Energy Saving in QoS Fog-supported Data Centers

One of the most important challenges that cloud providers face in the explosive growth of data is to reduce the energy consumption of their designed, modern data centers. The majority of current research focuses on energy-efficient resources management in the infrastructure as a service (IaaS) model through "resources virtualization" - virtual machines and physical machines consolidation. However, actual virtualized data centers are not supporting communication–computing intensive real-time applications, big data stream computing (info-mobility applications, real-time video co-decoding). Indeed, imposing hard-limits on the overall per-job computing-plus-communication delays forces the overall networked computing infrastructure to quickly adopt its resource utilization to the (possibly, unpredictable and abrupt) time fluctuations of the offered workload. Recently, Fog Computing centers are as promising commodities in Internet virtual computing platform that raising the energy consumption and making the critical issues on such platform. Therefore, it is expected to present some green solutions (i.e., support energy provisioning) that cover fog-supported delay-sensitive web applications. Moreover, the usage of traffic engineering-based methods dynamically keep up the number of active servers to match the current workload. Therefore, it is desirable to develop a flexible, reliable technological paradigm and resource allocation algorithm to pay attention the consumed energy. Furthermore, these algorithms could automatically adapt themselves to time-varying workloads, joint reconfiguration, and orchestration of the virtualized computing-plus-communication resources available at the computing nodes. Besides, these methods facilitate things devices to operate under real-time constraints on the allowed computing-plus-communication delay and service latency. The purpose of this thesis is: i) to propose a novel technological paradigm, the Fog of Everything (FoE) paradigm, where we detail the main building blocks and services of the corresponding technological platform and protocol stack; ii) propose a dynamic and adaptive energy-aware algorithm that models and manages virtualized networked data centers Fog Nodes (FNs), to minimize the resulting networking-plus-computing average energy consumption; and, iii) propose a novel Software-as-a-Service (SaaS) Fog Computing platform to integrate the user applications over the FoE. The emerging utilization of SaaS Fog Computing centers as an Internet virtual computing commodity is to support delay-sensitive applications. The main blocks of the virtualized Fog node, operating at the Middleware layer of the underlying protocol stack and comprises of: i) admission control of the offered input traffic; ii) balanced control and dispatching of the admitted workload; iii) dynamic reconfiguration and consolidation of the Dynamic Voltage and Frequency Scaling (DVFS)-enabled Virtual Machines (VMs) instantiated onto the parallel computing platform; and, iv) rate control of the traffic injected into the TCP/IP connection. The salient features of this algorithm are that: i) it is adaptive and admits distributed scalable implementation; ii) it has the capacity to provide hard QoS guarantees, in terms of minimum/maximum instantaneous rate of the traffic delivered to the client, instantaneous goodput and total processing delay; and, iii) it explicitly accounts for the dynamic interaction between computing and networking resources in order to maximize the resulting energy efficiency. Actual performance of the proposed scheduler in the presence of: i) client mobility; ii) wireless fading; iii) reconfiguration and two-thresholds consolidation costs of the underlying networked computing platform; and, iv) abrupt changes of the transport quality of the available TCP/IP mobile connection, is numerically tested and compared to the corresponding ones of some state-of-the-art static schedulers, under both synthetically generated and measured real-world workload traces

Distributed Adaptation Techniques for Connected Vehicles

In this PhD dissertation, we propose distributed adaptation mechanisms for connected vehicles to deal with the connectivity challenges. To understand the system behavior of the solutions for connected vehicles, we first need to characterize the operational environment. Therefore, we devised a large scale fading model for various link types, including point-to-point vehicular communications and multi-hop connected vehicles. We explored two small scale fading models to define the characteristics of multi-hop connected vehicles. Taking our research into multi-hop connected vehicles one step further, we propose selective information relaying to avoid message congestion due to redundant messages received by the relay vehicle. Results show that the proposed mechanism reduces messaging load by up to 75% without sacrificing environmental awareness. Once we define the channel characteristics, we propose a distributed congestion control algorithm to solve the messaging overhead on the channels as the next research interest of this dissertation. We propose a combined transmit power and message rate adaptation for connected vehicles. The proposed algorithm increases the environmental awareness and achieves the application requirements by considering highly dynamic network characteristics. Both power and rate adaptation mechanisms are performed jointly to avoid one result affecting the other negatively. Results prove that the proposed algorithm can increase awareness by 20% while keeping the channel load and interference at almost the same level as well as improve the average message rate by 18%. As the last step of this dissertation, distributed cooperative dynamic spectrum access technique is proposed to solve the channel overhead and the limited resources issues. The adaptive energy detection threshold, which is used to decide whether the channel is busy, is optimized in this work by using a computationally efficient numerical approach. Each vehicle evaluates the available channels by voting on the information received from one-hop neighbors. An interdisciplinary approach referred to as entropy-based weighting is used for defining the neighbor credibility. Once the vehicle accesses the channel, we propose a decision mechanism for channel switching that is inspired by the optimal flower selection process employed by bumblebees foraging. Experimental results show that by using the proposed distributed cooperative spectrum sensing mechanism, spectrum detection error converges to zero

