Relaying in the Internet of Things (IoT): A Survey

The deployment of relays between Internet of Things (IoT) end devices and gateways can improve link quality. In cellular-based IoT, relays have the potential to reduce base station overload. The energy expended in single-hop long-range communication can be reduced if relays listen to transmissions of end devices and forward these observations to gateways. However, incorporating relays into IoT networks faces some challenges. IoT end devices are designed primarily for uplink communication of small-sized observations toward the network; hence, opportunistically using end devices as relays needs a redesign of both the medium access control (MAC) layer protocol of such end devices and possible addition of new communication interfaces. Additionally, the wake-up time of IoT end devices needs to be synchronized with that of the relays. For cellular-based IoT, the possibility of using infrastructure relays exists, and noncellular IoT networks can leverage the presence of mobile devices for relaying, for example, in remote healthcare. However, the latter presents problems of incentivizing relay participation and managing the mobility of relays. Furthermore, although relays can increase the lifetime of IoT networks, deploying relays implies the need for additional batteries to power them. This can erode the energy efficiency gain that relays offer. Therefore, designing relay-assisted IoT networks that provide acceptable trade-offs is key, and this goes beyond adding an extra transmit RF chain to a relay-enabled IoT end device. There has been increasing research interest in IoT relaying, as demonstrated in the available literature. Works that consider these issues are surveyed in this paper to provide insight into the state of the art, provide design insights for network designers and motivate future research directions

