Increasing number of devices demand for low latency and high-speed data transmission require that the computation resources to be closer to users. The emerging Mobile Edge Computing (MEC) technology aims to bring the advantages of cloud computing which are computation, storage and networking capabilities in close proximity of user. MEC servers are also integrated with cloud servers which give them flexibility of reaching vast computational power whenever needed. In this thesis, leveraging the idea of Mobile Edge Computing, we propose algorithms for cost-efficient and energy-efficient the placement of Mobile Edge nodes. We focus on uplink energy-efficiency which is essential for certain applications including augmented reality and connected vehicles, as well as extending battery life of user equipment that is favorable for all applications. The experimental results show that our proposed schemes significantly reduce the uplink energy of devices and minimizes the number of edge nodes required in the network.

Mobile phones have evolved from feature phones to smart phones with processing power that can compete with personal computers ten years ago. Nevertheless, the computing power of personal computers has also multiplied in the past decade. Consequently, the gap between mobile platforms and personal computers and servers still exists. Mobile Cloud Computing (MCC) has emerged as a paradigm that leverages this difference in processing power. It achieve this goal by augmenting smart phones with resources from the cloud, including processing power and storage capacity. Recently, Mobile Edge Computing (MEC) has brought the benefits from MCC one hop away from the end user. Furthermore, it also provides additional advantages, e.g., access to network context information, reduced latency, and location awareness. This thesis explores the advantages provided by MEC in practice by augmenting an existing application called Human-Centric Positioning System (HoPS). HoPS is a system that relies on context information and information extracted from a photograph of signposts to estimate a user's location. This thesis presents the challenges of enabling HoPS in practice, and implement strategies that make use of the advantages provided by MEC to tackle the challenges. Afterwards, it presents an evaluation of the resulting system, and discusses the implications of the results. To summarise, we make three primary contributions in this thesis: (1) we find out that it is possible to augment HoPS and improve its response time by a factor of four by offloading the code processing; (2) we can improve the overall accuracy of HoPS by leveraging additional processing power at the MEC; (3) we observe that improved network conditions can lead to reduced response time, nevertheless, the difference becomes insignificant compared with the heavy processing required. / Utvecklingen av mobiltelefoner har skett på en rusande takt. Dagens smartphones har mer processorkraft än vad stationära datorer hade för tio år sen. Samtidigt så har även datorernas processorer blivit mycket starkare. Därmed så finns det fortfarande klyftor mellan mobil plattform och datorer och servrar. Mobile Cloud Computing (MCC) används idag som en hävstång för de olika plattformernas processorkraft. Den uppnår detta genom att förbättra smartphonens processorkraft och datorminne med hjälp från datormolnet. På sistånde så har Mobile Edge Computing (MEC) gjort så att förmånerna med MCC är ett steg ifrån slutanvändaren. Dessutom så finns det andra fördelar med MEC, till exempel tillgång till nätverkssammanhangsinformation, reducerad latens, och platsmedvetenhet. Denna tes utforskar de praktiska fördelarna med MEC genom att använda tillämpningsprogrammet Human-Centric Positioning System (HoPS). HoPS är ett system som försöker att hitta platsen där användaren befinner sig på genom att använda sammanhängande information samt information från bilder med vägvisare. Tesen presenterar även de hinder som kan uppstå när HoPS implementeras i verkligheten, och använder förmåner från MEC för att hitta lösningar till eventuella hinder. Sedan så utvärderar och diskuterar tesen det resulterande systemet. För att sammanfatta så består tesen av tre huvuddelar: (1) vi tar reda på att det är möjligt att förbättra HoPS och minska svarstiden med en fjärdedel genom att avlasta kodsprocessen; (2) vi tar reda på att man kan generellt förbättra HoPS noggrannhet genom att använda den utökade processorkraften från MEC; (3) vi ser att förbättrade nätverksförutsättningar kan leda till minskad svarstid, dock så är skillnaden försumbar jämfört med hur mycket bearbetning av information som krävs.

