Artificial intelligence for the metaverse: A survey Huynh-The, Thien; Pham, Quoc-Viet; Pham, Xuan-Qui ...
Engineering applications of artificial intelligence,
January 2023, 2023-01-00, Volume:
117
Journal Article
Peer reviewed
Along with the massive growth of the Internet from the 1990s until now, various innovative technologies have been created to bring users breathtaking experiences with more virtual interactions in ...cyberspace. Many virtual environments have been developed with immersive experience and digital transformation, but most are incoherent instead of being integrated into a platform. In this context, metaverse has been introduced as a shared virtual world that is fueled by many emerging technologies. Among such technologies, artificial intelligence (AI) has shown the great importance of enhancing immersive experience and enabling human-like intelligence of virtual agents. In this survey, we make a beneficial effort to explore the role of AI, including machine learning algorithms and deep learning architectures, in the foundation and development of the metaverse. As the main contributions, we convey a comprehensive investigation of AI-based methods concerning several technical aspects (e.g., natural language processing, machine vision, blockchain, networking, digital twin, and neural interface) that have potentials to build virtual worlds in the metaverse. Furthermore, several primary AI-aided applications, including healthcare, manufacturing, smart cities, and gaming, are studied to be promisingly deployed in the virtual worlds. Finally, we conclude the key contribution and open some future research directions of AI for the metaverse. Serving as a foundational survey, this work will help researchers, including experts and non-experts in related fields, in applying, developing, and optimizing AI techniques to polish the appearance of virtual worlds and improve the quality of applications built in the metaverse.
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•Studying AI-based technical works that are potential to build the Metaverse.•Surveying AI-aided application works that are promisingly deployed in the Metaverse.•Reviewing of some common AI-fueled Metaverse projects.•Providing some research directions based on some major limitations of AI.
The extent to which electrophores covalently bridged by a saturated linker are electrochemically independent was investigated considering the charge/spin duality of the electron and functionality of ...the electrophore as a spin carrier upon reduction. By combining computational modeling with electrochemical experiments, we investigated the mechanism by which tethered electrophores react together within 4,4'-oligonmethylene-bipyridinium assemblies (with n = 2 to 5). We show that native dicationic electrophores (redox state Z = +2) are folded prior to electron injection into the system, allowing the emergence of supra-molecular orbitals (supra-MOs) likely to support the process of the reductive s bond formation giving cyclomers. Indeed, for Z = +2, London Dispersion (LD) forces contribute to flatten the potential energy surface such that all-trans and folded conformers are approximately isoenergetic. Then, upon one-electron injection, for radical cations (Z = +1), LD forces significantly stabilize the folded conformers, except for the ethylene derivative deprived of supra-MOs. For radical cations equipped with supra-MOs, the unpaired electron is delocalized over both heterocycles through space. Cyclomer completion (Z = 0) upon the second electron transfer occurs according to the inversion of redox potentials. This mechanism explains why intramolecular reactivity is favored and why pyridinium electrophores are not independent.
In multi-access edge computing (MEC), computation tasks offloaded from users are usually associated with specific services that need to be cached in MEC nodes to enable task execution. The decisions ...as to which services to cache and which tasks to execute on each resource-limited MEC node are critical to maximizing the offloading efficiency. Moreover, quality of experience (QoE) is a key factor driving offloading decisions, so that limited computing resources can be effectively utilized to keep users satisfied. Therefore, in this letter, we introduce a new QoE-based utility optimization approach to address the problem of joint service caching and task offloading in MEC systems. Our utility model reflects the trade-off between the user's perception of service latency and the cost the user pays for the allocated computing resources. We formulate total utility maximization as an integer nonlinear programming problem and propose a genetic-based algorithm to solve it efficiently. Finally, evaluation results show that our proposal can significantly improve total user utility over traditional baselines.
