329 research outputs found

    Perception Intelligence Integrated Vehicle-to-Vehicle Optical Camera Communication.

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    Ubiquitous usage of cameras and LEDs in modern road and aerial vehicles open up endless opportunities for novel applications in intelligent machine navigation, communication, and networking. To this end, in this thesis work, we hypothesize the benefit of dual-mode usage of vehicular built-in cameras through novel machine perception capabilities combined with optical camera communication (OCC). Current key conception of understanding a line-of-sight (LOS) scenery is from the aspect of object, event, and road situation detection. However, the idea of blending the non-line-of-sight (NLOS) information with the LOS information to achieve a see-through vision virtually is new. This improves the assistive driving performance by enabling a machine to see beyond occlusion. Another aspect of OCC in the vehicular setup is to understand the nature of mobility and its impact on the optical communication channel quality. The research questions gathered from both the car-car mobility modelling, and evaluating a working setup of OCC communication channel can also be inherited to aerial vehicular situations like drone-drone OCC. The aim of this thesis is to answer the research questions along these new application domains, particularly, (i) how to enable a virtual see-through perception in the car assisting system that alerts the human driver about the visible and invisible critical driving events to help drive more safely, (ii) how transmitter-receiver cars behaves while in the mobility and the overall channel performance of OCC in motion modality, (iii) how to help rescue lost Unmanned Aerial Vehicles (UAVs) through coordinated localization with fusion of OCC and WiFi, (iv) how to model and simulate an in-field drone swarm operation experience to design and validate UAV coordinated localization for group of positioning distressed drones. In this regard, in this thesis, we present the end-to-end system design, proposed novel algorithms to solve the challenges in applying such a system, and evaluation results through experimentation and/or simulation

    Resilient and Scalable Forwarding for Software-Defined Networks with P4-Programmable Switches

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    Traditional networking devices support only fixed features and limited configurability. Network softwarization leverages programmable software and hardware platforms to remove those limitations. In this context the concept of programmable data planes allows directly to program the packet processing pipeline of networking devices and create custom control plane algorithms. This flexibility enables the design of novel networking mechanisms where the status quo struggles to meet high demands of next-generation networks like 5G, Internet of Things, cloud computing, and industry 4.0. P4 is the most popular technology to implement programmable data planes. However, programmable data planes, and in particular, the P4 technology, emerged only recently. Thus, P4 support for some well-established networking concepts is still lacking and several issues remain unsolved due to the different characteristics of programmable data planes in comparison to traditional networking. The research of this thesis focuses on two open issues of programmable data planes. First, it develops resilient and efficient forwarding mechanisms for the P4 data plane as there are no satisfying state of the art best practices yet. Second, it enables BIER in high-performance P4 data planes. BIER is a novel, scalable, and efficient transport mechanism for IP multicast traffic which has only very limited support of high-performance forwarding platforms yet. The main results of this thesis are published as 8 peer-reviewed and one post-publication peer-reviewed publication. The results cover the development of suitable resilience mechanisms for P4 data planes, the development and implementation of resilient BIER forwarding in P4, and the extensive evaluations of all developed and implemented mechanisms. Furthermore, the results contain a comprehensive P4 literature study. Two more peer-reviewed papers contain additional content that is not directly related to the main results. They implement congestion avoidance mechanisms in P4 and develop a scheduling concept to find cost-optimized load schedules based on day-ahead forecasts

