In order to achieve end-to-end optimal performance, we also need to manage theedge device resources that will execute a DNN-based application. Due to their unique characteristics, we distinguish the edge devices into two categories: (i) a neuromorphic platform that is designed to execute Spiking Neural Networks (SNNs), and (ii) a general-purpose edge device that is suitable to host a DNN. For the first category, we train a traditional DNN and then we convert it to a spiking representation. We target the SpiNNaker neuromorphic platform and we develop a novel technique that efficiently configures the platform-dependent parameters, in order to achieve the highest possible SNN accuracy.Experimental results show that our technique is 2.5× faster than an exhaustive approach and can reach up to 0.8% higher accuracy compared to a CPU-based simulation method. Regarding the general-purpose edge devices, we show that a DNN-unaware platform can result in sub-optimal DNN performance in terms of power and inference time. Our approachconfigures the frequency of the device components (GPU, CPU, Memory) and manages to achieve average of 33.4% and up to 66.3% inference time improvements and an average of 42.8% and up to 61.5% power savings compared to the predefined configuration of an edge device. The last part of this thesis is the offloading optimization between the edge devicesand the gateway. The offloaded tasks create contention effects on gateway, which can lead to application slowdown. Our proposed solution configures (i) the number of application stages that are executed on each edge device, and (ii) the achieved utility in terms of Quality of Service (QoS) on each edge device. Our technique manages to (i) maximize theoverall QoS, and (ii) simultaneously satisfy network constraints (bandwidth) and user expectations (execution time). In case of multi-gateway deployments, we tackled the problem of unequal workload distribution. In particular, we propose a workload-aware management scheme that performs intra- and inter-gateway optimizations. The intra-gateway mechanism provides a balanced execution environment for the applications, and it achieves up to 95% performance deviation improvement, compared to un-optimized systems. The presented inter-gateway method manages to balance the workload among multiple gateways and is able to achieve a global performance threshold.

Internet of Things (IoT) devices often described as Smart products for consumers consists ofphysical things that inherit an Internet connection and therefore enable physical things to talkbetween each other and with people. IoT is a growing market with products existing in e.g.,consumers' homes, healthcare, or industries. These physical things have sensors that cangather information about users which later on can be used to adapt the behaviors of IoTdevices or create profiles of users. As with any device that is connected to the Internet, IoTdevices can fall victim to attacks from outside parties that try to steal private information orobserve users of the devices. With the growing market, it is of importance to understand what people know about IoTdevices and that a Smart home does not come with comfortability without its rough edges.This Bachelor thesis answers what people know about IoT, how they handle security issues,and the sharing of people’s personal information.The data was gathered through a survey that had 133 participants, the survey was shared inFacebook groups and spread through the snowball effect. After a participant was done withthe survey, they were urged to continue to share the survey with people they knew. The result shows that the majority of people have not heard the term IoT before and that it isnot common to take steps in protecting private information when using IoT devices. IoT is aterm that is unfamiliar to many, the result shows that the term Smart devices is morecommonly used than IoT devices among people.

The term “IoT” has evolved to encompass a wide range of diffuse concepts, but the common thread among the myriad definitions has been the convergence of technology to bring <i>advanced conveniences</i> to our every day, but complicated, lives. A long-term focus of the Collaborative Robotics Lab, and a particular focus of many with interests in consumer assistance, has been the kitchen, which acts as the “nerve center” of the home in many cultures. However, despite the grand vision of revolutionizing the kitchen and improving our lifestyles with technology, what today’s IoT-integrated appliances and kitchen-focused conveniences offer is mainly limited to a remote control. While remote control is certainly convenient, it still requires human planning in both cognitive and physical loads in performing cooking activities. The goal of this thesis is to build a framework of the network of IoT-enabled kitchen appliances, <i>TupperwareEarth</i> for the “Internet of Kitchen Things” integrated with an inference engine that utilizes ontology as a knowledge database. From simple clustering of sensor data to recommender systems that employ crowd-sourced preference data, the cognitive burden is reduced with proactive suggestions to high-level queries based upon the current kitchen state. Through the progression of the studies in the “Internet of Kitchen Things,” <i>TupperwareEarth </i>aims to reduce human planning that involves both cognitive and physical loads of burden by inferring solutions to the activities of daily kitchen living using ontological semantics.

The Internet of Things (IoT) has evolved to be an important part of modern society. IoT devices can be found in several environments such as smart homes, transportation, the health sector, smart cities and even facilitates automation in organisations. The increasing dependence on IoT devices increases the possibility of security incidents in the physical or cyber environment. Traditional methods of digital forensic (DF) investigations are not always applicable to IoT devices due to their limited data processing resources. A possible solution for conducting forensic investigations on IoT devices is to utilise a proactive approach known as digital forensic readiness (DFR). This dissertation firstly aims to conduct a thorough review of the available literature in the current body of knowledge to identify a clear process that can be followed to implement DFR tailored for IoT devices. This dissertation then formulates requirements for DFR in IoT based on existing forensic techniques. The requirements for DFR in IoT give rise to the development of a model for DFR in IoT, which is then implemented in a prototype for IoT devices. The prototype is subsequently tested and evaluated on IoT devices that conduct proactive DFR in a simulation of a smart home system. Finally, the dissertation illustrates the feasibility of the DFR processes for IoT and serves as a basis for future research with regards to DFR in IoT. This dissertation will impact future research with regards to developing a standard for DFR in IoT. / Dissertation (MSc)--University of Pretoria, 2019. / Computer Science / MSc / Unrestricted

