The IoT offers enormous potentials thanks to its Widespread adoption by many industries, individuals, and governments, leading explosive growth and remarkable breakthroughs that have made it a technology with seemingly boundless applications. However, the far-reaching IoT applications cum its characteristic heterogeneity and ubiquity come with a huge price for more security vulnerabilities, making the deployed IoT systems increasingly susceptible to, and prime targets of many different physical and cyber-attacks including insider attacks, thereby growing the overall security risks to the systems. This research, which focuses on addressing insider attacks on IoT, studies the likelihood of malicious insiders' activities compromising some of the security triad of Confidentiality, Integrity and Availability (CIA) of a supposedly secure IoT system with implemented security mechanisms. To further establish the vulnerability of the IoT systems to the insider attack being investigated in our research, we first produced a research output that emphasized the need for multi-layer security of the overall system and proposed the implementation of security mechanisms on components at all layers of the IoT system to safeguard the system and ensure its CIA. Those conventional measures however do not safeguard against insider attacks, as found by our experimental investigation of a working IoT system prototype. The outcome of the investigation therefore necessitates our proposed solution to the problem, which leverages the integration of distributed edge computing with decentralized Ethereum blockchain technology to provide countermeasures that preserve the Integrity of the IoT system data and improve effectiveness of the system. We employed the power of Ethereum smart contracts to perform integrity checks on the system data logically and take risk management decisions. We considered the industry use case of Downstream Petroleum sector for application of our solution. The solution was evaluated using datasets from different experimental settings and showed up to 86% accuracy rate. / Government of the Federal Republic of Nigeria through the Petroleum Technology Development Fund (PTDF) Overseas Scholarship Scheme (OSS)

Edge computing can provide decentralized computation and storage resources with low latency and high bandwidth. It is a promising infrastructure to host services with stringent latency requirements, for example autonomous driving, cloud gaming, and telesurgery to the customers. Because of the structural complexity associated with the edge computing applications, research topics like service placement gain great importance. To provide a realistic and efficient general environment for evaluating service placement solutions that can be used to analyze latency requirements of services at scale, a new testing tool for mobile edge cloud is designed and implemented in this thesis. The proposed tool is implemented as a cloud native application, and allows deploying applications in an edge computing infrastructure that consists of Kubernetes and Istio, it can be easily scaled up to several hundreds of microservices, and deployment into the edge clusters is automated. With the help of the designed tool, two different microservice placement algorithms are evaluated in an emulated edge computing environment based on Federated Kubernetes. The results have shown how the performance of algorithms varies when the parameters of the environment, and the applications instantiated and deployed by the tool are changed. For example, increasing the request rate 200% can increase the delay by 100% for different algorithms. Moreover, complicating the mobile network can improve the latency performance up to 20% depending on the microservice placement algorithm. / Edge computing kan ge decentraliserad beräkning och lagringsresurser med låg latens och hög bandbredd. Det är en lovande infrastruktur för att vara värd för tjänster med strängt prestandakrav, till exempel autonom körning, molnspel och telekirurgi till kunderna. På grund av den strukturella komplexiteten som är associerad med edge computing applikationerna, får forskningsämnen som tjänsteplacering stor betydelse. För att tillhandahålla en realistisk och effektiv allmän miljö för utvärdering av lösningar för tjänsteplacering, designas och implementeras ett nytt testverktyg för mobilt kantmoln i denna avhandling. Det föreslagna verktyget implementeras på molnmässigt sätt som gör det möjligt att distribuera applikationer i en edge computing-infrastruktur som består av Kubernetes och Istio. Med hjälp av det konstruerade verktyget utvärderas två olika placeringsalgoritmer för mikrotjänster i en realistisk edge computing miljö. Resultaten visar att en ökning av förfrågningsgraden 200 % kan öka förseningen med 100 % för olika algoritmer. Dessutom kan komplicering av mobilnätet förbättra latensprestanda upp till 20% beroende på algoritmen för mikroserviceplaceringen.