Recent years have witnessed an increasing number of mobile devices such as smartphones and tablets characterized by low computing and storage capabilities. Meanwhile, there is an explosive growth of applications on mobile devices that require high computing and storage capabilities. These challenges lead to the introduction of cloud computing empowering mobile devices with remote computing and storage resources. However, cloud computing is centrally designed, thus encountering noticeable issues such as high communication latency and potential vulnerability. To tackle these problems posed by central cloud computing, Mobile Edge Cloud (MEC) has been recently introduced to bring the computing and storage resources in proximity to mobile devices, such as at base stations or shopping centers. Therefore, MEC has become a key enabling technology for various emerging use cases such as autonomous driving and tactile internet. Despite such a potential benefit, the design of MEC is challenging for the deployment of applications. First, as MEC aims to bring computation and storage resources closer to mobile devices, MEC servers that provide those resources become incredibly diverse in the network. Moreover, MEC servers typically have a small footprint design to flexibly place at various locations, thus providing limited resources. The challenge is to deploy applications in a cost-efficient manner. Second, applications have stringent requirements such as high mobility or low latency. The challenge is to deploy applications in MEC to satisfy their needs. Considering the above challenges, this thesis aims to study the orchestration of MEC applications. In particular, for computation offloading, we propose offloading schemes for immersive applications in MEC such as Augmented Reality or Virtual Reality (AR/VR) by employing application characteristics. For resource optimization, since many MEC applications such as gaming and streaming applications require the support of network functions such as encoder and decoder, we first present placement schemes that allow efficiently sharing network functions between multiple MEC applications. We then introduce the design of the proposed MANO framework in MEC, advocating the joint orchestration between MEC applications and network functions. For mobility support, low latency applications for use cases such as autonomous driving have to seamlessly migrate from one MEC server to another MEC server following the mobility of mobile device, to guarantee low latency communication. Traditional migration approaches based on virtual machine (VM) or container migration attempt to suspend the application at one MEC server and then recover it at another MEC server. These approaches require the transfer of the entire VM or container state and consequently lead to service interruption due to high migration time. Therefore, we advocate migration techniques that takes advantage of application states.

Au cours de ces dernières décennies, les réseaux de télécommunications mobiles ont évolué de la 1G à la 4G. La 4G permet la coexistence de différents réseaux d'accès. Ainsi, les utilisateurs ont la capacité de se connecter à un réseau hétérogène, constitué de plusieurs réseaux d'accès. Toutefois, la sélection du réseau approprié n'est pas une tâche facile pour les utilisateurs mobiles puisque les conditions de chaque réseau d'accès changent rapidement. Par ailleurs, en termes d'usage, le streaming vidéo devient le service principal de transfert de données sur les réseaux mobiles, ce qui amène les fournisseurs de contenu et les opérateurs de réseau à coopérer pour garantir la qualité de la diffusion. Dans ce contexte, la thèse propose la conception d'une approche novatrice pour la prise de décision optimale de sélection de réseau et une architecture améliorant les performances des services de streaming adaptatif dans un réseau hétérogène. En premier lieu, nous introduisons un modèle analytique décrivant la procédure de sélection de réseau en ne considérant déjà qu'une seule classe de trafic. Nous concevons ensuite une stratégie de sélection basée sur des fondements de la théorie du contrôle optimal linéaire. Des simulations sous MATLAB sont effectuées pour valider l'efficacité du mécanisme proposé. Sur ce même principe, nous étendons ce modèle avec un modèle analytique général décrivant les procédures de sélection de réseau dans des environnements de réseaux hétérogènes avec de multiples classes de trafic. Le modèle proposé est ensuite utilisé pour dériver un mécanisme adaptatif basé sur la théorie du contrôle, qui permet non seulement d'aider à piloter dynamiquement le trafic vers l'accès réseau le plus approprié mais aussi de bloquer dynamiquement le trafic résiduel lorsque le réseau est congestionné en ajustant les probabilités d'accès optimales. Nous discutons aussi les avantages d'une intégration transparente du mécanisme proposé avec l'ANDSF, solution fonctionnelle normalisée pour la sélection de réseau. Un prototype est également implémenté dans ns-3. En second lieu, nous nous concentrons sur l'amélioration des performances de DASH pour les utilisateurs mobiles dans un environnement de réseau d'accès 4G uniquement. Nous introduisons une nouvelle architecture basée sur l'utilisation de serveurs distribués en périphérie de réseau suivant le standard MEC. Le mécanisme