The 4th phase of the industrial revolution is focused on the relationship between the humans and the machines and how they can collaborate. We are going to look deeper into the relationship between autonomy and IoT-based solutions in a line organization with the help of a case study. In this case study, we discovered that the industry and their workers will benefit from using IoT-solutions to maintain more competitiveness to the market. With the use of IoT- based solutions such as a digital Dashboard, they get more reliable information and better ways of communication within the organization. Our study emphasizes the necessity of improving the autonomy within an industrial workplace. The personnel in an organization will be able to use IoTin a way to get more creative, gain more responsibility, keep a higher level of quality and make the work procedures more efficient.

El sector MIPYME (Micro, Pequeña y Mediana empresas), representa el 99.5% de empresas formales. El 96.2% son microempresas, 3.2% pequeña y 0.1% mediana (Ministerio de la Producción, 2017). El sector manufactura evoluciona constantemente y para ello requiere incorporar el uso de maquinaria especializada, dicha maquinaria opera principalmente con energía eléctrica. En 2015, el mayor consumo de energía se registró en el sector minero e industrial con 56% (22 440 GWh) del total a nivel nacional. Con respecto a 1995, el consumo del sector minero e industrial se incrementó 466% (Osineming, 2016). Por su lado, la producción de energía eléctrica no ha crecido con la misma magnitud, lo que ha ocasionado el aumento del costo del servicio. Por ejemplo, el cargo por energía activa para Lima Norte se ha incrementado 42% en el periodo 2005-2016 al pasar de 32.8 ctm S/. por kWh a 46.6 ctm S/. por kWh (Osineming, 2016). Actualmente la implementación de medidas de eficiencia energética, particularmente en energía eléctrica, requieren de personal especializado y son ejecutadas como parte de labores de mantenimiento en periodos definidos. Se propone el uso del IoT (Internet of Things) para identificar medidas de eficiencia energética desde una aplicación web, la que puede ser manejada por personal no especializado. El objetivo es definir una línea base de consumo y monitorear el consumo de energía eléctrica, para luego enviar alertas en real cuando el consumo se aleje de la línea base de consumo definida, de manera que el responsable pueda tomar las acciones correctivas necesarias inmediatamente y reducir la facturación generada por uso indebido de energía eléctrica. Estimamos que nuestro servicio tendrá un pago mensual de US$ 520.00, para su inversión estimamos se requiere de $250,000.00. Resultando un VAN de US$ 330 mil y una TIR de 41%, lo que resulta atractivo para inversionistas. / The micro, small and medium-sized businesses (MIPYME, as abbreviated in Spanish) represent 99.5% of formal businesses in Peru. Out of this number, 96.2% are micro, 3.2% are small and 0.1%, medium businesses (Peruvian Ministry of Production, 2017). The manufacturing sector is constantly evolving, which requires incorporating the use of specialized machinery. Such machinery operates mainly with electrical energy. In 2015, the highest energy consumption was recorded in the mining and industrial sector totaling 56% (22,440 GWh) at a national level. The consumption of the mining and industrial sector has increased 466% when compared to 1995 figures (Osinergmin, 2016). Meanwhile, the production of electrical energy has not grown to match these consumption rates, which has caused the costs of the service to increase. Energy costs for northern Lima have increased in 42% from 2005-2016 (actual cost of S/ 46.60 ctm/kWh from S/ 32.80 ctm/kWh) according to Osinergmin (Osinerming, 2016). Currently, the implementation of energy efficiency measures, particularly for electric energy, require specialized staff and are executed as part of maintenance work that takes place at specified times. This paper proposes the use of the IoT (Internet of Things) to identify energy efficiency measures from a web application, which can be handled by non-specialized staff. The objective is to define a baseline consumption and monitor the consumption of electrical energy. As consumption moves away from this baseline, alerts should prompt corrective actions that reduce the turnover generated by excessive electric power. We believe that our service will have a monthly fee of US$ 520.00. A total of US$ 250,000.00 has been calculated as total investment. In addition, an NPV of approximately US$ 330K and an IRR of 41% make it attractive to potential investors. / Trabajo de investigación

Advances in VLSI manufacturing and design technology over the decades have created many computing paradigms for disparate computing needs. With concerns for transmission cost, security, latency of centralized computing, edge/local computing are increasingly prevalent in the faster growing sectors like Internet-of-Things (IoT) and other sectors that require energy/connectivity autonomous systems such as biomedical and industrial applications. Energy and power efficient are the main design constraints in local and edge computing. While there exists a wide range of low power design techniques, they are often underutilized in custom circuit designs as the algorithms are developed independent of the hardware. Such compartmentalized design approach fails to take advantage of the many compatible algorithmic and hardware techniques that can improve the efficiency of the entire system. Algorithm hardware co-design is to explore the design space with whole stack awareness. The main goal of the algorithm hardware co-design methodology is the enablement and improvement of small form factor edge and local VLSI systems operating under strict constraints of area and energy efficiency. This thesis presents selected works of application specific digital and mixed-signal integrated circuit designs. The application space ranges from implantable biomedical devices to edge machine learning acceleration.