Multi-access Edge Cloud (MEC) networks are powerful for providing emerging computation-intensive and latency-sensitive applications with low latency leveraging ubiquitous edge devices. These networks enable complex applications to be split into multiple components/subtasks and deployed among multiple edge servers with limited computation and communication resources. However, multiple subtasks within an application are dependent on each other. They cannot be executed in parallel, resulting in non-trivial resource waste when allocating resources to every subtask throughout the lifetime of the application. This paper investigates the multi-component task offloading problem in MEC networks that addresses the dependencies among components and three-dimensional (3D) resource allocation, i.e., computation, communication, and time slots. The problem is NP-hard and challenging to solve due to the complex task dependencies, including triangular dependencies among multiple subtasks and the routing of edges between dependent subtasks. To address the challenge, we first propose a non-destructive task reconfiguration algorithm that transforms a task call graph into multiple sequential layers, breaking out the triangular dependency. Then, we develop a dePendency-awaRe task offloAding algorithm wIth taSk rEconfiguration (PRAISE) algorithm to maximize the total offloading benefit.PRAISEdecouples the original problem into task offloading and 3D convex resource optimization. Simulation results show thatPRAISEoutperforms baselines with higher system benefits and lower resource costs.

Multi-access edge computing (MEC) is a powerful technology that facilitates the provision of services to 6G users with ultra-low latency and high reliability, particularly in supporting artificial intelligence (AI) applications that rely on distributed machine learning (DL). However, the mobility of users poses challenges in offloading DL tasks to the MEC networks while ensuring satisfactory delay and blocking rates. Task replication emerges as a promising technique for achieving a cell-less design for mobile users. Nevertheless, existing research overlooks the replication of DL tasks involving multiple subtasks and users, as well as the high resource cost of task replication. Towards this challenge, this paper investigates the Mobility-awarE mulTi-replicA (META) DL task offloading problem in MEC networks. First, we propose a hybrid resource allocation mechanism that allocates resources to a replica with high access probability in a static manner and dynamically allocates resources to replicas with low access probabilities. Then, we develop an access base station (BS) clustering algorithm for each user to determine the optimal number of replicas. Additionally, we propose the META DL task offloading algorithms with proved approximation ratios to minimize the overall resource cost. Through simulations based on generated and real-world mobile users, we demonstrate the effectiveness of our proposed algorithms.

Cloud Computing (CC) is powerful for the computation offloading of services, promoting the implementation of various modern applications. Mobile Edge Computing (MEC) can provide low-latency services utilizing edge servers locating in proximity to users. The combination of MEC and CC can give play to the dual advantages of both. However, it is a challenging problem to offload service requests to the collaborative edge-cloud networks aiming at minimizing costs due to the resource limitation of edge servers and the online feature of services. To address this issue, we mathematically model the service requests with multiple inter-connected functions. Then, the problem of computation offloading of multi-function service requests in collaborative edge-cloud networks is formulated to be an Integer Linear Programming (ILP) and is proved to be NP-hard. Furthermore, a Cost-minimized Computation Offloading with Reconfiguration (CCOR) algorithm is proposed to minimize the total cost of online services. Finally, simulation results show that the proposed CCOR algorithm can effectively reduce the cost of computation offloading with higher resource utilization of edge cloud compared with baseline algorithms.

Knowledge distillation (KD) has enabled remarkable progress in model compression and knowledge transfer. However, KD requires a large volume of original data or their representation statistics that are not usually available in practice. Data-free KD has recently been proposed to resolve this problem, wherein teacher and student models are fed by a synthetic sample generator trained from the teacher. Nonetheless, existing data-free KD methods rely on fine-tuning of weights to balance multiple losses, and ignore the diversity of generated samples, resulting in limited accuracy and robustness. To overcome this challenge, we propose robustness and diversity seeking data-free KD (RDSKD) in this paper. The generator loss function is crafted to produce samples with high authenticity, class diversity, and inter-sample diversity. Without real data, the objectives of seeking high sample authenticity and class diversity often conflict with each other, causing frequent loss fluctuations. We mitigate this by exponentially penalizing loss increments. With MNIST, CIFAR-10, and SVHN datasets, our experiments show that RDSKD achieves higher accuracy with more robustness over different hyperparameter settings, compared to other data-free KD methods such as DAFL, MSKD, ZSKD, and DeepInversion.

Mobile edge computing (MEC) is powerful for providing services with ultra-low latency and extremely high reliability to support newly emerging applications in 5G and beyond by leveraging network function virtualization (NFV) where each mobile user requests a service function chain (SFC). To guarantee the quality of experience (QoE), e.g., reduce the downtime of moving users, effective placement of virtual network functions (VNFs) in MEC networks is critical to cope with the user mobility. Placing multiple instances of SFC for each user is promising for reducing the downtime during moving. However, an extremely high resource cost is induced. Towards this challenge, this paper investigates the mobility-aware multi-instance (MAMI) VNF placement problem in MEC networks. According to the empirical probabilities that one user stays within the coverage of different edge nodes, both static and dynamic SFC instances are placed to balance the tradeoff between the downtime and resource cost. A MAMI VNF placement algorithm is proposed for resource cost minimization, where resource sharing among the SFC instances of each user is allowed to further reduce resource cost. Simulation results based on real-world-like moving users show the effectiveness of our proposed algorithm.