Yes / Internet of Things (IoT) platforms are responsible for overall data processing in the IoT System. This ranges from analytics and big data processing to gathering all sensor data over time to analyze and produce long-term trends. However, this comes with prohibitively high demand for resources such as memory, computing power and bandwidth, which the highly resource constrained IoT devices lack to send data to the platforms to achieve efficient operations. This results in poor availability and risk of data loss due to single point of failure should the cloud platforms suffer attacks. The integrity of the data can also be compromised by an insider, such as a malicious system administrator, without leaving traces of their actions. To address these issues, we propose in this work an edge-based blockchain enabled anomaly detection technique to prevent insider attacks in IoT. The technique first employs the power of edge computing to reduce the latency and bandwidth requirements by taking processing closer to the IoT nodes, hence improving availability, and avoiding single point of failure. It then leverages some aspect of sequence-based anomaly detection, while integrating distributed edge with blockchain that offers smart contracts to perform detection and correction of abnormalities in incoming sensor data. Evaluation of our technique using real IoT system datasets showed that the technique remarkably achieved the intended purpose, while ensuring integrity and availability of the data which is critical to IoT success. / Petroleum Technology Development Fund(PTDF) Nigeria, Grant/Award Number:PTDF/ED/PHD/TYM/858/16

Tesis por compendio / Products related with Intelligent Transportation Systems (ITS) are becoming a reality on our roads. All car manufacturers are starting to include Internet access in their vehicles and to integrate smartphones directly from the dashboard, but more and more services will be introduced in the near future. Connectivity through "vehicular networks" will become a cornerstone of every new proposal, and offering an adequate quality of service is obviously desirable. However, a lot of work is needed for vehicular networks to offer performances similar to those of the wired networks. Vehicular networks can be characterized by two main features: high variability due to mobility levels that can reach up to 250 kilometers per hour, and heterogeneity, being that various competing versions from different vendors have and will be released. Therefore, to make the deployment of efficient services possible, an extensive study must be carried out and adequate tools must be proposed and developed. This PhD thesis addresses the service deployment problem in these networks at three different levels: (i) the physical and link layer, showing an exhaustive analysis of the physical channel and models; (ii) the network layer, proposing a forwarding protocol for IP packets; and (iii) the transport layer, where protocols are proposed to improve data delivery. First of all, the two main wireless technologies used in vehicular networks where studied and modeled, namely the 802.11 family of standards, particularly 802.11p, and the cellular networks focusing on LTE. Since 802.11p is a quite mature standard, we defined (i) a propagation and attenuation model capable of replicating the transmission range and the fading behavior of real 802.11p devices, both in line-of-sight conditions and when obstructed by small obstacles, and (ii) a visibility model able to deal with large obstacles, such as buildings and houses, in a realistic manner. Additionally, we proposed a model based on high-level performance indicators (bandwidth and delay) for LTE, which makes application validation and evaluation easier. At the network layer, a hybrid protocol called AVE is proposed for packet forwarding by switching among a set of standard routing strategies. Depending on the specific scenario, AVE selects one out of four different routing solutions: a) two-hop direct delivery, b) Dynamic MANET On-demand (DYMO), c) greedy georouting, and d) store-carry-and-forward technique, to dynamically adapt its behavior to the specific situation. At the transport layer, we proposed a content delivery protocol for reliable and bidirectional unicast communication in lossy links that improves content delivery in situations where the wireless network is the bottleneck. It has been designed, validated, optimized, and its performance has been analyzed in terms of throughput and resource efficiency. Finally, at system level, we propose an edge-assisted computing model that allows reducing the response latency of several queries by placing a computing unit at the network edge. This way, traffic traversal through the Internet is avoided when not needed. This scheme could be used in both 802.11p and cellular networks, and in this thesis we decided to focus on its evaluation using LTE networks. The platform presented in this thesis combines all the individual efforts to create a single efficient platform. This new environment could be used by any provider to improve the quality of the user experience obtainable through the proposed vehicular network-based services. / Los productos relacionados con los Sistemas Inteligentes de Transporte (ITS) se están transformando en una realidad en nuestras carreteras. Todos los fabricantes de coches