Parked vehicle-assisted multi-access edge computing (PVMEC) is a paradigm that exploits the under-utilized resources of parked vehicles (PVs) to assist MEC servers for offloaded task execution. This ...article investigates a partial offloading strategy for multi-user PVMEC, where each mobile device (MD)'s task can be partially offloaded to the MEC server or a nearby PV. We formulate the system utility maximization problem with joint consideration of offloading decisions, offloading ratio, and resource allocation. Considering the complexity and privacy issues of centralized scheme as well as the inherent resource contention among MDs, we adopt game-theoretic approach to devise a low-complexity distributed offloading scheme, in which the offloading decision-making problem is modeled as an exact potential game, and the optimal offloading ratio and resource allocation are determined using a subgradient method. Finally, simulation results show that our proposed approach can significantly improve the system utility over traditional baselines.
The rapid development of Internet of Things applications, along with the limitations of cloud computing due mainly to the far distance between Internet of Thing devices and cloud-based platform, has ...promoted a newly distributed computing platform based on collaboration between cloud computing and fog computing. Fog computing helps to reduce transmission latency and monetary cost for cloud resources, while cloud computing helps to fulfill the increasing demands of large-scale compute-intensive offloading applications. In this article, we study the tradeoff issue between the makespan and cloud cost when scheduling large-scale applications in such a platform. We propose a scheduling algorithm called Cost-Makespan aware Scheduling heuristic whose major objective is to achieve the balance between the performance of application execution and the mandatory cost for the use of cloud resources. Additionally, an efficient task reassignment strategy based on the critical path of the directed acyclic graph modeling the applications is also proposed to refine the output schedules of the Cost-Makespan aware Scheduling algorithm to satisfy the user-defined deadline constraints or quality of service of the system. We also verify our proposal by extensive simulations, and the experimental results show that our scheduling approach is more cost-effective and achieves better performance compared to others.
In recent years, multi-access edge computing (MEC) has become a promising technology used in 5G networks based on its ability to offload computational tasks from mobile devices (MDs) to edge servers ...in order to address MD-specific limitations. Despite considerable research on computation offloading in 5G networks, this activity in multi-tier multi-MEC server systems continues to attract attention. Here, we investigated a two-tier computation-offloading strategy for multi-user multi-MEC servers in heterogeneous networks. For this scenario, we formulated a joint resource-allocation and computation-offloading decision strategy to minimize the total computing overhead of MDs, including completion time and energy consumption. The optimization problem was formulated as a mixed-integer nonlinear program problem of NP-hard complexity. Under complex optimization and various application constraints, we divided the original problem into two subproblems: decisions of resource allocation and computation offloading. We developed an efficient, low-complexity algorithm using particle swarm optimization capable of high-quality solutions and guaranteed convergence, with a high-level heuristic (i.e., meta-heuristic) that performed well at solving a challenging optimization problem. Simulation results indicated that the proposed algorithm significantly reduced the total computing overhead of MDs relative to several baseline methods while guaranteeing to converge to stable solutions.
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•Two fluorescent sensors for UO22+ were synthesized.•The sensors are based on salicylaldehyde azine derivative.•The sensors exhibit efficient AIE properties in organo-aqueous ...medium.•Uranyl complexation induces efficient quenching.
A highly selective fluorescent sensor based on salicylaldehyde azine derivative for the detection of uranyl cation was developed. The aggregation induced emission characteristics of obtained compounds were investigated and a strong emission of these derivatives was observed upon aggregation in organoaqueous solvent. The emission of these aggregated forms was found to be effectively quenched by uranyl cation. The effect of uranyl was shown to be destructive rather than constructive to the aggregation of the salicylaldehyde azine derivatives. An aggregate-breaking process was proposed to explain the destruction of the aggregates by the complexation with uranyl cation. The sensor showed good selectivity towards uranyl and with a low detection limit up to ppb scale, hence, can be used as an efficient fluorescent sensor for detecting uranyl cation in aqueous solvent.
