arXiv subjects

Long Hu

Publications and source records attributed to Long Hu.

46 records · Page 3Linked to original sources

arXiv2019

Smart-Edge-CoCaCo: AI-Enabled Smart Edge with Joint Computation, Caching, and Communication in Heterogeneous IoT

The development of mobile communication technology, hardware, distributed computing, and artificial intelligence (AI) technology has promoted the application of edge computing in the field of heterogeneous Internet of Things (IoT). In order to overcome the defects of the traditional cloud computing model in the era of big data. In this article, we first propose a new AIenabled smart edge with heterogeneous IoT architecture which combines edge computing, caching, and communication. Then, we propose the Smart-Edge-CoCaCo algorithm. To minimize total delay and confirm the computation offloading decision, Smart-Edge-CoCaCo uses joint optimization of the wireless communication model, the collaborative filter caching model in edge cloud, and the computation offloading model. Finally, we built an emotion interaction testbed to perform computational delay experiments in real environments. The experiment results showed that the computation delay of the Smart-Edge-CoCaCo algorithm is lower than that of traditional cloud computing model with the increase of computing task data and the number of concurrent users.

cs.NI↗

arXiv2018

Urban Healthcare Big Data System Based on Crowdsourced and Cloud-Based Air Quality Indicators

The ever-accelerating process of globalization enables more than half the population to live in cities. Thus, the air quality in cities exerts critical influence on the health status of more and more urban residents. In this article, based on urban air quality data collected through meteorological sites, mobile crowdsourcing, and IoT sensing, along with users' body signals, we propose an urban healthcare big data system named UH-BigDataSys. In this article, we first introduce a method of integrating multi-source air quality data for the data preparation of artificial-intelligence-based smart urban services. Then a testbed of UH-BigDataSys is set up with the deployment of air-quality-aware healthcare applications. Finally, we provide health guidance for urban residents in aspects of respiratory diseases, outdoor travel, sleep quality, and so on. The ultimate goal of UH-BigDataSys is for urban residents to lead healthier lives.

cs.CY↗

arXiv2018

Wearable Affective Robot

With the development of the artificial intelligence (AI), the AI applications have influenced and changed people's daily life greatly. Here, a wearable affective robot that integrates the affective robot, social robot, brain wearable, and wearable 2.0 is proposed for the first time. The proposed wearable affective robot is intended for a wide population, and we believe that it can improve the human health on the spirit level, meeting the fashion requirements at the same time. In this paper, the architecture and design of an innovative wearable affective robot, which is dubbed as Fitbot, are introduced in terms of hardware and algorithm's perspectives. In addition, the important functional component of the robot-brain wearable device is introduced from the aspect of the hardware design, EEG data acquisition and analysis, user behavior perception, and algorithm deployment, etc. Then, the EEG based cognition of user's behavior is realized. Through the continuous acquisition of the in-depth, in-breadth data, the Fitbot we present can gradually enrich user's life modeling and enable the wearable robot to recognize user's intention and further understand the behavioral motivation behind the user's emotion. The learning algorithm for the life modeling embedded in Fitbot can achieve better user's experience of affective social interaction. Finally, the application service scenarios and some challenging issues of a wearable affective robot are discussed.

cs.HC↗

arXiv2018

Label-less Learning for Traffic Control in an Edge Network

With the development of intelligent applications (e.g., self-driving, real-time emotion recognition, etc), there are higher requirements for the cloud intelligence. However, cloud intelligence depends on the multi-modal data collected by user equipments (UEs). Due to the limited capacity of network bandwidth, offloading all data generated from the UEs to the remote cloud is impractical. Thus, in this article, we consider the challenging issue of achieving a certain level of cloud intelligence while reducing network traffic. In order to solve this problem, we design a traffic control algorithm based on label-less learning on the edge cloud, which is dubbed as LLTC. By the use of the limited computing and storage resources at edge cloud, LLTC evaluates the value of data, which will be offloaded. Specifically, we first give a statement of the problem and the system architecture. Then, we design the LLTC algorithm in detail. Finally, we set up the system testbed. Experimental results show that the proposed LLTC can guarantee the required cloud intelligence while minimizing the amount of data transmission.