comienzan a incluir acceso a internet en sus vehículos y a facilitar su integración con los teléfonos móviles, pero más y más servicios se introducirán en el futuro. La conectividad usando las "redes vehiculares" se convertirá en la piedra angular de cada nueva propuesta, y ofrecer una calidad de servicio adecuada será, obviamente, deseable. Sin embargo, se necesita una gran cantidad de trabajo para que las redes vehiculares ofrezcan un rendimiento similar al de las redes cableadas. Las redes vehiculares quedan definidas por sus dos características básicas: alto dinamismo, pues los nodos pueden alcanzar una velocidad relativa de más de 250 km/h; y heterogeneidad, por la gran cantidad de propuestas diferentes que los fabricantes están lanzando al mercado. Por ello, para hacer posible el despliegue de servicios sobre ellas, se impone la necesidad de hacer un estudio en profundidad de este entorno, y deben de proponerse y desarrollarse las herramientas adecuadas. Esta tesis ataca la problemática del despliegue de servicios en estas redes a tres niveles diferentes: (i) el nivel físico y de enlace, mostrando varios análisis en profundidad del medio físico y modelos derivados para su simulación; (ii) el nivel de red, proponiendo un protocolo de difusión de la información para los paquetes IP; y (iii) el nivel de transporte, donde otros protocolos son propuestos para mejorar el rendimiento del transporte de datos. En primer lugar, se han estudiado y modelado las dos principales tecnologías inalámbricas que se utilizan para la comunicación en redes vehiculares, la rama de estándares 802.11, en concreto 802.11p; y la comunicación celular, en particular LTE. Dado que el estándar 802.11p es un estándar bastante maduro, nos centramos en crear (i) un modelo de propagación y atenuación capaz de replicar el rango de transmisión de dispositivos 802.11p reales, en condiciones de visión directa y obstrucción por pequeños obstáculos, y (ii) un modelo de visibilidad capaz de simular el efecto de grandes obstáculos, como son los edifcios, de una manera realista. Además, proponemos un modelo basado en indicadores de rendimiento de alto nivel (ancho de banda y retardo) para LTE, que facilita la validación y evaluación de aplicaciones. En el plano de red, se propone un protocolo híbrido, llamado AVE, para el encaminamiento y reenvío de paquetes usando un conjunto de estrategias estándar de enrutamiento. Dependiendo del escenario, AVE elige entre cuatro estrategias diferentes: a) entrega directa a dos saltos, b) Dynamic MANET On-demand (DYMO) c) georouting voraz, y d) una técnica store-carry-and- forward, para adaptar su comportamiento dinámicamente a cada situación. En el plano de transporte, se propone un protocolo bidireccional de distribución de contenidos en canales con pérdidas que mejora la entrega de contenidos en situaciones en las que la red es un cuello de botella, como las redes inalámbricas. Ha sido diseñado, validado, optimizado, y su rendimiento ha sido analizado en términos de productividad y eficiencia en la utilización de recursos. Finalmente, a nivel de sistema, proponemos un modelo de computación asistida que permite reducir la latencia en la respuesta a muchas consultas colocando una unidad de computación en el borde de la red, i.e., la red de acceso. Este esquema podría ser usado en redes basadas en 802.11p y en redes celulares, si bien en esta tesis decidimos centrarnos en su evaluación usando redes LTE. La plataforma presentada en esta tesis combina todos los esfuerzos individuales para crear una plataforma única y eficiente. Este nuevo entorno puede ser usado por cualquier proveedor para mejorar la calidad de la experiencia de usuario en los servicios desplegados sobre redes vehiculares. / Els productes relacionats amb els sistemes intel · ligents de transport (ITS) s'estan transformant en una realitat en les nostres carreteres. Tots els fabri- cants de cotxes comencen a incloure accés a internet en els vehicles i a facilitar- ne la integració amb els telèfons mòbils, però en el futur més i més serveis s'hi introduiran. La connectivitat usant les xarxes vehicular esdevindrà la pedra angular de cada nova proposta, i oferir una qualitat de servei adequada serà, òbviament, desitjable. No obstant això, es necessita una gran quantitat de treball perquè les xarxes vehiculars oferisquen un rendiment similar al de les xarxes cablejades. Les xarxes vehiculars queden definides per dues característiques bàsiques: alt dinamisme, ja que els nodes poden arribar a una velocitat relativa de més de 250 km/h; i heterogeneïtat, per la gran quantitat de propostes diferents que els fabricants estan llançant al mercat. Per això, per a fer possible el desplegament de serveis sobre aquestes xarxes, s'imposa la necessitat de fer un estudi en profunditat d'aquest entorn, i cal proposar i desenvolupar les eines adequades. Aquesta tesi ataca la problemàtica del desplegament de serveis en aquestes xarxes a tres nivells diferents: (i) el nivell físic i d'enllaç , mostrant diverses anàlisis en profunditat del medi físic i models derivats per simular-lo; (ii) el nivell de xarxa, proposant un protocol de difusió de la informació per als paquets IP; i (iii) el nivell de transport, on es proposen altres protocols per a millorar el rendiment del transport de dades. En primer lloc, s'han estudiat i modelat les dues principals