188 research outputs found
Multi-objective resource optimization in space-aerial-ground-sea integrated networks
Space-air-ground-sea integrated (SAGSI) networks are envisioned to connect satellite, aerial, ground,
and sea networks to provide connectivity everywhere and all the time in sixth-generation (6G) networks. However, the success of SAGSI networks is constrained by several challenges including
resource optimization when the users have diverse requirements and applications. We present a
comprehensive review of SAGSI networks from a resource optimization perspective. We discuss
use case scenarios and possible applications of SAGSI networks. The resource optimization discussion considers the challenges associated with SAGSI networks. In our review, we categorized
resource optimization techniques based on throughput and capacity maximization, delay minimization, energy consumption, task offloading, task scheduling, resource allocation or utilization,
network operation cost, outage probability, and the average age of information, joint optimization (data rate difference, storage or caching, CPU cycle frequency), the overall performance of
network and performance degradation, software-defined networking, and intelligent surveillance
and relay communication. We then formulate a mathematical framework for maximizing energy
efficiency, resource utilization, and user association. We optimize user association while satisfying
the constraints of transmit power, data rate, and user association with priority. The binary decision
variable is used to associate users with system resources. Since the decision variable is binary and
constraints are linear, the formulated problem is a binary linear programming problem. Based on
our formulated framework, we simulate and analyze the performance of three different algorithms
(branch and bound algorithm, interior point method, and barrier simplex algorithm) and compare
the results. Simulation results show that the branch and bound algorithm shows the best results,
so this is our benchmark algorithm. The complexity of branch and bound increases exponentially
as the number of users and stations increases in the SAGSI network. We got comparable results
for the interior point method and barrier simplex algorithm to the benchmark algorithm with low
complexity. Finally, we discuss future research directions and challenges of resource optimization
in SAGSI networks
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Joint Optimization of Computation, Communication and Caching in D2D-Assisted Caching-Enhanced MEC System
Article discusses how, in the era of intelligent applications, Mobile Edge Computing (MEC) is emerging as a promising technology that provides abundant resources for mobile devices. The authors of the article introduce a novel Device-to-Device (D2D)-assisted system to address this challenge
Resource Allocation in Networking and Computing Systems: A Security and Dependability Perspective
In recent years, there has been a trend to integrate networking and computing systems, whose management is getting increasingly complex. Resource allocation is one of the crucial aspects of managing such systems and is affected by this increased complexity. Resource allocation strategies aim to effectively maximize performance, system utilization, and profit by considering virtualization technologies, heterogeneous resources, context awareness, and other features. In such complex scenario, security and dependability are vital concerns that need to be considered in future computing and networking systems in order to provide the future advanced services, such as mission-critical applications. This paper provides a comprehensive survey of existing literature that considers security and dependability for resource allocation in computing and networking systems. The current research works are categorized by considering the allocated type of resources for different technologies, scenarios, issues, attributes, and solutions. The paper presents the research works on resource allocation that includes security and dependability, both singularly and jointly. The future research directions on resource allocation are also discussed. The paper shows how there are only a few works that, even singularly, consider security and dependability in resource allocation in the future computing and networking systems and highlights the importance of jointly considering security and dependability and the need for intelligent, adaptive and robust solutions. This paper aims to help the researchers effectively consider security and dependability in future networking and computing systems.publishedVersio
Toward Dynamic Social-Aware Networking Beyond Fifth Generation
The rise of the intelligent information world presents significant challenges for the telecommunication industry in meeting the service-level requirements of future applications and incorporating societal and behavioral awareness into the Internet of Things (IoT) objects. Social Digital Twins (SDTs), or Digital Twins augmented with social capabilities, have the potential to revolutionize digital transformation and meet the connectivity, computing, and storage needs of IoT devices in dynamic Fifth-Generation (5G) and Beyond Fifth-Generation (B5G) networks.
This research focuses on enabling dynamic social-aware B5G networking. The main contributions of this work include(i) the design of a reference architecture for the orchestration of SDTs at the network edge to accelerate the service discovery procedure across the Social Internet of Things (SIoT); (ii) a methodology to evaluate the highly dynamic system performance considering jointly communication and computing resources; (iii) a set of practical conclusions and outcomes helpful in designing future digital twin-enabled B5G networks. Specifically, we propose an orchestration for SDTs and an SIoT-Edge framework aligned with the Multi-access Edge Computing (MEC) architecture ratified by the European Telecommunications Standards Institute (ETSI). We formulate the optimal placement of SDTs as a Quadratic Assignment Problem (QAP) and propose a graph-based approximation scheme considering the different types of IoT devices, their social features, mobility patterns, and the limited computing resources of edge servers. We also study the appropriate intervals for re-optimizing the SDT deployment at the network edge. The results demonstrate that accounting for social features in SDT placement offers considerable improvements in the SIoT browsing procedure. Moreover, recent advancements in wireless communications, edge computing, and intelligent device technologies are expected to promote the growth of SIoT with pervasive sensing and computing capabilities, ensuring seamless connections among SIoT objects.
