698 research outputs found

    Online Resource Allocation for Semantic-Aware Edge Computing Systems

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    In this paper, we propose a semantic-aware joint communication and computation resource allocation framework for MEC systems. In the considered system, random tasks arrive at each terminal device (TD), which needs to be computed locally or offloaded to the MEC server. To further release the transmission burden, each TD sends the small-size extracted semantic information of tasks to the server instead of the original large-size raw data. An optimization problem of joint semanticaware division factor, communication and computation resource management is formulated. The problem aims to minimize the energy consumption of the whole system, while satisfying longterm delay and processing rate constraints. To solve this problem, an online low-complexity algorithm is proposed. In particular, Lyapunov optimization is utilized to decompose the original coupled long-term problem into a series of decoupled deterministic problems without requiring the realizations of future task arrivals and channel gains. Then, the block coordinate descent method and successive convex approximation algorithm are adopted to solve the current time slot deterministic problem by observing the current system states. Moreover, the closed-form optimal solution of each optimization variable is provided. Simulation results show that the proposed algorithm yields up to 41.8% energy reduction compared to its counterpart without semantic-aware allocation

    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

    Statistical Performance Evaluation for Energy Harvesting Communications based on Large Deviation Theorem

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    Energy harvesting (EH) is a promising technology for enhancing a network’s quality of service (QoS). EH-based communication systems are studied by tackling the challenges of energy-outage probability and energy conditioning. These issues motivate this research to develop new solutions for increasing the lifetime of device batteries by leveraging renewable energy sources available in the surrounding environment, for instance, from solar and radio-frequency (RF) energy through harvesting. This dissertation studies an energy outage problem and user QoS requirements for energy harvesting communications. In the first part of this dissertation, the performance of an energy harvesting communication link is analysed by allowing a certain level of energy-outage. In EH systems, energy consumed from the battery depends on the QoS required by the end user and on the channel state information. At the same time, the energy arrival to the battery depends on the strength of the power source, solar in this case, and is independent of the fading channel conditions and the required QoS. Due to the independence between the energy arrival into the battery and the energy consumed from there, it is challenging to estimate the exact status of the available energy in the battery. An energy outage is experienced when there is no further energy for the system to utilise for data transmission. In this part, a thorough study was carried out to analyse the required energy harvesting (EH) rate for satisfying the QoS requirements when a level of energy-outage is allowed in a point-to-point EH-based communication system equipped with a finite-sized battery. Furthermore, an expression relating the rate of the incoming energy with the fading channel conditions and the minimum required QoS of the system was provided to analyse the performance of the EH-based communication system under energy constraints. Finally, numerical results confirm the proposed mechanism’s analytical findings and correctness. In the second part of this dissertation, the performance of point-to-point communications is investigated in which the source node can harvest and store energy from RF signals and then use the harvested energy to communicate with its end destination. The continuous availability of RF energy has proved advantageous as a wireless power source to support low-power devices, making RF-based energy harvesting an alternative and viable solution for powering next-generation wireless networks, particularly for Internet-of-Things (IoT) applications. Specifically, the point-to-point RF-based energy-harvesting communication is considered, where the transmitter, which can be an IoT sensor, implements a time-switching protocol between the energy harvesting and the information transfer, and we focus on analysing the system performance while aiming to guarantee the required QoS of the end user subject to system constraint energy outage. The time-switching circuit at the source node allows the latter to switch between harvesting energy from a distant RF energy source and transmitting data to its target destination using the scavenged energy. Using a duality principle between the physical energy queue and a proposed virtual energy queue and assuming that a certain level of energy outage can be tolerated in the communication process, the system performance was evaluated with a novel analytical framework that leverages tools for the large deviation principle. In the third and last part of this dissertation, an empirical study of the RF-EH model is presented for ensuring the QoS constraints during an energy-outage for Simultaneous Wireless Information and Power Transfer (SWIPT) network. We consider a relay network over a Rayleigh fading channel where the relay lacks a permanent power source. Thus, we obtain energy from wireless energy harvesting (EH) of the source’s signals to maintain operation. This process is performed using a time-switching protocol at the relay for enhancing the quality of service (QoS) in SWIPT networks. A numerical approach is incorporated to evaluate the performance of the proposed RF-EH model in terms of different evaluation parameters such as time-switching protocol, transmit power and outage. The assumptions of the large deviation principle are satisfied using a proposed virtual energy queuing model, which is then used for the performance analysis. We established a closed-form expression for the system’s probability of experiencing an energy outage and the energy consumed by the relay battery