Edge computing that utilizes ubiquitous edge devices locating in close proximity to users is powerful for providing Quality of Service guaranteed computation offloading services. Toward the limited resources of edge servers and wireless links, large services can be split into multiple interconnected components to be served by multiple edge servers cooperatively. The current works on service placement either assume unsplittable services or ignore the geographically isolated property of edge servers. They also ignore the interference among online services that share the same physical nodes/links in terms of executing delay. Namely, every service adds load to the placed nodes/links and every increment on load of nodes/links risks delay violation of existing services. To overcome above challenges, this article emphasizes on the interference-aware (IA) online multicomponent service placement in edge cloud networks. First, the delay of tree-like services is analyzed considering the dependency among components, based on which the IA residual capacities of physical nodes, links, and paths are defined and formulated theoretically. Furthermore, we reduce the problem of multicomponent service placement to be NP-hard and transform it into an ant colony optimization (ACO) problem to obtain the near-optimal solution. More importantly, a level traversal component ranking method and an IA dynamic pruning method are proposed for ACO to achieve faster convergence, interference awareness, and higher acceptance ratio of services. Simulation results are presented to validate the effectiveness of proposed methods. In addition, the classic artificial intelligence application of image classification is experimented to further strength the motivation of IA investigation in practical.

With the incoming era of 5G communication, the number of mobile devices is anticipated to increase dramatically. Flexible network resource allocation is required urgently to meet the mobility needs of a large number of users, accelerating the rise of network virtualization. However, the existing researches on virtual network embedding (VNE) consider less the virtual node migration caused by user mobility. In this paper, we attempt to address the problem of VNE supporting user mobility. The concepts of interruption penalty and blocking penalty are proposed to quantify the impact of virtual node mobility on infrastructure providers (InPs) and refine the revenue model of InPs. Then we propose a location-constrained 5G VNE algorithm, where a virtual node and virtual link pair embedding method is designed to increase the probability of successful VNE. Based on the proposed VNE algorithm, we further propose a virtual network re-embedding algorithm that can dynamically migrate the embedding of virtual nodes following user mobility. The virtual node migration is triggered by predicting the locations of virtual nodes and selecting the target physical nodes with the minimum number of re-embedding. Simulation results show that the proposed algorithm outperforms the existing VNE algorithms with higher InP revenue.

Fog computing-based radio access networks (F-RANs) aim to extend the computing and storage facilities of the centralized cloud radio access networks (C-RANs) to the network edge. Compared with the centralized baseband unit pool in the C-RAN, F-RAN reduces the burden on fronthaul. Thus, the F-RAN is foreseen as a viable solution towards ultra-low latency service provisioning. However, due to limited computing and storage facilities in fog computing-enabled access points (F-APs), some tasks that cannot be executed on the primary F-APs are transferred to other F-APs. In worst-case, the tasks are sent to the resource-enriched centralized cloud for processing. The transmission latency between F-APs, F-AP-to-end- user, and fronthaul latency strongly depends on interference power from the undesired network element as well as end-users. At the same time, the computational latency increases with the queuing delay. In this paper, we propose a load balancing scheme to address the tradeoff between transmission and computing latencies in F- RANs. Finally, the extensive simulation results show that the proposed scheme outperforms the greedy approach to meet the critical requirements, such as low-latency and minimal task offloading to the cloud in the F-RAN for the low-latency communications.

Besides fashion, personalization is another important factor of wearing. How to balance fashion trend and personal preference to better appreciate wearing is a non-trivial task. In previous work we develop a demo, Magic Mirror, to recommend clothing collocation based on the fashion trend. However, the diversity of people’s aesthetics is huge. In order to meet different demand, Magic Mirror is upgraded in this paper, and it can give out recommendations by considering both the fashion trend and personal preference, and work as a private clothing consultant. For more suitable recommendation, the virtual consultant will learn users’ tastes and preferences from their behaviors by using Genetic algorithm. Users can get collocations or matched top/bottom recommendation after choosing occasion and style. They can also get a report about their fashion state and aesthetic standpoint on recent wearing.

In this paper, we aim to better understand the clothing fashion styles. There remain two challenges for us: 1) how to quantitatively describe the fashion styles of various clothing, 2) how to model the subtle relationship between visual features and fashion styles, especially considering the clothing collocations. Using the words that people usually use to describe clothing fashion styles on shopping websites, we build a Fashion Semantic Space (FSS) based on Kobayashi's aesthetics theory to describe clothing fashion styles quantitatively and universally. Then we propose a novel fashion-oriented multimodal deep learning based model, Bimodal Correlative Deep Autoencoder (BCDA), to capture the internal correlation in clothing collocations. Employing the benchmark dataset we build with 32133 full-body fashion show images, we use BCDA to map the visual features to the FSS. The experiment results indicate that our model outperforms (+13% in terms of MSE) several alternative baselines, confirming that our model can better understand the clothing fashion styles. To further demonstrate the advantages of our model, we conduct some interesting case studies, including fashion trends analyses of brands, clothing collocation recommendation, etc.