cs.NI↗

arXiv2018

A Dynamic Service-Migration Mechanism in Edge Cognitive Computing

Driven by the vision of edge computing and the success of rich cognitive services based on artificial intelligence, a new computing paradigm, edge cognitive computing (ECC), is a promising approach that applies cognitive computing at the edge of the network. ECC has the potential to provide the cognition of users and network environmental information, and further to provide elastic cognitive computing services to achieve a higher energy efficiency and a higher Quality of Experience (QoE) compared to edge computing. This paper firstly introduces our architecture of the ECC and then describes its design issues in detail. Moreover, we propose an ECC-based dynamic service migration mechanism to provide an insight into how cognitive computing is combined with edge computing. In order to evaluate the proposed mechanism, a practical platform for dynamic service migration is built up, where the services are migrated based on the behavioral cognition of a mobile user. The experimental results show that the proposed ECC architecture has ultra-low latency and a high user experience, while providing better service to the user, saving computing resources, and achieving a high energy efficiency.

cs.NI↗

arXiv2017

Finite-time boundary stabilization of general linear hyperbolic balance laws via Fredholm backstepping transformation $\star$

This paper is devoted to a simple and new proof on the optimal finite control time for general linear coupled hyperbolic system by using boundary feedback on one side. The feedback control law is designed by first using a Volterra transformation of the second kind and then using an invertible Fredholm transformation. Both existence and invertibility of the transformations are easily obtained.

math.OC↗

arXiv2015

Boundary exponential stabilization of 1-D inhomogeneous quasilinear hyperbolic systems

This paper deals with the problem of boundary stabilization of first-order n\times n inhomogeneous quasilinear hyperbolic systems. A backstepping method is developed. The main result supplements the previous works on how to design multi-boundary feedback controllers to realize exponential stability of the original nonlinear system in the spatial H^2 sense.

math.OC↗

arXiv2015

Stabilization and controllability of first-order integro-differential hyperbolic equations

In the present article we study the stabilization of first-order linear integro-differential hyperbolic equations. For such equations we prove that the stabilization in finite time is equivalent to the exact controllability property. The proof relies on a Fredholm transformation that maps the original system into a finite-time stable target system. The controllability assumption is used to prove the invertibility of such a transformation. Finally, using the method of moments, we show in a particular case that the controllability is reduced to the criterion of Fattorini.

math.OC↗

arXiv2015

Control of Homodirectional and General Heterodirectional Linear Coupled Hyperbolic PDEs

Research on stabilization of coupled hyperbolic PDEs has been dominated by the focus on pairs of counter-convecting ("heterodirectional") transport PDEs with distributed local coupling and with controls at one or both boundaries. A recent extension allows stabilization using only one control for a system containing an arbitrary number of coupled transport PDEs that convect at different speeds against the direction of the PDE whose boundary is actuated. In this paper we present a solution to the fully general case, in which the number of PDEs in either direction is arbitrary, and where actuation is applied on only one boundary (to all the PDEs that convect downstream from that boundary). To solve this general problem, we solve, as a special case, the problem of control of coupled "homodirectional" hyperbolic linear PDEs, where multiple transport PDEs convect in the same direction with arbitrary local coupling. Our approach is based on PDE backstepping and yields solutions to stabilization, by both full-state and observer-based output feedback, trajectory planning, and trajectory tracking problems.

math.OC↗

arXiv2014

Cross-Layer Software-Defined 5G Network

In the past few decades, the world has witnessed a rapid growth in mobile communication and reaped great benefits from it. Even though the fourth generation (4G) mobile communication system is just being deployed worldwide, proliferating mobile demands call for newer wireless communication technologies with even better performance. Consequently, the fifth generation (5G) system is already emerging in the research field. However, simply evolving the current mobile networks can hardly meet such great expectations, because over the years the infrastructures have generally become ossified, closed, and vertically constructed. Aiming to establish a new paradigm for 5G mobile networks, in this article, we propose a cross-layer software-defined 5G network architecture. By jointly considering both the network layer and the physical layer together, we establish the two software-defined programmable components, the control plane and the cloud computing pool, which enable an effective control of the mobile network from the global perspective and benefit technological innovations. Specifically, by the cross-layer design for software-defining, the logically centralized and programmable control plane abstracts the control functions from the network layer down to the physical layer, through which we achieve the fine-grained controlling of mobile network, while the cloud computing pool provides powerful computing capability to implement the baseband data processing of multiple heterogeneous networks. We discuss the main challenges of our architecture, including the fine-grained control strategies, network virtualization, and programmability. The architecture significantly benefits the convergence towards heterogeneous networks and it enables much more controllable, programmable and evolvable mobile networks.

cs.NI↗