Enhanced mobility management mechanisms for 5G networks

Many mechanisms that served the legacy networks till now, are being identified as being grossly sub-optimal for 5G networks. The reason being, the increased complexity of the 5G networks compared previous legacy systems. One such class of mechanisms, important for any wireless standard, is the Mobility Management (MM) mechanisms. MM mechanismsensure the seamless connectivity and continuity of service for a user when it moves away from the geographic location where it initially got attached to the network. In this thesis, we firstly present a detailed state of the art on MM mechanisms. Based on the 5G requirements as well as the initial discussions on Beyond 5G networks, we provision a gap analysis for the current technologies/solutions to satisfy the presented requirements. We also define the persistent challenges that exist concerning MM mechanisms for 5G and beyond networks. Based on these challenges, we define the potential solutions and a novel framework for the 5G and beyond MM mechanisms. This framework specifies a set of MM mechanisms at the access, core and the extreme edge network (users/devices) level, that will help to satisfy the requirements for the 5G and beyond MM mechanisms. Following this, we present an on demand MM service concept. Such an on-demand feature provisions the necessary reliability, scalability and flexibility to the MM mechanisms. It's objective is to ensure that appropriate resources and mobility contexts are defined for users who will have heterogeneous mobility profiles, versatile QoS requirements in a multi-RAT network. Next, in this thesis we tackle the problem of core network signaling that occurs during MM in 5G/4G networks. A novel handover signaling mechanism has been developed, which eliminates unnecessary handshakes during the handover preparation phase, while allowing the transition to future softwarized network architectures. We also provide a handover failure aware handover preparation phase signaling process. We then utilize operator data and a realistic network deployment to perform a comparative analysis of the proposed strategy and the 3GPP handover signaling strategy on a network wide deployment scenario. We show the benefits of our strategy in terms of latency of handover process, and the transmission and processing cost incurred. Lastly, a novel user association and resource allocation methodology, namely AURA-5G, has been proposed. AURA-5G addresses scenarios wherein applications with heterogeneous requirements, i.e., enhanced Mobile Broadband (eMBB) and massive Machine Type Communications (mMTC), are present simultaneously. Consequently, a joint optimization process for performing the user association and resource allocation while being cognizant of heterogeneous application requirements, has been performed. We capture the peculiarities of this important mobility management process through the various constraints, such as backhaul requirements, dual connectivity options, available access resources, minimum rate requirements, etc., that we have imposed on a Mixed Integer Linear Program (MILP). The objective function of this established MILP problem is to maximize the total network throughput of the eMBB users, while satisfying the minimum requirements of the mMTC and eMBB users defined in a given scenario. Through numerical evaluations we show that our approach outperforms the baseline user association scenario significantly. Moreover, we have presented a system fairness analysis, as well as a novel fidelity and computational complexity analysis for the same, which express the utility of our methodology given the myriad network scenarios.Muchos mecanismos que sirvieron en las redes actuales, se están identificando como extremadamente subóptimos para las redes 5G. Esto es debido a la mayor complejidad de las redes 5G. Un tipo de mecanismo importante para cualquier estándar inalámbrico, consiste en el mecanismo de gestión de la movilidad (MM). Los mecanismos MM aseguran la conectividad sin interrupciones y la continuidad del servicio para un usuario cuando éste se aleja de la ubicación geográfica donde inicialmente se conectó a la red. En esta tesis, presentamos, en primer lugar, un estado del arte detallado de los mecanismos MM. Bas ándonos en los requisitos de 5G, así como en las discusiones iniciales sobre las redes Beyond 5G, proporcionamos un análisis de las tecnologías/soluciones actuales para satisfacer los requisitos presentados. También definimos los desafíos persistentes que existen con respecto a los mecanismos MM para redes 5G y Beyond 5G. En base a estos desafíos, definimos las posibles soluciones y un marco novedoso para los mecanismos 5G y Beyond 5G de MM. Este marco especifica un conjunto de mecanismos MM a nivel de red acceso, red del núcleo y extremo de la red (usuarios/dispositivos), que ayudarán a satisfacer los requisitos para los mecanismos MM 5G y posteriores. A continuación, presentamos el concepto de servicio bajo demanda MM. Tal característica proporciona la confiabilidad, escalabilidad y flexibilidad necesarias para los mecanismos MM. Su objetivo es garantizar que se definan los recursos y contextos de movilidad adecuados para los usuarios que tendrán perfiles de movilidad heterogéneos, y requisitos de QoS versátiles en una red multi-RAT. Más adelante, abordamos el problema de la señalización de la red troncal que ocurre durante la gestión de la movilidad en redes 5G/4G. Se ha desarrollado un nuevo mecanismo de señalización de handover, que elimina los intercambios de mensajes innecesarios durante la fase de preparación del handover, al tiempo que permite la transición a futuras arquitecturas de red softwarizada. Utilizamos los datos de operadores y consideramos un despliegue de red realista para realizar un análisis comparativo de la estrategia propuesta y la estrategia de señalización de 3GPP. Mostramos los beneficios de nuestra estrategia en términos de latencia del proceso de handover y los costes de transmisión y procesado. Por último, se ha propuesto una nueva asociación de usuarios y una metodología de asignación de recursos, i.e, AURA-5G. AURA-5G aborda escenarios en los que las aplicaciones con requisitos heterogéneos, i.e., enhanced Mobile Broadband (eMBB) y massive Machine Type Communications (mMTC), están presentes simultáneamente. En consecuencia, se ha llevado a cabo un proceso de optimización conjunta para realizar la asociación de usuarios y la asignación de recursos mientras se tienen en cuenta los requisitos de aplicaciónes heterogéneas. Capturamos las peculiaridades de este importante proceso de gestión de la movilidad a través de las diversas restricciones impuestas, como son los requisitos de backhaul, las opciones de conectividad dual, los recursos de la red de acceso disponibles, los requisitos de velocidad mínima, etc., que hemos introducido en un Mixed Integer Linear Program (MILP). La función objetivo de este problema MILP es maximizar el rendimiento total de la red de los usuarios de eMBB, y a la vez satisfacer los requisitos mínimos de los usuarios de mMTC y eMBB definidos en un escenario dado. A través de evaluaciones numéricas, mostramos que nuestro enfoque supera significativamente el escenario de asociación de usuarios de referencia. Además, hemos presentado un análisis de la justicia del sistema, así como un novedoso análisis de fidelidad y complejidad computacional para el mismo, que expresa la utilidad de nuestra metodología

A Survey on Mobile Edge Computing for Video Streaming : Opportunities and Challenges