tecnologies sense fils que s'utilitzen per a la comunicació en xarxes vehiculars, la branca d'estàndards 802.11, en concret 802.11p; i la comunicació cel · lular, en partic- ular LTE. Atès que l'estàndard 802.11p és un estàndard bastant madur, ens centrem a crear (i) un model de propagació i atenuació capaç de replicar el rang de transmissió de dispositius 802.11p reals, en condicions de visió directa i obstrucció per petits obstacles, i (ii) un model de visibilitat capaç de simular l'efecte de grans obstacles, com són els edificis, d'una manera realista. A més, proposem un model basat en indicadors de rendiment d'alt nivell (ample de banda i retard) per a LTE, que facilita la validació i l'avaluació d'aplicacions. En el pla de xarxa, es proposa un protocol híbrid, anomenat AVE, per a l'encaminament i el reenviament de paquets usant un conjunt d'estratègies estàndard d'encaminament. Depenent de l'escenari , AVE tria entre quatre estratègies diferents: a) lliurament directe a dos salts, b) Dynamic MANET On-demand (DYMO) c) georouting voraç, i d) una tècnica store-carry-and- forward, per a adaptar-ne el comportament dinàmicament a cada situació. En el pla de transport, es proposa un protocol bidireccional de distribució de continguts en canals amb pèrdues que millora el lliurament de continguts en situacions en què la xarxa és un coll de botella, com les xarxes sense fils. Ha sigut dissenyat, validat, optimitzat, i el seu rendiment ha sigut analitzat en termes de productivitat i eficiència en la utilització de recursos. Finalment, a nivell de sistema, proposem un model de computació assistida que permet reduir la latència en la resposta a moltes consultes col · locant una unitat de computació a la vora de la xarxa, és a dir, la xarxa d'accés. Aquest esquema podria ser usat en xarxes basades en 802.11p i en xarxes cel · lulars, si bé en aquesta tesi decidim centrar-nos en la seua avaluació usant xarxes LTE. La plataforma presentada en aquesta tesi combina tots els esforços indi- viduals per a crear una plataforma única i eficient. Aquest nou entorn pot ser usat per qualsevol proveïdor per a millorar la qualitat de l'experiència d'usuari en els serveis desplegats sobre xarxes vehiculars. / Báguena Albaladejo, M. (2017). Design and implementation of simulation tools, protocols and architectures to support service platforms on vehicular networks [Tesis doctoral]. Universitat Politècnica de València. https://doi.org/10.4995/Thesis/10251/85333 / Compendio

<p dir="ltr">Mobile vision is becoming the norm, transforming our daily lives. It powers numerous applications, enabling seamless interactions between the digital and physical worlds, such as augmented reality, real-time object detection, and many others. The popularity of mobile vision has spurred advancements from both computer vision (CV) and mobile edge computing (MEC) communities. The former focuses on improving analytics accuracy through the use of proper deep neural networks (DNNs), while the latter addresses the resource limitations of mobile environments by coordinating tasks between mobile and edge devices, determining which data to transmit and process to enable real-time performance. </p><p dir="ltr"> Despite recent advancements, existing approaches typically integrate the functionalities of the two camps at a basic task level. They rely on a uniform on-device processing scheme that streams the same type of data and uses the same DNN model for identical CV tasks, regardless of the analytical complexity of the current input, input size, or latency requirements. This lack of adaptability to dynamic contexts limits their ability to achieve optimal efficiency in scenarios involving diverse source data, varying computational resources, and differing application requirements. </p><p dir="ltr">Our approach seeks to move beyond task-level adaptation by emphasizing customized optimizations tailored to dynamic use scenarios. This involves three key adaptive strategies: dynamically compressing source data based on contextual information, selecting the appropriate computing model (e.g., DNN or sub-DNN) for the vision task, and establishing a feedback mechanism for context-aware runtime tuning. Additionally, for scenarios involving movable cameras, the feedback mechanism guides the data capture process to further enhance performance. These innovations are explored across three use cases categorized by the capture device: one stationary camera, one moving camera, and cross-camera analytics. </p><p dir="ltr">My dissertation begins with a stationary camera scenario, where we improve efficiency by adapting to the use context on both the device and edge sides. On the device side, we explore a broader compression space and implement adaptive compression based on data context. Specifically, we leverage changes in confidence scores as feedback to guide on-device compression, progressively reducing data volume while preserving the accuracy of visual analytics. On the edge side, instead of training a specialized DNN for each deployment scenario, we adaptively select the best-fit sub-network for the given context. A shallow sub-network is used to “test the waters”, accelerating the search for a deep sub-network that maximizes analytical accuracy while meeting latency requirements.