We then offer a performance evaluation methodology for eXtended Reality (XR) services in edge-assisted wireless networks and propose fluid approximations to characterize the XR content evolution. The approach captures the time and space dynamics of the content distribution process during its transient phase, including time-varying loads, which are affected by arrival, transition, and departure processes. We examine the effects of XR user mobility on both communication and computing patterns. The results demonstrate that communication and computing planes are the key barriers to meeting the requirement for real-time transmissions. Furthermore, due to the trend toward immersive, interactive, and contextualized experiences, new use cases affect user mobility patterns and, therefore, system performance.Cotutelle -yhteisväitöskirj
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Cyberattacks and security of cloud computing: a complete guideline
Cloud computing is an innovative technique that offers shared resources for stock cache and server management. Cloud computing saves time and monitoring costs for any organization and turns technological solutions for large-scale systems into server-to-service frameworks. However, just like any other technology, cloud computing opens up many forms of security threats and problems. In this work, we focus on discussing different cloud models and cloud services, respectively. Next, we discuss the security trends in the cloud models. Taking these security trends into account, we move to security problems, including data breaches, data confidentiality, data access controllability, authentication, inadequate diligence, phishing, key exposure, auditing, privacy preservability, and cloud-assisted IoT applications. We then propose security attacks and countermeasures specifically for the different cloud models based on the security trends and problems. In the end, we pinpoint some of the futuristic directions and implications relevant to the security of cloud models. The future directions will help researchers in academia and industry work toward cloud computing security
Rural implementation of connected, autonomous and electric vehicles
Connected, autonomous and electric vehicles (CAEV) are at the forefront of transport development. They are intended to provide efficient, safe and sustainable transport solutions to solve everyday transport problems including congestion, accidents and pollution. However, despite significant industry and government investment in the technology, little has been done in the way of exploring the implementation of CAEVs in rural scenarios. This thesis investigates the potential for rural road CAEV implementation in the UK. In this work, the rural digital and physical infrastructure requirements for CAEVs were first investigated through physical road-based experimentation of CAEV technologies. Further investigations into the challenges facing the rural implementation of CAEVs were then conducted through qualitative consultations with transport planning professionals. Quantitative and qualitative analysis of these investigations revealed a need for better rural infrastructure, and an overall lack of understanding regarding CAEVs and their rural implementation requirements amongst the transport planning industry. The need for a measurement tool for transport planners was identified, to expose the industry to, and educate them about, CAEVs and their rural potential. As a result, a CAEV Rural Transport Index (CARTI) is proposed as a simple measurement tool to assess the potential for rural CAEV implementation. The CARTI was implemented, and its effectiveness tested, through further consultation with transport planning professionals. The results indicate the potential for the CARTI to be used as a component of decision-making processes at both local authority and national levels. In conclusion, effective rural CAEV implementation relies on transport planners having a strong understanding of rural community transport needs, the solutions CAEV technologies can offer and the supporting infrastructure they require. Further, the CARTI was found to be an effective tool to support the development of this required understanding and recommendations have therefore been made for its future development
Trustworthy Federated Learning: A Survey
Federated Learning (FL) has emerged as a significant advancement in the field
of Artificial Intelligence (AI), enabling collaborative model training across
distributed devices while maintaining data privacy. As the importance of FL
increases, addressing trustworthiness issues in its various aspects becomes
crucial. In this survey, we provide an extensive overview of the current state
of Trustworthy FL, exploring existing solutions and well-defined pillars
relevant to Trustworthy . Despite the growth in literature on trustworthy
centralized Machine Learning (ML)/Deep Learning (DL), further efforts are
necessary to identify trustworthiness pillars and evaluation metrics specific
to FL models, as well as to develop solutions for computing trustworthiness
levels. We propose a taxonomy that encompasses three main pillars:
Interpretability, Fairness, and Security & Privacy. Each pillar represents a
dimension of trust, further broken down into different notions. Our survey
covers trustworthiness challenges at every level in FL settings. We present a
comprehensive architecture of Trustworthy FL, addressing the fundamental
principles underlying the concept, and offer an in-depth analysis of trust
assessment mechanisms. In conclusion, we identify key research challenges
related to every aspect of Trustworthy FL and suggest future research
directions. This comprehensive survey serves as a valuable resource for
researchers and practitioners working on the development and implementation of
Trustworthy FL systems, contributing to a more secure and reliable AI
landscape.Comment: 45 Pages, 8 Figures, 9 Table
Unleashing the Power of Edge-Cloud Generative AI in Mobile Networks: A Survey of AIGC Services
Artificial Intelligence-Generated Content (AIGC) is an automated method for
generating, manipulating, and modifying valuable and diverse data using AI
algorithms creatively. This survey paper focuses on the deployment of AIGC
applications, e.g., ChatGPT and Dall-E, at mobile edge networks, namely mobile
AIGC networks, that provide personalized and customized AIGC services in real
time while maintaining user privacy. We begin by introducing the background and
fundamentals of generative models and the lifecycle of AIGC services at mobile
AIGC networks, which includes data collection, training, finetuning, inference,
and product management. We then discuss the collaborative cloud-edge-mobile
infrastructure and technologies required to support AIGC services and enable
users to access AIGC at mobile edge networks. Furthermore, we explore
AIGCdriven creative applications and use cases for mobile AIGC networks.
Additionally, we discuss the implementation, security, and privacy challenges
of deploying mobile AIGC networks. Finally, we highlight some future research
directions and open issues for the full realization of mobile AIGC networks
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