9,987 research outputs found
Internet of robotic things : converging sensing/actuating, hypoconnectivity, artificial intelligence and IoT Platforms
The Internet of Things (IoT) concept is evolving rapidly and influencing newdevelopments in various application domains, such as the Internet of MobileThings (IoMT), Autonomous Internet of Things (A-IoT), Autonomous Systemof Things (ASoT), Internet of Autonomous Things (IoAT), Internetof Things Clouds (IoT-C) and the Internet of Robotic Things (IoRT) etc.that are progressing/advancing by using IoT technology. The IoT influencerepresents new development and deployment challenges in different areassuch as seamless platform integration, context based cognitive network integration,new mobile sensor/actuator network paradigms, things identification(addressing, naming in IoT) and dynamic things discoverability and manyothers. The IoRT represents new convergence challenges and their need to be addressed, in one side the programmability and the communication ofmultiple heterogeneous mobile/autonomous/robotic things for cooperating,their coordination, configuration, exchange of information, security, safetyand protection. Developments in IoT heterogeneous parallel processing/communication and dynamic systems based on parallelism and concurrencyrequire new ideas for integrating the intelligent “devices”, collaborativerobots (COBOTS), into IoT applications. Dynamic maintainability, selfhealing,self-repair of resources, changing resource state, (re-) configurationand context based IoT systems for service implementation and integrationwith IoT network service composition are of paramount importance whennew “cognitive devices” are becoming active participants in IoT applications.This chapter aims to be an overview of the IoRT concept, technologies,architectures and applications and to provide a comprehensive coverage offuture challenges, developments and applications
Resource Allocation of Federated Learning for the Metaverse with Mobile Augmented Reality
The Metaverse has received much attention recently. Metaverse applications
via mobile augmented reality (MAR) require rapid and accurate object detection
to mix digital data with the real world. Federated learning (FL) is an
intriguing distributed machine learning approach due to its privacy-preserving
characteristics. Due to privacy concerns and the limited computation resources
on mobile devices, we incorporate FL into MAR systems of the Metaverse to train
a model cooperatively. Besides, to balance the trade-off between energy,
execution latency and model accuracy, thereby accommodating different demands
and application scenarios, we formulate an optimization problem to minimize a
weighted combination of total energy consumption, completion time and model
accuracy. Through decomposing the non-convex optimization problem into two
subproblems, we devise a resource allocation algorithm to determine the
bandwidth allocation, transmission power, CPU frequency and video frame
resolution for each participating device. We further present the convergence
analysis and computational complexity of the proposed algorithm. Numerical
results show that our proposed algorithm has better performance (in terms of
energy consumption, completion time and model accuracy) under different weight
parameters compared to existing benchmarks.Comment: This paper appears in IEEE Transactions on Wireless Communications.
DOI: https://doi.org/10.1109/TWC.2023.3326884 It is the journal version of
2022 IEEE 42nd International Conference on Distributed Computing Systems
(ICDCS) paper: arXiv:2209.14900; i.e.,
https://doi.org/10.1109/ICDCS54860.2022.0010
Towards Tactile Internet in Beyond 5G Era: Recent Advances, Current Issues and Future Directions
Tactile Internet (TI) is envisioned to create a paradigm shift from the content-oriented
communications to steer/control-based communications by enabling real-time transmission of haptic information (i.e., touch, actuation, motion, vibration, surface texture) over Internet in addition to the conventional audiovisual and data traffics. This emerging TI technology, also considered as the next evolution phase of Internet of Things (IoT), is expected to create numerous opportunities for technology markets in a wide variety of applications ranging from teleoperation systems and Augmented/Virtual Reality (AR/VR) to automotive safety and eHealthcare towards addressing the complex problems of human society. However, the realization of TI over wireless media in the upcoming Fifth Generation (5G) and beyond networks creates various non-conventional communication challenges and stringent requirements
in terms of ultra-low latency, ultra-high reliability, high data-rate connectivity, resource allocation, multiple access and quality-latency-rate tradeoff. To this end, this paper aims to provide a holistic view on wireless TI along with a thorough review of the existing state-of-the-art, to identify and analyze the involved technical issues, to highlight potential solutions and to propose future research directions. First, starting with the vision of TI and recent advances and a review of related survey/overview articles, we present a generalized framework for wireless TI in the Beyond 5G Era including a TI architecture, the main technical requirements, the key application areas and potential enabling technologies. Subsequently, we provide a comprehensive review of the existing TI works by broadly categorizing them into three main paradigms; namely, haptic communications, wireless AR/VR, and autonomous, intelligent and cooperative mobility systems. Next, potential enabling technologies across physical/Medium Access Control (MAC) and network layers are identified and discussed in detail. Also, security and privacy issues of TI applications are discussed
along with some promising enablers. Finally, we present some open research challenges and recommend promising future research directions
Metaverse for Wireless Systems: Architecture, Advances, Standardization, and Open Challenges
The growing landscape of emerging wireless applications is a key driver
toward the development of novel wireless system designs. Such a design can be
based on the metaverse that uses a virtual model of the physical world systems
along with other schemes/technologies (e.g., optimization theory, machine
learning, and blockchain). A metaverse using a virtual model performs proactive
intelligent analytics prior to a user request for efficient management of the
wireless system resources. Additionally, a metaverse will enable
self-sustainability to operate wireless systems with the least possible
intervention from network operators. Although the metaverse can offer many
benefits, it faces some challenges as well. Therefore, in this tutorial, we
discuss the role of a metaverse in enabling wireless applications. We present
an overview, key enablers, design aspects (i.e., metaverse for wireless and
wireless for metaverse), and a novel high-level architecture of metaverse-based
wireless systems. We discuss metaverse management, reliability, and security of
the metaverse-based system. Furthermore, we discuss recent advances and
standardization of metaverse-enabled wireless system. Finally, we outline open
challenges and present possible solutions
Play to Earn in the Metaverse with Mobile Edge Computing over Wireless Networks: A Deep Reinforcement Learning Approach
The Metaverse play-to-earn games have been gaining popularity as they enable
players to earn in-game tokens which can be translated to real-world profits.
With the advancements in augmented reality (AR) technologies, users can play AR
games in the Metaverse. However, these high-resolution games are
compute-intensive, and in-game graphical scenes need to be offloaded from
mobile devices to an edge server for computation. In this work, we consider an
optimization problem where the Metaverse Service Provider (MSP)'s objective is
to reduce downlink transmission latency of in-game graphics, the latency of
uplink data transmission, and the worst-case (greatest) battery charge
expenditure of user equipments (UEs), while maximizing the worst-case (lowest)
UE resolution-influenced in-game earning potential through optimizing the
downlink UE-Metaverse Base Station (UE-MBS) assignment and the uplink
transmission power selection. The downlink and uplink transmissions are then
executed asynchronously. We propose a multi-agent, loss-sharing (MALS)
reinforcement learning model to tackle the asynchronous and asymmetric problem.
We then compare the MALS model with other baseline models and show its
superiority over other methods. Finally, we conduct multi-variable optimization
weighting analyses and show the viability of using our proposed MALS algorithm
to tackle joint optimization problems.Comment: This paper has been submitted to IEEE Transactions on Wireless
Communications (TWC), 202
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