    Performance Modeling of Vehicular Clouds Under Different Service Strategies

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    The amount of data being generated at the edge of the Internet is rapidly rising as a result of the Internet of Things (IoT). Vehicles themselves are contributing enormously to data generation with their advanced sensor systems. This data contains contextual information; it's temporal and needs to be processed in real-time to be of any value. Transferring this data to the cloud is not feasible due to high cost and latency. This has led to the introduction of edge computing for processing of data close to the source. However, edge servers may not have the computing capacity to process all the data. Future vehicles will have significant computing power, which may be underutilized, and they may have a stake in the processing of the data. This led to the introduction of a new computing paradigm called vehicular cloud (VC), which consists of interconnected vehicles that can share resources and communicate with each other. The VCs may process the data by themselves or in cooperation with edge servers. Performance modeling of VCs is important, as it will help to determine whether it can provide adequate service to users. It will enable determining appropriate service strategies and the type of jobs that may be served by the VC such that Quality of service (QoS) requirements are met. Job completion time and throughput of VCs are important performance metrics. However, performance modeling of VCs is difficult because of the volatility of resources. As vehicles join and leave the VC, available resources vary in time. Performance evaluation results in the literature are lacking, and available results mostly pertain to stationary VCs formed from parked vehicles. This thesis proposes novel stochastic models for the performance evaluation of vehicular cloud systems that take into account resource volatility, composition of jobs from multiple tasks that can execute concurrently under different service strategies. First, we developed a stochastic model to analyze the job completion time in a VC system deployed on a highway with service interruption. Next, we developed a model to analyze the job completion time in a VC system with a service interruption avoidance strategy. This strategy aims to prevent disruptions in task service by only assigning tasks to vehicles that can complete the tasks’ execution before they leave the VC. In addition to analyzing job completion time, we evaluated the computing capacity of VC systems with a service interruption avoidance strategy, determining the number of jobs a VC system can complete during its lifetime. Finally, we studied the computing capacity of a robotaxi fleet, analyzing the average number of tasks that a robotaxi fleet can serve to completion during a cycle. By developing these models, conducting various analyses, and comparing the numerical results of the analyses to extensive Monte Carlo simulation results, we gained insights into job completion time, computing capacity, and overall performance of VC systems deployed in different contexts

    Energy-Sustainable IoT Connectivity: Vision, Technological Enablers, Challenges, and Future Directions

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    Technology solutions must effectively balance economic growth, social equity, and environmental integrity to achieve a sustainable society. Notably, although the Internet of Things (IoT) paradigm constitutes a key sustainability enabler, critical issues such as the increasing maintenance operations, energy consumption, and manufacturing/disposal of IoT devices have long-term negative economic, societal, and environmental impacts and must be efficiently addressed. This calls for self-sustainable IoT ecosystems requiring minimal external resources and intervention, effectively utilizing renewable energy sources, and recycling materials whenever possible, thus encompassing energy sustainability. In this work, we focus on energy-sustainable IoT during the operation phase, although our discussions sometimes extend to other sustainability aspects and IoT lifecycle phases. Specifically, we provide a fresh look at energy-sustainable IoT and identify energy provision, transfer, and energy efficiency as the three main energy-related processes whose harmonious coexistence pushes toward realizing self-sustainable IoT systems. Their main related technologies, recent advances, challenges, and research directions are also discussed. Moreover, we overview relevant performance metrics to assess the energy-sustainability potential of a certain technique, technology, device, or network and list some target values for the next generation of wireless systems. Overall, this paper offers insights that are valuable for advancing sustainability goals for present and future generations.Comment: 25 figures, 12 tables, submitted to IEEE Open Journal of the Communications Societ

    Reinforcement Learning Based Resource Allocation for Energy-Harvesting-Aided D2D Communications in IoT Networks