d'adaptation proposé, fonctionnant en tant que service MEC, peut modifier les fichiers de manifeste en temps réel, en réponse à la congestion du réseau et à la demande dynamique de flux de streaming. Ces modifications conduisent ainsi les clients à sélectionner des représentations vidéo de débit / qualité plus appropriées. Nous avons développé une plateforme de test virtualisée pour l'expérimentation de notre proposition. Les résultats ainsi obtenus démontrent ses avantages en terme de QoE comparés aux approches d'adaptation traditionnelles, purement pilotées par les clients, car notre approche améliore non seulement le MOS mais aussi l'équité face à la congestion. Enfin, nous étendons l'architecture proposée basée sur MEC pour supporter le service de streaming adaptatif DASH dans un réseau hétérogène multi-accès afin de maximiser la QoE et l'équité des utilisateurs mobiles. Dans ce scénario, notre mécanisme doit aider les utilisateurs à sélectionner la qualité vidéo et le réseau et nous le formulons comme un problème d'optimisation. Ce problème d'optimisation peut être résolu par l'outil IBM CPLEX, mais cela prend du temps et ne peut être envisagé à grande échelle. Par conséquent, nous introduisons une heuristique pour aborder la solution optimale avec moins de complexité. Ensuite, nous mettons en œuvre une expérimentation sur notre plateforme de tests. Le résultat démontre que, par rapport à l'outil IBM CPLEX, notre algorithme permet d'obtenir des performances similaires sur la QoE globale et l'équité, avec un gain de temps significatif. / Telecommunication network has evolved from 1G to 4G in the past decades. One of the typical characteristics of the 4G network is the coexistence of heterogeneous radio access technologies, which offers end-users the capability to connect them and to switch between them with their mobile devices of the new generation. However, selecting the right network is not an easy task for mobile users since access network condition changes rapidly. Moreover, video streaming is becoming the major data service over the mobile network where content providers and network operators should cooperate to guarantee the quality of video delivery. In order to cope with this context, the thesis concerns the design of a novel approach for making an optimal network selection decision and architecture for improving the performance of adaptive streaming in the context of a heterogeneous network. Firstly, we introduce an analytical model (i.e. linear discrete-time system) to describe the network selection procedure considering one traffic class. Then, we consider the design of a selection strategy based on foundations from linear optimal control theory, with the objective to maximize network resource utilization while meeting the constraints of the supported services. Computer simulations with MATLAB are carried out to validate the efficiency of the proposed mechanism. Based on the same principal we extend this model with a general analytical model describing the network selection procedures in heterogeneous network environments with multiple traffic classes. The proposed model was, then, used to derive a scalable mechanism based on control theory, which allows not only to assist in steering dynamically the traffic to the most appropriate network access but also helps in blocking the residual traffic dynamically when the network is congested by adjusting dynamically the access probabilities. We discuss the advantages of a seamless integration with the ANDSF. A prototype is also implemented into ns-3. Simulation results sort out that the proposed scheme prevents the network congestion and demonstrates the effectiveness of the controller design, which can maximize the network resources allocation by converging the network workload to the targeted network occupancy. Thereafter, we focus on enhancing the performance of DASH in a mobile network environment for the users which has one access network. We introduce a novel architecture based on MEC. The proposed adaptation mechanism, running as an MEC service, can modify the manifest files in real time, responding to network congestion and dynamic demand, thus driving clients towards selecting more appropriate quality/bitrate video representations. We have developed a virtualized testbed to run the experiment with our proposed scheme. The simulation results demonstrate its QoE benefits compared to traditional, purely client-driven, bitrate adaptation approaches since our scheme notably improves both on the achieved MOS and on fairness in the face of congestion. Finally, we extend the proposed the MEC-based architecture to support the DASH service in a multi-access heterogeneous network in order to maximize the QoE and fairness of mobile users. In this scenario, our scheme should help users select both video quality and access network and we formulate it as an optimization problem. This optimization problem can be solved by IBM CPLEX tool. However, this tool is time-consuming and not scalable. Therefore, we introduce a heuristic algorithm to make a sub-optimal solution with less complexity. Then we implement a testbed to conduct the experiment and the result demonstrates that our proposed algorithm notably can achieve similar performance on overall achieved QoE and fairness with much more time-saving compared to the IBM CPLEX tool.