Advanced Modeling and Research in Hybrid Microgrid Control and Optimization

This book presents the latest solutions in fuel cell (FC) and renewable energy implementation in mobile and stationary applications. The implementation of advanced energy management and optimization strategies are detailed for fuel cell and renewable microgrids, and for the multi-FC stack architecture of FC/electric vehicles to enhance the reliability of these systems and to reduce the costs related to energy production and maintenance. Cyber-security methods based on blockchain technology to increase the resilience of FC renewable hybrid microgrids are also presented. Therefore, this book is for all readers interested in these challenging directions of research

The 1990 Johnson Space Center bibliography of scientific and technical papers

Abstracts are presented of scientific and technical papers written and/or presented by L. B. Johnson Space Center (JSC) authors, including civil servants, contractors, and grantees, during the calendar year of 1990. Citations include conference and symposium presentations, papers published in proceedings or other collective works, seminars, and workshop results, NASA formal report series (including contractually required final reports), and articles published in professional journals

Cellular, Wide-Area, and Non-Terrestrial IoT: A Survey on 5G Advances and the Road Towards 6G

The next wave of wireless technologies is proliferating in connecting things among themselves as well as to humans. In the era of the Internet of things (IoT), billions of sensors, machines, vehicles, drones, and robots will be connected, making the world around us smarter. The IoT will encompass devices that must wirelessly communicate a diverse set of data gathered from the environment for myriad new applications. The ultimate goal is to extract insights from this data and develop solutions that improve quality of life and generate new revenue. Providing large-scale, long-lasting, reliable, and near real-time connectivity is the major challenge in enabling a smart connected world. This paper provides a comprehensive survey on existing and emerging communication solutions for serving IoT applications in the context of cellular, wide-area, as well as non-terrestrial networks. Specifically, wireless technology enhancements for providing IoT access in fifth-generation (5G) and beyond cellular networks, and communication networks over the unlicensed spectrum are presented. Aligned with the main key performance indicators of 5G and beyond 5G networks, we investigate solutions and standards that enable energy efficiency, reliability, low latency, and scalability (connection density) of current and future IoT networks. The solutions include grant-free access and channel coding for short-packet communications, non-orthogonal multiple access, and on-device intelligence. Further, a vision of new paradigm shifts in communication networks in the 2030s is provided, and the integration of the associated new technologies like artificial intelligence, non-terrestrial networks, and new spectra is elaborated. Finally, future research directions toward beyond 5G IoT networks are pointed out.Comment: Submitted for review to IEEE CS&

New applications of data science for intelligent transportation systems

Streets and motorways are the basic blocks in the core of our transportation networks. In recent years, increases in available sensory and computing power have allowed us to start massive gatherings of data related to their use and performance, and to obtain insightful information via data science. This, in turn, has increased our ability to create systems that estimate the state of the transportation networks and provide us with control capabilities over it, giving rise to the concept of Intelligent Transportation Systems. These systems aim to provide deeper levels of observability to our transportation networks so that their capacity can be increased without the need of further heavy investment to develop traffic infrastructure, especially in terms of laying new roads and streets. In this thesis we aim to contribute to this process in both urban and interurban settings. Here we propose two different algorithms to estimate and forecast expected travel time in motorways over the long term, ranging from hours to a week. The first of them is centred around the identification of the different traffic regimes and leveraging their specific characteristics to improve estimation and forecasting. The second of them looks further into the differentiation between recurrent and non recurrent congestion from the point of view of statistical analysis in the frequency space, using the natural frequencies of the traffic system to tell them apart and exert prediction. We also delve into how Intelligent Transportation Systems can affect our cities, looking at how reinforcement learning can create independent agents capable of controlling traffic lights at intersections. We do this by first looking at the most effective agent architectures in different junctions of increasing complexity. Then we dive into the difference in performance for agents in charge of vehicular intersections, provided by an array of reward functions that use different measures obtained from the traffic flow. Finally, we expand these systems to also take pedestrians into account, investigating the rewards that produce the lowest waiting times when serving different modes of transportation with opposing needs