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    It is anticipated that mobile data traffic and the demand for higher data rates will increase dramatically as a result of the explosion of wireless devices, such as the Internet of Things (IoT) and machine-to-machine communication. There are numerous location-based peer-to-peer services available today that allow mobile users to communicate directly with one another, which can help offload traffic from congested cellular networks. In cellular networks, Device-to-Device (D2D) communication has been introduced to exploit direct links between devices instead of transmitting through a the Base Station (BS). However, it is critical to note that D2D and IoT communications are hindered heavily by the high energy consumption of mobile devices and IoT devices. This is because their battery capacity is restricted. There may be a way for energy-constrained wireless devices to extend their lifespan by drawing upon reusable external sources of energy such as solar, wind, vibration, thermoelectric, and radio frequency (RF) energy in order to overcome the limited battery problem. Such approaches are commonly referred to as Energy Harvesting (EH) There is a promising approach to energy harvesting that is called Simultaneous Wireless Information and Power Transfer (SWIPT). Due to the fact that wireless users are on the rise, it is imperative that resource allocation techniques be implemented in modern wireless networks. This will facilitate cooperation among users for limited resources, such as time and frequency bands. As well as ensuring that there is an adequate supply of energy for reliable and efficient communication, resource allocation also provides a roadmap for each individual user to follow in order to consume the right amount of energy. In D2D networks with time, frequency, and power constraints, significant computing power is generally required to achieve a joint resource management design. Thus the purpose of this study is to develop a resource allocation scheme that is based on spectrum sharing and enables low-cost computations for EH-assisted D2D and IoT communication. Until now, there has been no study examining resource allocation design for EH-enabled IoT networks with SWIPT-enabled D2D schemes that utilize learning techniques and convex optimization. In most of the works, optimization and iterative approaches with a high level of computational complexity have been used which is not feasible in many IoT applications. In order to overcome these obstacles, a learning-based resource allocation mechanism based on the SWIPT scheme in IoT networks is proposed, where users are able to harvest energy from different sources. The system model consists of multiple IoT users, one BS, and multiple D2D pairs in EH-based IoT networks. As a means of developing an energy-efficient system, we consider the SWIPT scheme with D2D pairs employing the time switching method (TS) to capture energy from the environment, whereas IoT users employ the power splitting method (PS) to harvest energy from the BS. A mixed-integer nonlinear programming (MINLP) approach is presented for the solution of the Energy Efficiency (EE) problem by jointly optimizing subchannel allocation, power-splitting factor, power, and time together. As part of the optimization approach, the original EE optimization problem is decomposed into three subproblems, namely: (a) subchannel assignment and power splitting factor, (b) power allocation, and (c) time allocation. In order to solve the subproblem assignment problem, which involves discrete variables, the Q-learning approach is employed. Due to the large size of the overall problem and the continuous nature of certain variables, it is impractical to optimize all variables by using the learning technique. Instead dealing for the continuous variable problems, namely power and time allocation, the original non-convex problem is first transformed into a convex one, then the Majorization-Minimization (MM) approach is applied as well as the Dinkelbach. The performance of the proposed joint Q-learning and optimization algorithm has been evaluated in detail. In particular, the solution was compared with a linear EH model, as well as two heuristic algorithms, namely the constrained allocation algorithm and the random allocation algorithm, in order to determine its performance. The results indicate that the technique is superior to conventional approaches. For example, it can be seen that for the distance of d=10d = 10 m, our proposed algorithm leads to EE improvement when compared to the method such as prematching algorithm, constrained allocation, and random allocation methods by about 5.26\%, 110.52\%, and 143.90\%, respectively. Considering the simulation results, the proposed algorithm is superior to other methods in the literature. Using spectrum sharing and harvesting energy from D2D and IoT devices achieves impressive EE gains. This superior performance can be seen both in terms of the average and sum EEs, as well as when compared to other baseline schemes

    Vehicle as a Service (VaaS): Leverage Vehicles to Build Service Networks and Capabilities for Smart Cities

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    Smart cities demand resources for rich immersive sensing, ubiquitous communications, powerful computing, large storage, and high intelligence (SCCSI) to support various kinds of applications, such as public safety, connected and autonomous driving, smart and connected health, and smart living. At the same time, it is widely recognized that vehicles such as autonomous cars, equipped with significantly powerful SCCSI capabilities, will become ubiquitous in future smart cities. By observing the convergence of these two trends, this article advocates the use of vehicles to build a cost-effective service network, called the Vehicle as a Service (VaaS) paradigm, where vehicles empowered with SCCSI capability form a web of mobile servers and communicators to provide SCCSI services in smart cities. Towards this direction, we first examine the potential use cases in smart cities and possible upgrades required for the transition from traditional vehicular ad hoc networks (VANETs) to VaaS. Then, we will introduce the system architecture of the VaaS paradigm and discuss how it can provide SCCSI services in future smart cities, respectively. At last, we identify the open problems of this paradigm and future research directions, including architectural design, service provisioning, incentive design, and security & privacy. We expect that this paper paves the way towards developing a cost-effective and sustainable approach for building smart cities.Comment: 32 pages, 11 figure