Applications today are generally deployed in public cloud environments such as Azure, AWS etc. Mobile edge computing (MEC) enables these applications to be relocated to edge nodes which are located in close proximity to the end user, thereby allowing the application to serve the user at lower latency. However, these edge nodes have limited capacity and hence a problem arises of when to relocate an application to an edge. This thesis project attempts to tackle the problem of detecting when an application’s quality of experience is degraded, and how to use this information in order to generate candidates for relocation to edge nodes. The assumption for this thesis project is there is no insight to the application itself, meaning the applications are treated as blackboxes. To detect quality of experience degradation we chose to capture network packets and inspect protocol-level information. We chose WebRTC and HTTP as communication protocols because they were the most common protocols used by the target environment. We developed two application prototypes. The first prototype was a rudimentary server based on HTTP and the second prototype was a video streaming application based on WebRTC. The prototypes were used to study the possibility of breaking down latency components and obtaining quality of service parameters. We then developed a recommendation engine to use this information in order to generate relocation candidates. The recommendation engine was evaluated by placing the WebRTC prototype under quality of experience affecting scenarios and measuring the time taken to generate a relocation candidate of the application. The result of this project show it is possible in some cases to break down latency components for HTTP based applications. However, for WebRTC based applications our approach was not sufficient enough to break down latency components. Instead, we had to rely on quality of service parameters to generate relocation candidates. Based on the outcomes of the project, we conclude detecting quality of experience degradation for blackbox applications have three generalizations. Firstly, the underlying transport and communication protocol has an impact on available approaches and obtainable information. Secondly, the implementation of the communication protocol also has an impact on obtainable information. Lastly, the underlying infrastructure can matter for the approaches used in this project. / Applikationer idag produktionssätts allmänhet i offentliga molntjänster som Azure, AWS etc. Mobile edge computing (MEC) gör att dessa applikationer kan flyttas till gränsnoder som är placerade i närheten av slutanvändaren, vilket gör att applikationen kan erbjuda användaren lägre latens. Dessa gränsnoder har emellertid begränsad kapacitet och därför uppstår ett problem om när en applikation ska flyttas till en gränsnod. Detta examensarbete försöker ta itu med problemet med att upptäcka när en applikations upplevelsekvalitet försämras, och hur man använder denna information för att generera kandidater för omlokalisering till gränsnoder. Antagandet för detta examensarbete är att det inte finns någon insikt i själva applikationen, vilket innebär att applikationer behandlas som svarta lådor. För att upptäcka försämring av upplevelsekvalitet valde vi att fånga nätverkspaket och inspektera information på protokollnivå. Vi valde WebRTC och HTTP som kommunikationsprotokoll eftersom de var de vanligaste protokollen som användes i målmiljön. Vi utvecklade två applikationsprototyper. Den första prototypen var en rudimentär server baserad på HTTPoch den andra prototypen var en videoströmningsapplikation baserad på WebRTC. Prototyperna användes för att studera möjligheten att bryta ned latenskomponenter och erhålla tjänstekvalitetsparametrar. Vi utvecklade sedan en rekommendationsmotor för att använda denna information till att generera omplaceringskandidater. Rekommendationsmotorn utvärderades genom att placera WebRTC-prototypen under scenarion som påverkar upplevelsekvaliten, och sedan mäta tiden det tog att generera en omlokaliseringskandidat av applikationen. Resultatet av detta projekt visar att det i vissa fall är möjligt att bryta ned latenskomponenter för HTTP-baserade applikationer. Dock för WebRTCbaserade applikationer var vårt tillvägagångssätt inte tillräckligt för att bryta ned latenskomponenter. Istället var vi tvungna att förlita oss på kvalitetsparametrar för tjänsten för att generera omlokaliseringskandidater. Baserat på resultaten av projektet drar vi slutsatsen att upptäcka kvalitetsförsämring av erfarenheter för blackbox-applikationer har tre generaliseringar. För det första har det underliggande transport- och kommunikationsprotokollet en inverkan på tillgängliga tillvägagångssätt och tillgänglig information. För det andra har implementeringen av kommunikationsprotokollet också en inverkan på tillgänglig information. Slutligen kan den underliggande infrastrukturen ha betydelse för de tillvägagångssätt som används i detta projekt.

