With the emergence of in-vehicle applications, providing required computational capabilities is becoming a crucial problem. This paper proposes framework named autonomous vehicular edge (AVE) for computing on road, with aim increasing vehicles in decentralized manner. By managing idle resources and using them efficiently, proposed AVE can provide computation services dynamic environments without requiring particular infrastructures to be deployed. Specifically, this introduces workflow...

Completion time and energy consumption of the unmanned aerial vehicle (UAV) are two important design aspects in UAV-enabled applications. In this article, we consider a mobile-edge computing (MEC) system for Internet-of-Things (IoT) computation offloading with limited or no common cloud/edge infrastructure. We study joint resource allocation, as well UAV trajectory minimization completion UAV, subject to IoT devices' task budget constraints. first problem without predetermined time,...

As an enabling technology for the Internet of Vehicles (IoV), mobile edge computing (MEC) provides potential solutions sharing computation capabilities among vehicles, in addition to other accessible resources. In this article, we first introduce a distributed vehicular solution named autonomous (AVE), which makes it possible share neighboring vehicles' available resources via vehicle-tovehicle (V2V) communications. We then extend concept more general online called hybrid cloud (HVC),...

The appearance of generative adversarial networks (GAN) provides a new approach and framework for computer vision. Compared with traditional machine learning algorithms, GAN works via training concept is more powerful in both feature representation. also exhibits some problems, such as non-convergence, model collapse, uncontrollability due to high degree freedom. How improve the theory apply it computer-vision-related tasks have now attracted much research efforts. In this paper, recently...

Vehicular computation offloading is a well-received strategy to execute delay-sensitive and/or compute-intensive tasks of legacy vehicles. The response time vehicular can be shortened by using mobile edge computing that offers strong power, driving these closer end users. However, the quality communication hard guarantee due obstruction dense buildings or lack infrastructure in some zones. Unmanned Aerial Vehicles (UAVs), therefore, have become one means establish links for two ends owing...

In this paper, we investigate the problem of age information (AoI)-aware radio resource management for expected long-term performance optimization in a Manhattan grid vehicle-to-vehicle network. With observation global network state at each scheduling slot, roadside unit (RSU) allocates frequency bands and schedules packet transmissions all vehicle user equipment-pairs (VUE-pairs). We model stochastic decision-making procedure as discrete-time single-agent Markov decision process (MDP). The...

Facing the trend of merging wireless communications and multi-access edge computing (MEC), this article studies computation offloading in beyond fifth generation networks. To address technical challenges originating from uncertainties sharing limited resource an MEC system, we formulate problem as a multi-agent Markov decision process, for which distributed learning framework is proposed. We present case study on orchestration to showcase potential online reinforcement algorithm developed...

Volumetric video enables a six-degree-of-freedom (6DoF) immersive viewing experience and has wide range of applications in entertainment education, among others. Most existing approaches to volumetric streaming are extensions VR solutions that do not take into account user behavior the properties during tiling process, complexity decoding is high. To this end, we study paper address research questions mentioned above. In particular, first propose hybrid visual saliency hierarchical...

Real-time smart grid monitoring is critical to enhancing resiliency and operational efficiency of power equipment. Cloud-based edge-based fault detection systems integrating deep learning have been proposed recently monitor the in real time. However, state-of-the-art cloud-based may require uploading a large amount data suffer from long network delay, while schemes do not adequately consider requirement thus cannot provide flexible optimal performance. To solve these problems, we study...

This paper studies how to efficiently apply device-to-device (D2D) communications underlaying a cellular system support vehicle-to-vehicle (V2V) connection (termed D2D-V). By considering the geographic features of D2D-V system, we propose grouping, reuse channel selection (RS), and power control (PC) framework achieve optimal performance in terms either maximized sum rate or minimally achievable rate. First, full state information (CSI) scenario, difference two convex functions (D.C.)...

Some Internet-of-Things (IoT) applications have a strict requirement on the end-to-end delay where edge computing can be used to provide short for end-users by conducing efficient caching and at nodes. However, fast communication route creation in multi-access vehicular environment is an underexplored research problem. In this paper, we propose collaborative learning-based routing scheme environment. The proposed employs reinforcement learning algorithm based end-edge-cloud collaboration...