    Modelling, Dimensioning and Optimization of 5G Communication Networks, Resources and Services

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    This reprint aims to collect state-of-the-art research contributions that address challenges in the emerging 5G networks design, dimensioning and optimization. Designing, dimensioning and optimization of communication networks resources and services have been an inseparable part of telecom network development. The latter must convey a large volume of traffic, providing service to traffic streams with highly differentiated requirements in terms of bit-rate and service time, required quality of service and quality of experience parameters. Such a communication infrastructure presents many important challenges, such as the study of necessary multi-layer cooperation, new protocols, performance evaluation of different network parts, low layer network design, network management and security issues, and new technologies in general, which will be discussed in this book

    Optimization of Handover, Survivability, Multi-Connectivity and Secure Slicing in 5G Cellular Networks using Matrix Exponential Models and Machine Learning

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    Title from PDF of title page, viewed January 31, 2023Dissertation advisor: Cory BeardVitaIncludes bibliographical references (pages 173-194)Dissertation (Ph.D.)--Department of Computer Science and Electrical Engineering. University of Missouri--Kansas City, 2022This works proposes optimization of cellular handovers, cellular network survivability modeling, multi-connectivity and secure network slicing using matrix exponentials and machine learning techniques. We propose matrix exponential (ME) modeling of handover arrivals with the potential to much more accurately characterize arrivals and prioritize resource allocation for handovers, especially handovers for emergency or public safety needs. With the use of a ‘B’ matrix for representing a handover arrival, we have a rich set of dimensions to model system handover behavior. We can study multiple parameters and the interactions between system events along with the user mobility, which would trigger a handoff in any given scenario. Additionally, unlike any traditional handover improvement scheme, we develop a ‘Deep-Mobility’ model by implementing a deep learning neural network (DLNN) to manage network mobility, utilizing in-network deep learning and prediction. We use the radio and the network key performance indicators (KPIs) to train our model to analyze network traffic and handover requirements. Cellular network design must incorporate disaster response, recovery and repair scenarios. Requirements for high reliability and low latency often fail to incorporate network survivability for mission critical and emergency services. Our Matrix Exponential (ME) model shows how survivable networks can be designed based on controlling numbers of crews, times taken for individual repair stages, and the balance between fast and slow repairs. Transient and the steady state representations of system repair models, namely, fast and slow repairs for networks consisting of multiple repair crews have been analyzed. Failures are exponentially modeled as per common practice, but ME distributions describe the more complex recovery processes. In some mission critical communications, the availability requirements may exceed five or even six nines (99.9999%). To meet such a critical requirement and minimize the impact of mobility during handover, a Fade Duration Outage Probability (FDOP) based multiple radio link connectivity handover method has been proposed. By applying such a method, a high degree of availability can be achieved by utilizing two or more uncorrelated links based on minimum FDOP values. Packet duplication (PD) via multi-connectivity is a method of compensating for lost packets on a wireless channel. Utilizing two or more uncorrelated links, a high degree of availability can be attained with this strategy. However, complete packet duplication is inefficient and frequently unnecessary. We provide a novel adaptive fractional packet duplication (A-FPD) mechanism for enabling and disabling packet duplication based on a variety of parameters. We have developed a ‘DeepSlice’ model by implementing Deep Learning (DL) Neural Network to manage network load efficiency and network availability, utilizing in-network deep learning and prediction. Our Neural Network based ‘Secure5G’ Network Slicing model will proactively detect and eliminate threats based on incoming connections before they infest the 5G core network elements. These will enable the network operators to sell network slicing as-a-service to serve diverse services efficiently over a single infrastructure with higher level of security and reliability.Introduction -- Matrix exponential and deep learning neural network modeling of cellular handovers -- Survivability modeling in cellular networks -- Multi connectivity based handover enhancement and adaptive fractional packet duplication in 5G cellular networks -- Deepslice and Secure5G: a deep learning framework towards an efficient, reliable and secure network slicing in 5G networks -- Conclusion and future scop
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