Timely data collection and execution in heterogeneous Internet of Things (IoT) networks in which different protocols and spectrum bands coexist such as WiFi, RFID, Zigbee, and LoRa, requires further investigation. This thesis studies the problem of age-of-information minimization in heterogeneous IoT networks consisting of heterogeneous IoT devices, an intermediate layer of multi-protocol mobile gateways (M-MGs) that collects and relays data from IoT objects and performs computing tasks, and heterogeneous access points (APs). A federated matching framework is presented to model the collaboration between different service providers (SPs) to deploy and share M-MGs and minimize the average weighted sum of the age-of-information and energy consumption. Further, we develop a two-level multi-protocol multi-agent actor-critic (MP-MAAC) to solve the optimization problem, where M-MGs and SPs can learn collaborative strategies through their own observations. The M-MGs' strategies include selecting IoT objects for data collection, execution, relaying, and/or offloading to SPs’ access points while SPs decide on spectrum allocation. Finally, to improve the convergence of the learning process we incorporate federated learning into the multi-agent collaborative framework. The numerical results show that our Fed-Match algorithm reduces the AoI by factor four, collects twice more packets than existing approaches, reduces the penalty by factor five when enabling relaying, and establishes design principles for the stability of the training process.

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.

Power conservation has become a severe issue in devices since battery capability advancement is not keeping pace with the swift development of other technologies such as processing technologies. This issue becomes critical when both the number of resource-intensive applications and the number of connected devices are rapidly growing. The former results in an increase in power consumption per device, and the latter causes an increase in the total power consumption of devices. Mobile edge computing (MEC) and low power wide area networks (LPWANs) are raised as two important research areas in wireless networks, which can assist devices to save power. On the one hand, devices are being considered as a platform to run resource-intensive applications while they have limited resources such as battery and processing capabilities. On the other hand, LPWANs raised as an important enabler for massive IoT (Internet of Things) to provide long-range and reliable connectivity for low power devices. The scope of this thesis spans over these two main research areas: (1) MEC, where devices can use radio resources to offload their processing tasks to the cloud to save energy. (2) LPWAN, with grant-free radio access where devices from different technology transmit their packets without any handshaking process. In particular, we consider a MEC network, where the processing resources are distributed in the proximity of the users. Hence, devices can save energy by transmitting the data to be processed to the edge cloud provided that the delay requirement is met and transmission power consumption is less than the local processing power consumption. This thesis addresses the question of whether to offload or not to minimize the uplink power consumption in a multi-cell multi-user MEC network. We consider the maximum acceptable delay as the QoS metric to be satisfied in our system. We formulate the problem as a mixed-integer nonlinear problem, which is converted into a convex form using D.C. approximation. To solve the converted optimization problem, we have proposed centralized and distributed algorithms for joint power allocation and channel assignment together with decision-making on job offloading. Our results show that there exists a region in which offloading can save power at mobile devices and increases the battery lifetime. Another focus of this thesis is on LPWANs, which are becoming more and more popular, due to the limited battery capacity and the ever-increasing need for durable battery lifetime for IoT networks. Most studies evaluate the system performance assuming single radio access technology deployment. In this thesis, we study the impact of coexisting competing radio access technologies on the system performance. We consider K technologies, defined by time and frequency activity factors, bandwidth, and power, which share a set of radio resources. Leveraging tools from stochastic geometry, we derive closed-form expressions for the successful transmission probability, expected battery lifetime, experienced delay, and expected number of retransmissions. Our analytical model, which is validated by simulation results, provides a tool to evaluate the coexistence scenarios and analyze how the introduction of a new coexisting technology may degrade the system performance in terms of success probability, delay, and battery lifetime. We further investigate the interplay between traffic load, the