Wi-Fi-based indoor positioning for determining accurate wireless indoor location information has become crucial in meeting increasing demands for location-based services by leveraging the Internet of ...Things (IoT) and ubiquitous connectivity. Most Wi-Fi-based indoor positioning techniques using wireless received signal strength (RSS)-based methods are affected by the indoor environment and depend on the respective signals from at least three reference access points. In this paper, we propose a cloudlet-based cloud computing system enabling Wi-Fi indoor positioning and navigation through a Wi-Fi located on a one-hop wireless network. Our cloudlet-based cloud computing system provides the reference point data and real-time interactive response for a self-driving indoor cart. The system was tested in a real environment with the following results: (1) our system autonomously performed actions, such as turning right or left or going straight according to a movement decision algorithm and determined the position within a stable range of Wi-Fi coverage; (2) the cloudlet and core cloud can track navigation for an indoor self-driving cart; (3) the global and local positions designed for reference access points and a specific position can navigate the self-driving cart to a particular position accurately; (4) the moving edge clouds play a role in deciding three action movements (go straight, turn left, and turn right), as well as managing the local position of the items; and (5) a core cloud is deployed to store all information for the items, such as their positions and corresponding Wi-Fi locations. A core cloud manages items that have the same position (i.e., a global position) defined as the corresponding Wi-Fi location. Finally, the practical results have significance in designing a cloudlet-based cloud computing system enabling Wi-Fi indoor positioning and navigation.
The resource limitation of multi-access edge computing (MEC) is one of the major issues in order to provide low-latency high-reliability computing services for Internet of Things (IoT) devices. ...Moreover, with the steep rise of task requests from IoT devices, the requirement of computation tasks needs dynamic scalability while using the potential of offloading tasks to mobile volunteer nodes (MVNs). We, therefore, propose a scalable vehicle-assisted MEC (SVMEC) paradigm, which cannot only relieve the resource limitation of MEC but also enhance the scalability of computing services for IoT devices and reduce the cost of using computing resources. In the SVMEC paradigm, a MEC provider can execute its users’ tasks by choosing one of three ways: (i) Do itself on local MEC, (ii) offload to the remote cloud, and (iii) offload to the MVNs. We formulate the problem of joint node selection and resource allocation as a Mixed Integer Nonlinear Programming (MINLP) problem, whose major objective is to minimize the total computation overhead in terms of the weighted-sum of task completion time and monetary cost for using computing resources. In order to solve it, we adopt alternative optimization techniques by decomposing the original problem into two sub-problems: Resource allocation sub-problem and node selection sub-problem. Simulation results demonstrate that our proposed scheme outperforms the existing schemes in terms of the total computation overhead.
Summary
The existing adaptive multichannel medium access control (MAC) protocols in vehicular ad hoc networks can adjust themselves according to different vehicular traffic densities. These protocols ...can increase throughput and guarantee a bounded transmission delay for real‐time safety applications. However, the optimized control channel interval is computed based on the maximum throughput while ignoring the strict safety packet transmission delay requirements. In this paper, we analyze the effects of the throughput and strict safety packet transmission delay with adaptive multichannel MAC protocols, such as connectivity‐aware MAC (CA MAC), adaptive multi‐priority distributed MAC (APDM), multi‐priority supported p‐persistent MAC (MP MAC), and variable control channel interval MAC (VCI) protocols. The performance and analysis results show that: (a) under a low data rate condition, CA MAC does not guarantee a strict safety packet transmission delay; (b) APDM not only satisfies the safety packet transmission requirement, but also provides the lowest safety packet transmission delay; (c) under a high data rate condition, we suggest APDM for use as an adaptive MAC protocol because it allows for high throughput for nonsafety packets and preserves low safety packet transmission delay; (d) under a low data rate condition with various data packet sizes, we suggest MP MAC for high throughput, which satisfies the safety packet transmission requirement; and (e) under low vehicle density and low data rate conditions, VCI can support high throughput. A balance between transmission delay and throughput must be considered to improve the optimal efficiency, reliability, and adaptability.
The existing adaptive multichannel medium access control (MAC) protocols in vehicular ad hoc networks, which adjust themselves according to different vehicular traffic densities, can increase throughput and guarantee a bounded transmission delay for real‐time safety applications. In this paper, we qualitatively compared the existing adaptive multichannel MAC protocols under different traffic conditions to obtain the transmission delay and throughput results. A balance between transmission delay and throughput must be considered to improve the optimal efficiency, reliability, and adaptability.
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