Edge intelligence can reduce power dissipation to enable power-hungry long-range wireless applications. This work applies edge intelligence to quantify the reduction in power dissipation. We designed a wireless sensor node with a LoRa radio and implemented a decision tree classifier, in situ, to classify behaviors of cattle. We estimate that employing edge intelligence on our wireless sensor node reduces its average power dissipation by up to a factor of 50, from 20.10 mW to 0.41 mW. We also observe that edge intelligence increases the link budget without significantly affecting average power dissipation. / Master of Science / Battery powered sensor nodes have access to a limited amount of energy. However, many applications of sensor nodes such as animal monitoring require energy intensive, long range data transmissions. In this work, we used machine learning to process motion data within our sensor node to classify cattle behaviors. We estimate that transmitting processed data dissipates up to 50 times less power when compared to transmitting raw data. Due to the properties of our transmission protocol, we also observe that transmitting processed data increases the range of transmissions without impacting power dissipation.

Le secteur des télécommunications passe par une phase délicate en raison de profondes mutations technologiques, principalement motivées par le développement de l'Internet. Elles ont un impact majeur sur l'industrie des télécommunications dans son ensemble et, par conséquent, sur les futurs déploiements des nouveaux réseaux, plateformes et services. L'évolution de l'Internet a un impact particulièrement fort sur les opérateurs des télécommunications (Telcos). En fait, l'industrie des télécommunications est à la veille de changements majeurs en raison de nombreux facteurs, comme par exemple la banalisation progressive de la connectivité, la domination dans le domaine des services de sociétés du web (Webcos), l'importance croissante de solutions à base de logiciels et la flexibilité qu'elles introduisent (par rapport au système statique des opérateurs télécoms). Cette thèse élabore, propose et compare les scénarios possibles basés sur des solutions et des approches qui sont technologiquement viables. Les scénarios identifiés couvrent un large éventail de possibilités: 1) Telco traditionnel; 2) Telco transporteur de Bits; 3) Telco facilitateur de Plateforme; 4) Telco fournisseur de services; 5) Disparition des Telco. Pour chaque scénario, une plateforme viable (selon le point de vue des opérateurs télécoms) est décrite avec ses avantages potentiels et le portefeuille de services qui pourraient être fournis / The telecommunications industry is going through a difficult phase because of profound technological changes, mainly originated by the development of the Internet. They have a major impact on the telecommunications industry as a whole and, consequently, the future deployment of new networks, platforms and services. The evolution of the Internet has a particularly strong impact on telecommunications operators (Telcos). In fact, the telecommunications industry is on the verge of major changes due to many factors, such as the gradual commoditization of connectivity, the dominance of web services companies (Webcos), the growing importance of software based solutions that introduce flexibility (compared to static system of telecom operators). This thesis develops, proposes and compares plausible future scenarios based on future solutions and approaches that will be technologically feasible and viable. Identified scenarios cover a wide range of possibilities: 1) Traditional Telco; 2) Telco as Bit Carrier; 3) Telco as Platform Provider; 4) Telco as Service Provider; 5) Telco Disappearance. For each scenario, a viable platform (from the point of view of telecom operators) is described highlighting the enabled service portfolio and its potential benefits

The Internet of Things (IoT) has deeply changed how we interact with our world. Today, smart homes, self-driving cars, connected industries, and wearables are just a few mainstream applications where IoT plays the role of enabling technology. When IoT became popular, Cloud Computing was already a mature technology able to deliver the computing resources necessary to execute heavy tasks (e.g., data analytic, storage, AI tasks, etc.) on data coming from IoT devices, thus practitioners started to design and implement their applications exploiting this approach. However, after a hype that lasted for a few years, cloud-centric approaches have started showing some of their main limitations when dealing with the connectivity of many devices with remote endpoints, like high latency, bandwidth usage, big data volumes, reliability, privacy, and so on. At the same time, a few new distributed computing paradigms emerged and gained attention. Among all, Edge Computing allows to shift the execution of applications at the edge of the network (a partition of the network physically close to data-sources) and provides improvement over the Cloud Computing paradigm. Its success has been fostered by new powerful embedded computing devices able to satisfy the everyday-increasing computing requirements of many IoT applications. Given this context, how can next-generation IoT applications take advantage of the opportunity offered by Edge Computing to shift the processing from the cloud toward the data sources and exploit everyday-more-powerful devices? This thesis provides the ingredients and the guidelines for practitioners to foster the migration from cloud-centric to novel distributed design approaches for IoT applications at the edge of the network, addressing the issues of the original approach. This requires the design of the processing pipeline of applications by considering the system requirements and constraints imposed by embedded devices. To make this process smoother, the transition is split into different steps starting with the off-loading of the processing (including the Artificial Intelligence algorithms) at the edge of the network, then the distribution of computation across multiple edge devices and even closer to data-sources based on system constraints, and, finally, the optimization of the processing pipeline and AI models to efficiently run on target IoT edge devices. Each step has been validated by delivering a real-world IoT application that fully exploits the novel approach. This paradigm shift leads the way toward the design of Edge Intelligence IoT applications that efficiently and reliably execute Artificial Intelligence models at the edge of the network.