</p><p dir="ltr"> Next, we explore scenarios involving a moving camera, such as those mounted on drones. These introduce new challenges, including increased data encoding demands due to camera movement and degraded analytics performance (e.g., tracking) caused by changing perspectives. To address these issues, we leverage drone-specific domain knowledge to optimize compression for object detection by applying global motion compensation and assigning different resolutions at a tile-granularity level based on the far-near effect. Furthermore, we tackle the more complex task of object tracking and following, where the analytics results directly influence the drone’s navigation. To enable effective target following with minimal processing overhead, we design an adaptive frame rate tracking mechanism that dynamically adjusts based on changing contexts.</p><p dir="ltr"> Last but not least, we extend the work to cross-camera analytics, focusing on coordination between one stationary ground-based camera and one moving aerial camera. The primary challenge lies in addressing significant misalignments (e.g., scale, rotation, and lighting variations) between the two perspectives. To overcome these issues, we propose a multi-exit matching mechanism that prioritizes local feature matching while incorporating global features and additional cues, such as color and location, to refine matches as needed. This approach ensures accurate identification of the same target across viewpoints while minimizing computational overhead by dynamically adapting to the complexity of the matching task. </p><p dir="ltr">While the current work primarily addresses ideal conditions, assuming favorable weather, optimal lighting, and reliable network performance, it establishes a solid foundation for future innovations in adaptive video processing under more challenging conditions. Future efforts will focus on enhancing robustness against adversarial factors, such as sensing data drift and transmission losses. Additionally, we plan to explore multi-camera coordination and multimodal data integration, leveraging the growing potential of large language models to further advance this field.</p>

The evolution and deployment of fifth-generation (5G) and beyond (B5G) infrastructure will require a tremendous effort to specify the design, standards, and manufacturing. 5G is vital to modern technological evolution, including industry 4.0, automotive, entertainment, and health care. The ambitious and challenging 5G project is classified into three categories, which provide an essential supporting platform for applications associated with: Enhanced mobile broadband (eMBB) Ultra-reliable low-latency communication (URLLC) Massive machine-type communication (mMTC) The demand for URLLC grows, particularly for applications like autonomous guided vehicles (AGVs), unmanned aerial vehicles (UAVs), and factory automation, and has a strict requirement of low latency of 1 ms and high reliability of 99.999%. To meet the needs of communication-sensitive and computation-intensive applications with different quality-of-service (QoS) requirements, this evolution focuses on ultra-dense edge networks with multi-access edge computing (MEC) facilities. MEC emerges as a solution, placing resourceful servers closer to users. However, the dynamic nature of processing and interaction patterns necessitates effective network control, which is challenging due to stringent requirements on both communication and computation. In this context, we introduce a novel approach to optimally manage task offloading, considering the intricacies of heterogeneous computing and communication services. Unlike existing methods, our methodology incorporates the number of admitted service migrations and QoS upper and lower bounds as binding constraints. The comprehensive model encompasses agent positions, MEC servers, QoS requirements, edge network communication, and server computing capabilities. Formulated as a mixed-integer linear program (MILP), it provides an optimal schedule for service migrations and bandwidth allocation, addressing the challenges posed by computation-intensive and communication-sensitive applications. Moreover, tailoring to an indoor robotics environment, we explore optimization-based approaches seeking an optimal system-level architecture while considering QoS guarantees. Optimization tools, e.g., ARCHEX, prove their ability to capture cyber-physical systems (CPS) requirements and generate correct-by-construction architectural solutions. We propose an extension in ARCHEX by incorporating dynamic properties, i.e., robot trajectories, time dimension, application-specific QoS constraints, and finally, integrating the optimization tool with a discrete-event network simulator (OMNeT++). This extension automates the generation of configuration files and facilitates result analysis, ensuring a comprehensive evaluation. This part of the work focuses on the dynamism of robots, server-to-service mapping, and the integration of automated simulation. The proposed extension is validated by optimizing and analyzing various indoor robotics scenarios, emphasizing critical performance parameters such as overall throughput and end-to-end delay (E2E). This integrated approach addresses the complex interplay of computation and communication resources, providing a solution for dynamic mobility, traffic, and application patterns in edge server environments.