    Efficiency and Sustainability of the Distributed Renewable Hybrid Power Systems Based on the Energy Internet, Blockchain Technology and Smart Contracts-Volume II

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    The climate changes that are becoming visible today are a challenge for the global research community. In this context, renewable energy sources, fuel cell systems, and other energy generating sources must be optimally combined and connected to the grid system using advanced energy transaction methods. As this reprint presents the latest solutions in the implementation of fuel cell and renewable energy in mobile and stationary applications, such as hybrid and microgrid power systems based on the Energy Internet, Blockchain technology, and smart contracts, we hope that they will be of interest to readers working in the related fields mentioned above

    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

    QoE-Aware Cost-Minimizing Bandwidth Renting for Satellite-as-a-Service enabled Multiple-Beam SATCOM Systems

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    peer reviewedThe advent of Satellite as a Service (SaaS) platforms has empowered satellite service providers (SPs) to rent portions of satellite capacity from infrastructure providers (IPs) to cater to the diverse demands of their users across multiple satellite services. To effectively manage costs and maintain a high Quality of Experience (QoE) for numerous concurrent connections, SPs should secure flexible capacity from IPs. However, the irregular and unpredictable nature of traffic demands from various applications complicates the capacity-renting framework.This study presents a dynamic capacity allocation framework that efficiently handles diverse traffic flows with varying arrival rates, aiming to minimize rental costs while meeting blocking probability and QoE requirements. Utilizing the / /1 queuing model and a continuous-time Markov chain, the technical designs are framed as a statistical optimization problem. In this context, the system waiting-queue lengths are estimated using the transient probabilities of Kolmogorov equations. Subsequently, cumulative distribution functions are employed to re-formulate this challenging stochastic optimization problem into a convex form, which can be tackled through the Lagrangian duality method. Through extensive simulations and numerical assessments, we illustrate our method’s efficacy, with the proposed algorithm outperforming benchmarks by reducing costs by up to 9.85% and 3.1%

    Operational Research: methods and applications

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    This is the final version. Available on open access from Taylor & Francis via the DOI in this recordThroughout its history, Operational Research has evolved to include methods, models and algorithms that have been applied to a wide range of contexts. This encyclopedic article consists of two main sections: methods and applications. The first summarises the up-to-date knowledge and provides an overview of the state-of-the-art methods and key developments in the various subdomains of the field. The second offers a wide-ranging list of areas where Operational Research has been applied. The article is meant to be read in a nonlinear fashion and used as a point of reference by a diverse pool of readers: academics, researchers, students, and practitioners. The entries within the methods and applications sections are presented in alphabetical order. The authors dedicate this paper to the 2023 Turkey/Syria earthquake victims. We sincerely hope that advances in OR will play a role towards minimising the pain and suffering caused by this and future catastrophes

    Operational research:methods and applications

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    Throughout its history, Operational Research has evolved to include a variety of methods, models and algorithms that have been applied to a diverse and wide range of contexts. This encyclopedic article consists of two main sections: methods and applications. The first aims to summarise the up-to-date knowledge and provide an overview of the state-of-the-art methods and key developments in the various subdomains of the field. The second offers a wide-ranging list of areas where Operational Research has been applied. The article is meant to be read in a nonlinear fashion. It should be used as a point of reference or first-port-of-call for a diverse pool of readers: academics, researchers, students, and practitioners. The entries within the methods and applications sections are presented in alphabetical order
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