In this demo, we build a practical mobile application, Moodee,to help detect and release users’ psychological stress byleveraging users’ social media data in online social networks,and provide an interactive user interface to present users’and friends’ psychological stress states in an visualized andintuitional way.Given users’ online social media data as input, Moodee intelligentlyand automatically detects users’ stress states. Moreover,Moodee would recommend users with different linksto help release their stress. The main technology of this demois a novel hybrid model - a factor graph model combinedwith Deep Neural Network, which can leverage social mediacontent and social interaction information for stress detection.We think that Moodee can be helpful to people’s mentalhealth, which is a vital problem in modern world.

Visible Light Communication (VLC) is a promising technique for high-speed, low-cost wireless services with the rapid development and wide deployment of Light-Emitting Diodes (LEDs). However, the broadcast nature of the VLC makes eavesdroppers easily intercept the light communication in various settings, e.g., offices, conference rooms and airport lobbies. Although previous work put forward physical layer mechanisms to improve VLC security, they ignored the impact of light reflection and channel correlation on VLC security, which is not practical for the indoor environment. In this paper, we propose a new eavesdropping-resilient framework to defend against eavesdropping attacks under both Single Input Single Output (SISO) and Multiple Input Single Output (MISO) models. We propose a random time reversal scheme, that makes transmitted signal automatically focus on legitimate receivers while interfering the eavesdropper's channel with VLC's multipath redundancy, time reversal, and random choice technique. We analyze the impact of channel correlation on VLC security under the MISO model and find that the system secrecy capacity decreases. Therefore, we propose to use Karhunen-LoEve (KL) transform to improve the system secrecy capacity. Finally, we conduct extensive simulations to show that, our framework can make the eavesdropper's Bit Error Rate (BER) be above 0.0038, which is the threshold of Forward Error Correction (FEC), even when he tries to attack with Constant Modulus Algorithm (CMA). Furthermore, the system secrecy capacity is improved up to 3 Bit/Sec/Hz.

Fiber-Wireless (FiWi) access network, which could provide an anytime-anywhere access for end users with high bandwidth capacity and long distance, is facing the challenge of resource allocation and optimization due to the complexity and diversity of traffic demands. Though network virtualization becomes a promising solution, which allows heterogeneous virtual networks coexisting on the shared substrate network, previous works ignored both varied requirements of Quality of Service (QoS) satisfaction of virtual networks and the flexibility of reconfiguring the resource of substrate FiWi access network. In this paper, we propose a new Virtual Network Embedding (VNE) framework based on QoS satisfaction and network reconfiguration. By equipping each virtual network with a specific QoS satisfaction requirement, the characteristics of virtual network demands are formulated from a more practical point of view. Moreover, the adaptive bandwidth allocation of substrate network and virtual network reconfiguration are exploited to maximize the InP revenue. Simulation results demonstrate that our proposed VNE algorithm outperforms previous approaches with multifold increment of InP revenue.

What should I wear? We present Magic Mirror, a virtual fashion consultant, which can parse, appreciate and recommend the wearing. Magic Mirror is designed with a large display and Kinect to simulate the real mirror and interact with users in augmented reality. Internally, Magic Mirror is a practical appreciation system for automatic aesthetics-oriented clothing analysis. Specifically, we focus on the clothing collocation rather than the single one, the style (aesthetic words) rather than the visual features. We bridge the gap between the visual features and aesthetic words of clothing collocation to enable the computer to learn appreciating the clothing collocation. Finally, both object and subject evaluations verify the effectiveness of the proposed algorithm and Magic Mirror system.

As more and more people inquire to know their hearing level condition, audiometry is becoming increasingly important. However, traditional audiometric method requires the involvement of audiometers, which are very expensive and time consuming. In this paper, we present mobile personal hearing assessment (MPHA), a novel interactive mode for testing hearing level based on mobile devices. MPHA, 1) provides a general method to calibrate sound intensity for mobile devices to guarantee the reliability and validity of the audiometry system; 2) designs an audiometric correction algorithm for the real noisy audiometric environment. The experimental results show that MPHA is reliable and valid compared with conventional audiometric assessment.

Survivability is one of the key issues in Fiber-Wireless (FiWi) broadband access network since huge data loss could be caused by single segment failure. Previous schemes focus on protecting FiWi against single segment failure by deploying backup fibers. However, these schemes suffer from two key problems. First, they ignore optimizing the selection of backup ONUs, which determines the recovery delay of the traffic interrupted by failure. Second, they underutilize the residual capacity of segments, thus require high cost of backup fibers. In this paper, aiming to tackle the inefficiency of previous schemes, we propose an efficient protection scheme, called Optimizing Backup ONUs selection and backup Fibers deployment (OBOF), to enhance the survivability of FiWi against single segment failure. Extensive experimental results demonstrate that our OBOF scheme outperforms the previous schemes significantly, especially in the scenario of higher traffic demand.