<p dir="ltr">Immersive applications such as augmented reality (AR) and mixed reality (MR) often need to perform latency-critical analytics tasks on every frame captured on camera. These tasks, often powered by deep neural networks (DNNs) for their superior accuracy, necessitate offloading to edge servers with GPUs due to their computational intensity. Achieving high accuracy and efficient AR task offloading faces two fundamental challenges untapped by prior work: (1) In practice, multiple DNN-supported tasks need to offload concurrently to achieve the app functionality -- how to schedule such offloaded tasks on the client which compete for shared edge server resources to maximize the app QoE? (2) Concurrent AR clients from a large user base offload to a cluster of GPU servers -- how to schedule the offloaded tasks on the servers to maximize the number of clients served and lower the operating cost?</p><p dir="ltr">To tackle the first challenge, we design a framework, AccuMO, that balances the offloading frequencies of different tasks by dynamically scheduling the offloading of multiple tasks from an AR client to an edge server, thereby optimizing the overall accuracy across tasks and hence app QoE. Our design employs two novel ideas: (1) task-specific lightweight models that predict offloading accuracy drop as a function of offloading frequency and frame content, and (2) a general two-level control feedback loop that concurrently balances offloading among tasks and adapts between offloading and using local algorithms for each task.</p><p dir="ltr">We tackle the challenge of supporting concurrent AR clients in two steps. We first focus on maximizing the capacity of individual edge servers, where we present ARISE, which untangles the intricate interplay between per-client offloading schedule and batched inference on the server by proactively coordinating offloading requests from different AR clients. In the second step, we focus on a cluster setup of heterogeneous GPU servers which exposes the synergy between diversity in both DNN layers and GPU architectures, manifesting as comparable inference latency for many layers in DNN models when running on low-class and high-class GPUs. We exploit such overlooked capability of low-class GPUs using pipeline parallelism and present a novel inference serving system, IPIPE, that employs pool-based pipeline parallelism with a mixed-integer linear programming (MILP)-based control plane and a data plane that performs resource reservation-based adaptive batching.</p>

<p>Recent advancements in deep learning (DL) and high-communication bandwidth access networks such as 5G enable applications that require intelligence and faster computational power at the edge with low power consumption. In this thesis, we study how to improve the Quality-of-Experience (QoE) of these emerging 5G applications, e.g., virtual reality (VR) and video analytics on edge devices. These 5G applications either require high-quality visual effects with a stringent latency requirement (for VR) or high analytics accuracy (for video analytics) while maintaining frame rate requirements under dynamic conditions. </p> <p>In part 1, we study how to support high-quality untethered immersive multiplayer VR on commodity mobile devices. Simply replicating the prior-art for a single-user VR will result in a linear increase in network bandwidth requirement that exceeds the bandwidth of WiFi (802.11ac). We propose a novel technique, <em>Coterie, </em>that splits the rendering of background environment (BE) frames between the mobile device and the edge server that drastically enhances the similarity of the BE frames and reduces the network load via frame caching and reuse. Our proposed VR framework, Coterie, reduces per-player network requirement by over 10x and easily supports 4 players on Pixel 2 over 802.11ac while maintaining the QoE constraints of 4K VR.</p> <p>In part 2, we study how to achieve high accuracy of analytics in video analytics pipelines (VAP). We observe that the frames captured by the surveillance video cameras powering a variety of 24X7 analytics applications are not always pristine -- they can be distorted due to environmental condition changes, lighting issues, sensor noise, compression, etc. Such distortions not only deteriorate the accuracy of deep learning applications but also negatively impact the utilization of the edge server resources used to run these computationally expensive DL models. First, we study how to dynamically filter out low-quality frames captured. We propose a lightweight DL-based quality estimator, <em>AQuA</em>, that can be used to filter out low-quality frames that can