density of access points, and reliability/delay of communications, and examine the bounds beyond which, mean delay becomes infinite. / Antalet anslutna enheter till nätverk ökar. Det finns olika trender som mobil edgecomputing (MEC) och low power wide area-nätverk (LPWAN) som har blivit intressantai trådlösa nätverk. Därför står trådlösa nätverk inför nya utmaningar som ökadenergiförbrukning. I den här avhandlingen beaktar vi dessa två mobila nätverk. I MECavlastar mobila enheter sina bearbetningsuppgifter till centraliserad beräkningsresurser (”molnet”). I avhandlingensvarar vi på följande fråga: När det är energieffektivt att avlasta dessa beräkningsuppgifter till molnet?Vi föreslår två algoritmer för att bestämma den rätta tiden för överflyttning av beräkningsuppgifter till molnet.I LPWANs, antar vi att det finns ett mycket stort antal enheter av olika art som kommunicerar mednätverket. De använder s.k. ”Grant-free”-åtkomst för att ansluta till nätverket, där basstationerna inte ger explicita sändningstillstånd till enheterna. Denanalytiska modell som föreslås i avhandlingen utgör ett verktyg för att utvärdera sådana samexistensscenarier.Med verktygen kan vi analysera olika systems prestanda när det gäller framgångssannolikhet, fördröjning och batteriershållbarhetstid. / <p>QC 20200228</p> / SOOGreen

Today's Internet is seeing a massive shift from traditional client-server applications towards real-time, context-sensitive, and highly immersive applications. The fusion between Cyber-physical systems, The Internet of Things (IoT), Augmented/Virtual-Reality (AR/VR), and the Tactile Internet with the Human-in-the-Loop (TaHIL) means that Ultra-Reliable Low Latency Communication (URLLC) is a key functional requirement. Mobile Edge Computing (MEC) has emerged as a network architectural paradigm to address such ever-increasing resource demands. MEC leverages networking and computational resource pools that are closer to the end-users at the far edge of the network, eliminating the need to send and process large volumes of data over multiple distant hops at central cloud computing data centers. Multiple 'cloudlets' are formed at the edge, and the access to resources is shared and federated across them over multiple network domains that are distributed over various geographical locations. However, this federated access comes at the cost of a fuzzy and dynamically-changing network security perimeter because there are multiple sources of mobility. Not only are the end users mobile, but the applications themselves virtually migrate over multiple network domains and cloudlets to serve the end users, bypassing statically placed network security middleboxes and firewalls. This work aims to address this problem by proposing adaptive network security measures that can be dynamically changed at runtime, and are decoupled from the ever-changing network topology. In particular, we: 1) use the state of the art in programmable networking to protect MEC networks from internal adversaries that can adapt and laterally move, 2) Automatically infer application security contexts, and device vulnerabilities, then evolve the network access control policies to segment the network in such a way that minimizes the attack surface with minimal impact on its utility, 3) propose new metrics to assess the susceptibility of edge nodes to a new class of stealthy attacks that bypasses traditional statically placed Intrusion Detection Systems (IDS), and a probabilistic approach to pro-actively protect them.:Acknowledgments Acronyms & Abbreviations 1 Introduction 1.1 Prelude 1.2 Motivation and Challenges 1.3 Aim and objectives 1.4 Contributions 1.5 Thesis structure 2 Background 2.1 A primer on computer networks 2.2 Network security 2.3 Network softwarization 2.4 Cloudification of networks 2.5 Securing cloud networks 2.6 Towards Securing Edge Cloud Networks 2.7 Summary I Adaptive security in consumer edge cloud networks 3 Automatic microsegmentation of smarthome IoT networks 3.1 Introduction 3.2 Related work 3.3 Smart home microsegmentation 3.4 Software-Defined Secure Isolation 3.5 Evaluation 3.6 Summary 4 Smart home microsegmentation with user privacy in mind 4.1 Introduction 4.2 Related Work 4.3 Goals and Assumptions 4.4 Quantifying the security and privacy of SHIoT devices 4.5 Automatic microsegmentation 4.6 Manual microsegmentation 4.7 Experimental setup 4.8 Evaluation 4.9 Summary II Adaptive security in enterprise edge cloud networks 5 Adaptive real-time network deception and isolation 5.1 Introduction 5.2 Related work 5.3 Sandnet’s concept 5.4 Live Cloning and Network Deception 5.5 Evaluation 5.6 Summary 6 Localization of internal stealthy DDoS attacks on Microservices 6.1 Introduction 6.2 Related work 6.3 Assumptions & Threat model 6.4 Mitigating SILVDDoS 6.5 Evaluation 6.6 Summary III Summary of Results 7 Conclusion 7.1 Main outcomes 7.2 Future outlook Listings Bibliography List of Algorithms List of Figures List of Tables Appendix

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.