Quality-Driven Cross-Layer Protocols for Video Streaming over Vehicular Ad-Hoc Networks

The emerging vehicular ad-hoc networks (VANETs) offer a variety of applications and new potential markets related to safety, convenience and entertainment, however, they suffer from a number of challenges not shared so deeply by other types of existing networks, particularly, in terms of mobility of nodes, and end-to-end quality of service (QoS) provision. Although several existing works in the literature have attempted to provide efficient protocols at different layers targeted mostly for safety applications, there remain many barriers to be overcome in order to constrain the widespread use of such networks for non-safety applications, specifically, for video streaming: 1) impact of high speed mobility of nodes on end-to-end QoS provision; 2) cross-layer protocol design while keeping low computational complexity; 3) considering customer-oriented QoS metrics in the design of protocols; and 4) maintaining seamless single-hop and multi-hop connection between the destination vehicle and the road side unit (RSU) while network is moving. This thesis addresses each of the above limitations in design of cross-layer protocols for video streaming application. 1) An adaptive MAC retransmission limit selection scheme is proposed to improve the performance of IEEE 802.11p standard MAC protocol for video streaming applications over VANETs. A multi-objective optimization framework, which jointly minimizes the probability of playback freezes and start-up delay of the streamed video at the destination vehicle by tuning the MAC retransmission limit with respect to channel statistics as well as packet transmission rate, is applied at road side unit (RSU). Two-hop transmission is applied in zones in which the destination vehicle is not within the transmission range of any RSU. In the multi-hop scenario, we discuss the computation of access probability used in the MAC adaptation scheme and propose a cross-layer path selection scheme; 2) We take advantage of similarity between multi-hop urban VANETs in dense traffic conditions and mesh connected networks. First, we investigate an application-centric routing scheme for video streaming over mesh connected overlays. Next, we introduce the challenges of urban VANETs compared to mesh networks and extend the proposed scheme in mesh network into a protocol for urban VANETs. A classification-based method is proposed to select an optimal path for video streaming over multi-hop mesh networks. The novelty is to translate the path selection over multi-hop networks to a standard classification problem. The classification is based on minimizing average video packet distortion at the receiving nodes. The classifiers are trained offline using a vast collection of video sequences and wireless channel conditions in order to yield optimal performance during real time path selection. Our method substantially reduces the complexity of conventional exhaustive optimization methods and results in high quality (low distortion). Next, we propose an application-centric routing scheme for real-time video transmission over urban multi-hop vehicular ad-hoc network (VANET) scenarios. Queuing based mobility model, spatial traffic distribution and prob- ability of connectivity for sparse and dense VANET scenarios are taken into consideration in designing the routing protocol. Numerical results demonstrate the gain achieved by the proposed routing scheme versus geographic greedy forwarding in terms of video frame distortion and streaming start-up delay in several urban communication scenarios for various vehicle entrance rate and traffic densities; and 3) finally, the proposed quality-driven routing scheme for delivering video streams is combined with a novel IP management scheme. The routing scheme aims to optimize the visual quality of the transmitted video frames by minimizing the distortion, the start-up delay, and the frequency of the streaming freezes. As the destination vehicle is in motion, it is unrealistic to assume that the vehicle will remain connected to the same access router (AR) for the whole trip. Mobile IP management schemes can benefit from the proposed multi-hop routing protocol in order to adapt proxy mobile IPv6 (PMIPv6) for multi-hop VANET for video streaming applications. The proposed cross-layer protocols can significantly improve the video streaming quality in terms of the number of streaming freezes and start-up delay over VANETs while achieving low computational complexity by using pattern classification methods for optimization