lead to high-confidence errors (false-positives) if fed into the analytic units (AU) in the VAP. AQuA-filter reduces false positives by 17% and the compute and network usage by up to 27% when used in a face-recognition VAP. Second, we study how to reduce such poor-quality frame captures by the camera. We propose <em>CamTuner, </em>a system that automatically and dynamically adapts the complex camera settings to changing environmental conditions based on analytical quality estimation to enhance the accuracy of video analytics. In a real customer deployment, <em>CamTuner</em> enhances VAP accuracy by detecting 15.9% additional persons and 2.6%–4.2% additional cars (without any false positives) than the default camera setting. While <em>CamTuner</em> focuses on improving the accuracy of single-AU running on a camera stream, next we present <em>Elixir</em>, a system that enhances the video stream quality for multiple analytics on a video stream by jointly optimizing different AUs’ objectives. In a real-world deployment, <em>Elixir</em> correctly detects 7.1% (22,068) and 5.0% (15,731) more cars, 94% (551) and 72% (478) more faces, and 670.4% (4975) and 158.6% (3507) more persons than the default-camera-setting and time-sharing approaches, respectively.</p>

Advantages of cache cooperation on edge cache networks serving dynamic web content were studied. Design of cooperative edge cache grid a large-scale cooperative edge cache network for delivering highly dynamic web content with varying server update frequencies was presented. A cache clouds-based architecture was proposed to promote low-cost cache cooperation in cooperative edge cache grid. An Internet landmarks-based scheme, called selective landmarks-based server-distance sensitive clustering scheme, for grouping edge caches into cooperative clouds was presented. Dynamic hashing technique for efficient, load-balanced, and reliable documents lookups and updates was presented. Utility-based scheme for cooperative document placement in cache clouds was proposed. The proposed architecture and techniques were evaluated through trace-based simulations using both real-world and synthetic traces. Results showed that the proposed techniques provide significant performance benefits. A framework for automatically detecting cache-effective fragments in dynamic web pages was presented. Two types of fragments in web pages, namely, shared fragments and lifetime-personalization fragments were identified and formally defined. A hierarchical fragment-aware web page model called the augmented-fragment tree model was proposed. An efficient algorithm to detect maximal fragments that are shared among multiple documents was proposed. A practical algorithm for detecting fragments based on their lifetime and personalization characteristics was designed. The proposed framework and algorithms were evaluated through experiments on real web sites. The effect of adopting the detected fragments on web-caches and origin-servers is experimentally studied.

Fifth Generation (5G) cellular networks will experience a thousand-fold increase in data traffic with over 100 billion connected devices by 2020. In order to support this skyrocketing traffic demand, smaller base stations (BSs) are deployed to increase capacity. However, more BSs increase energy consumption which contributes to operational expenditure (OPEX) and CO2 emissions. Also, an introduction of a plethora of 5G applications running in the mobile devices cause a significant amount of energy consumption in the mobile devices. This thesis presents a novel framework for energy efficiency in 5G cloud radio access networks (C-RAN) by leveraging cloud computing technology. Energy efficiency is achieved in three ways; (i) at the radio side of H-C-RAN (Heterogeneous C-RAN), a dynamic BS switching off algorithm is proposed to minimise energy consumption while maintaining Quality of Service (QoS), (ii) in the BS cloud, baseband workload consolidation schemes are proposed based on simulated annealing and genetic algorithms to minimise energy consumption in the cloud, where also advanced fuzzy based admission control with pre-emption is implemented to improve QoS and resource utilisation (iii) at the mobile device side, Mobile Edge Computing (MEC) is used where computer intensive tasks from the mobile device are executed in the MEC server in the cloud. The simulation results show that the proposed framework effectively reduced energy consumption by up to 48% within RAN and 57% in the mobile devices, and improved network energy efficiency by a factor of 10, network throughput by a factor of 2.7 and resource utilisation by 54% while maintaining QoS.