5G communication brings substantial improvements in the quality of service provided to various applications by achieving higher throughput and lower latency. However, interactive multimedia applications (e.g., ultra high definition video conferencing, 3D and multiview video streaming, crowd-sourced video streaming, cloud gaming, virtual and augmented reality) are becoming more ambitious with high volume and low latency video streams putting strict demands on the already congested networks. Mobile Edge Computing (MEC) is an emerging paradigm that extends cloud computing capabilities to the edge of the network i.e., at the base station level. To meet the latency requirements and avoid the end-to-end communication with remote cloud data centers, MEC allows to store and process video content (e.g., caching, transcoding, pre-processing) at the base stations. Both video on demand and live video streaming can utilize MEC to improve existing services and develop novel use cases, such as video analytics, and targeted advertisements. MEC is expected to reshape the future of video streaming by providing ultra-reliable and low latency streaming (e.g., in augmented reality, virtual reality, and autonomous vehicles), pervasive computing (e.g., in real-time video analytics), and blockchain-enabled architecture for secure live streaming. This paper presents a comprehensive survey of recent developments in MEC-enabled video streaming bringing unprecedented improvement to enable novel use cases. A detailed review of the state-of-the-art is presented covering novel caching schemes, optimal computation offloading, cooperative caching and offloading and the use of artificial intelligence (i.e., machine learning, deep learning, and reinforcement learning) in MEC-assisted video streaming services.publishedVersionPeer reviewe

Intelligent and Efficient Ultra-Dense Heterogeneous Networks for 5G and Beyond

Ultra-dense heterogeneous network (HetNet), in which densified small cells overlaying the conventional macro-cells, is a promising technique for the fifth-generation (5G) mobile network. The dense and multi-tier network architecture is able to support the extensive data traffic and diverse quality of service (QoS) but meanwhile arises several challenges especially on the interference coordination and resource management. In this thesis, three novel network schemes are proposed to achieve intelligent and efficient operation based on the deep learning-enabled network awareness. Both optimization and deep learning methods are developed to achieve intelligent and efficient resource allocation in these proposed network schemes. To improve the cost and energy efficiency of ultra-dense HetNets, a hotspot prediction based virtual small cell (VSC) network is proposed. A VSC is formed only when the traffic volume and user density are extremely high. We leverage the feature extraction capabilities of deep learning techniques and exploit a long-short term memory (LSTM) neural network to predict potential hotspots and form VSC. Large-scale antenna array enabled hybrid beamforming is also adaptively adjusted for highly directional transmission to cover these VSCs. Within each VSC, one user equipment (UE) is selected as a cell head (CH), which collects the intra-cell traffic using the unlicensed band and relays the aggregated traffic to the macro-cell base station (MBS) in the licensed band. The inter-cell interference can thus be reduced, and the spectrum efficiency can be improved. Numerical results show that proposed VSCs can reduce $55\%$ power consumption in comparison with traditional small cells. In addition to the smart VSCs deployment, a novel multi-dimensional intelligent multiple access (MD-IMA) scheme is also proposed to achieve stringent and diverse QoS of emerging 5G applications with disparate resource constraints. Multiple access (MA) schemes in multi-dimensional resources are adaptively scheduled to accommodate dynamic QoS requirements and network states. The MD-IMA learns the integrated-quality-of-system-experience (I-QoSE) by monitoring and predicting QoS through the LSTM neural network. The resource allocation in the MD-IMA scheme is formulated as an optimization problem to maximize the I-QoSE as well as minimize the non-orthogonality (NO) in view of implementation constraints. In order to solve this problem, both model-based optimization algorithms and model-free deep reinforcement learning (DRL) approaches are utilized. Simulation results demonstrate that the achievable I-QoSE gain of MD-IMA over traditional MA is $15\%$ - $18\%$. In the final part of the thesis, a Software-Defined Networking (SDN) enabled 5G-vehicle ad hoc networks (VANET) is designed to support the growing vehicle-generated data traffic. In this integrated architecture, to reduce the signaling overhead, vehicles are clustered under the coordination of SDN and one vehicle in each cluster is selected as a gateway to aggregate intra-cluster traffic. To ensure the capacity of the trunk-link between the gateway and macro base station, a Non-orthogonal Multiplexed Modulation (NOMM) scheme is proposed to split aggregated data stream into multi-layers and use sparse spreading code to partially superpose the modulated symbols on several resource blocks. The simulation results show that the energy efficiency performance of proposed NOMM is around 1.5-2 times than that of the typical orthogonal transmission scheme