Cognitive virtual ad hoc mobile cloud-based networking architecture

This thesis was submitted for the degree of Doctor of Philosophy and awarded by Brunel University LondonThis thesis proposed cognitive techniques and intelligent algorithms that offered adaptive and advanced facilities to cloud-based networking by using Virtual Ad Hoc Mobile Cloud Computing Networks architecture (VAMCCNs). This is presented as a working case to address their global network challenges and to add cognitive support to the network design and implementation for better meeting traffic management and application requirements in mission objectives. The thesis concentrates on three main contributions. Firstly, an adaptive model, namely: a Heterogeneous Mobile Cloud Computing Network (HMCCN), was proposed to integrate different cloud networks architectures into one workflow. The cognitive data offloading task and the routing decision methods were applied using two different approaches: Fuzzy Analytic Hierarchy system (FAH) as a first approach and cognitive Software Defined Network (SDN) model as a second centralised approach. Experimental results show improvement in network reliability and throughputs, minimised in both nodes’ energy consumption and network latency with efficient intelligent data load balance and network resources allocation with best cloud model selection. Secondly, based on a virtual Ad Hoc cloud network with a realistic Random Waypoint Motion (RWM) model, an innovative cognitive routing algorithm was presented to improve efficient and reliable route selection among multiple possible routes. Routing protocols based on conventional, Fuzzy logic used important parameters with two data collections and decisions techniques and a new adaptive Intelligent Hybrid Fuzzy-Neural routing protocol (IHFN) that included prior knowledge to the network of the underlying motion and energy parameters were all proposed and compared. Results with the new hybrid algorithm shown a significant improvement to solve the network end-to-end performance degradation problem. The new hybrid protocol improved network throughput with an average of 20% higher than traditional Ad Hoc On-Demand Distance Vector (AODV) Routing protocol, improved the usage of network resources and reduced the maintenance process in adynamic topologies network. Finally, based on datasets collected from a realistic motion RWM model in a virtual Ad Hoc cloud network, the performance behaviour of six selected deep learning algorithms to predict the next steps of positions, speed and residual battery energy values of these mobile nodes have been evaluated and compared. This work goes further by presenting two algorithm's training techniques to predict the next 300-time steps of position, speed, and energy. Results and dissuasion show the differences concerning prediction accuracy between using the single node dataset model or Multiple node's dataset model

Design and Analysis of Opportunistic MAC Protocols for Cognitive Radio Wireless Networks

As more and more wireless applications/services emerge in the market, the already heavily crowded radio spectrum becomes much scarcer. Meanwhile, however,as it is reported in the recent literature, there is a large amount of radio spectrum that is under-utilized. This motivates the concept of cognitive radio wireless networks that allow the unlicensed secondary-users (SUs) to dynamically use the vacant radio spectrum which is not being used by the licensed primary-users (PUs). In this dissertation, we investigate protocol design for both the synchronous and asynchronous cognitive radio networks with emphasis on the medium access control (MAC) layer. We propose various spectrum sharing schemes, opportunistic packet scheduling schemes, and spectrum sensing schemes in the MAC and physical (PHY) layers for different types of cognitive radio networks, allowing the SUs to opportunistically utilize the licensed spectrum while confining the level of interference to the range the PUs can tolerate. First, we propose the cross-layer based multi-channel MAC protocol, which integrates the cooperative spectrum sensing at PHY layer and the interweave-based spectrum access at MAC layer, for the synchronous cognitive radio networks. Second, we propose the channel-hopping based single-transceiver MAC protocol for the hardware-constrained synchronous cognitive radio networks, under which the SUs can identify and exploit the vacant channels by dynamically switching across the licensed channels with their distinct channel-hopping sequences. Third, we propose the opportunistic multi-channel MAC protocol with the two-threshold sequential spectrum sensing algorithm for asynchronous cognitive radio networks. Fourth, by combining the interweave and underlay spectrum sharing modes, we propose the adaptive spectrum sharing scheme for code division multiple access (CDMA) based cognitive MAC in the uplink communications over the asynchronous cognitive radio networks, where the PUs may have different types of channel usage patterns. Finally, we develop a packet scheduling scheme for the PU MAC protocol in the context of time division multiple access (TDMA)-based cognitive radio wireless networks, which is designed to operate friendly towards the SUs in terms of the vacant-channel probability. We also develop various analytical models, including the Markov chain models, M=GY =1 queuing models, cross-layer optimization models, etc., to rigorously analyze the performance of our proposed MAC protocols in terms of aggregate throughput, access delay, and packet drop rate for both the saturation network case and non-saturation network case. In addition, we conducted extensive simulations to validate our analytical models and evaluate our proposed MAC protocols/schemes. Both the numerical and simulation results show that our proposed MAC protocols/schemes can significantly improve the spectrum utilization efficiency of wireless networks