Wireless anomaly detection is a mature field with several unique solutions. This thesis aims to describe a novel way of detecting wireless anomalies using metadata analysis based methods. The metadata is processed and analyzed by a LSTM based Autoencoder and a LSTM based feature analyzer to produce a wide range of anomaly scores. The anomaly scores are then uploaded and analyzed to identify any anomalous fluctuations. An associated tool can also automatically download live data, train, test, and upload results to the Elasticsearch database. The overall method described is in sharp contrast to the more weathered solution of analyzing raw data from a Software Designed Radio, and has the potential to be scaled much more efficiently. / Master of Science / Wireless communications are a major part of our world. Detecting unusual changes in the wireless spectrum is therefore a high priority in maintaining networks and more. This paper describes a method that allows centralized processing of wireless network output, allowing monitoring of several areas simultaneously. This is in sharp contrast to other methods which generally must be located near the area being monitored. In addition, this implementation has the capability to be scaled more efficiently as the hardware required to monitor is less costly than the hardware required to process wireless data.

The telecommunications industry is going through a difficult phase because of profound technological changes, mainly originated by the development of the Internet. They have a major impact on the telecommunications industry as a whole and, consequently, the future deployment of new networks, platforms and services. The evolution of the Internet has a particularly strong impact on telecommunications operators (Telcos). In fact, the telecommunications industry is on the verge of major changes due to many factors, such as the gradual commoditization of connectivity, the dominance of web services companies (Webcos), the growing importance of software based solutions that introduce flexibility (compared to static system of telecom operators). This thesis develops, proposes and compares plausible future scenarios based on future solutions and approaches that will be technologically feasible and viable. Identified scenarios cover a wide range of possibilities: 1) Traditional Telco; 2) Telco as Bit Carrier; 3) Telco as Platform Provider; 4) Telco as Service Provider; 5) Telco Disappearance. For each scenario, a viable platform (from the point of view of telecom operators) is described highlighting the enabled service portfolio and its potential benefits

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<p>The swift emergence and wide-ranging utilization of machine learning (ML) across various industries, including healthcare, transportation, and robotics, have underscored the escalating need for efficient, scalable, and privacy-preserving solutions. Recognizing this, we present an integrated examination of three novel frameworks, each addressing different aspects of distributed learning and privacy issues: Two Timescale Hybrid Federated Learning (TT-HF), Delay-Aware Federated Learning (DFL), and Differential Privacy Hierarchical Federated Learning (DP-HFL). TT-HF introduces a semi-decentralized architecture that combines device-to-server and device-to-device (D2D) communications. Devices execute multiple stochastic gradient descent iterations on their datasets and sporadically synchronize model parameters via D2D communications. A unique adaptive control algorithm optimizes step size, D2D communication rounds, and global aggregation period to minimize network resource utilization and achieve a sublinear convergence rate. TT-HF outperforms conventional FL approaches in terms of model accuracy, energy consumption, and resilience against outages. DFL focuses on enhancing distributed ML training efficiency by accounting for communication delays between edge and cloud. It also uses multiple stochastic gradient descent iterations and periodically consolidates model parameters via edge servers. The adaptive control algorithm for DFL mitigates energy consumption and edge-to-cloud latency, resulting in faster global model convergence, reduced resource consumption, and robustness against delays. Lastly, DP-HFL is introduced to combat privacy vulnerabilities in FL. Merging the benefits of FL and Hierarchical Differential Privacy (HDP), DP-HFL significantly reduces the need for differential privacy noise while maintaining model performance, exhibiting an optimal privacy-performance trade-off. Theoretical analysis under both convex and nonconvex loss functions confirms DP-HFL’s effectiveness regarding convergence speed, privacy performance trade-off, and potential performance enhancement with appropriate network configuration. In sum, the study thoroughly explores TT-HF, DFL, and DP-HFL, and their unique solutions to distributed learning challenges such as efficiency, latency, and privacy concerns. These advanced FL frameworks have considerable potential to further enable effective, efficient, and secure distributed learning.</p>