Designing mobile edge computing (MEC) systems by jointly optimizing communication and computation resources, which can help increase batteries' lifetime improve quality of experience for computation-intensive latency-sensitive applications, has received significant interest. In this paper, we consider energy-efficient resource allocation schemes a multi-user system with inelastic tasks non-negligible task execution durations. First, establish mathematical model to characterize the offloading...

With widely deployed WiFi network and the uniqueness feature (fingerprint) of wireless channel information, fingerprinting based positioning is currently mainstream indoor method, in which fingerprint database construction crucial. However, for accuracy, this approach requires enough data to be sampled at many reference points, consumes excessive efforts time. In paper, we collect Channel State Information (CSI) points by method device-free localization, then convert collected CSI into...

Unmanned-aerial-vehicle (UAV)-enabled mobile-edge computing (MEC) has emerged as a promising paradigm to extend the coverage of computation service for Internet Things (IoT) applications, which are usually time sensitive and intensive. In this article, novel design framework is proposed multi-UAV-enabled MEC system, where edge servers equipped on multiple UAVs provide flexible assistance IoT devices with hard deadlines. The aim maximize number served through jointly optimizing UAV trajectory...

Spatial challenges for the vehicular Internet of Things come from mobility, high density, sparse connectivity, and heterogeneity. In this article, we propose two techniques, namely decentralized moving edge multi-tier multi-access clustering, to handle these challenges. The "vehicle as an edge" concept provides a more suitable solution meet throughput latency performance requirements by conducting distributed communication, data caching, computing tasks at vehicles. Multi-tier clustering...

Volumetric video (or hologram video), the medium for representing natural content in VR/AR/MR, is presumably next generation of technology and a typical use case 5G beyond wireless communications. To realize volumetric applications, efficient streaming critical demand. This article responds to challenges proposes solutions transmission systems point cloud video, which most popular favored way represent media significantly differs from other types videos. In particular, we first introduce its...

This paper investigates an air-ground integrated multi-access edge computing system, which is deployed by infrastructure provider (InP). Under a business agreement with the InP, third-party service provides services to subscribed mobile users (MUs). MUs compete for shared spectrum and resources over time achieve their distinctive goals. From perspective of MU, we deliberately define age update capture staleness information from refreshing computation outcomes. Given system dynamics, model...

Field of view (FoV) prediction is critical in 360-degree video multicast, which a key component the emerging virtual reality and augmented applications. Most current methods combining saliency detection FoV information neither take into account that distortion projected videos can invalidate weight sharing traditional convolutional networks nor do they adequately consider difficulty obtaining complete multi-user information, degrades performance. This article proposes spherical...

Gestures constitute an important form of nonverbal communication where bodily actions are used for delivering messages alone or in parallel with spoken words. Recently, there exists emerging trend WiFi sensing-enabled gesture recognition due to its inherent merits like remote sensing, non-line-of-sight covering, and privacy-friendly. However, current WiFi-based approaches mainly reply on domain-specific training since they don’t know “where look” “when look.” To this end, we propose WiGRUNT,...

Recently, Low Earth Orbit (LEO) satellite-based Internet of Things (LEO-IoT) becomes attractive for computation offloading in mobile-edge computing (MEC) since it can overcome terrain obstacles, such as depopulated villages and disaster sites. However, is extremely hard to allocate bandwidth power resources jointly with multiple users satellites. In this paper, we study collaborative LEO-IoT where satellites forward data from the MEC server, goal making fast energy efficient. To achieve...

This century has been a major avenue for revolutionary changes in technology and industry. Industries have transitioned towards intelligent automation, relying less on human intervention, resulting the fourth industrial revolution, Industry 4.0. That is why IoT researcher's arena quite some time. With 4.0 still motion, world verge of 5th relatively new concept with many unclear opinions regarding its potential benefits, challenges, opportunities, trends, impact society. There dire need...

Federated learning (FL) has gained increasing attention due to its ability collaboratively train while protecting client data privacy. However, vanilla FL cannot adapt heterogeneity, leading a degradation in training efficiency stragglers, and is still vulnerable privacy leakage. To address these issues, this paper proposes RingSFL, novel distributed scheme that integrates with model split mechanism heterogeneity maintaining In all clients form ring topology. For each client, instead of the...