Jeungeun Song

Smart Clothing: Connecting Human with Clouds and Big Data for Sustainable Health Monitoring

Traditional wearable devices have various shortcomings, such as uncomfortableness for long-term wearing, and insufficient accuracy, etc. Thus, health monitoring through traditional wearable devices is hard to be sustainable. In order to obtain healthcare big data by sustainable health monitoring, we design “Smart Clothing”, facilitating unobtrusive collection of various physiological indicators of human body. To provide pervasive intelligence for smart clothing system, mobile healthcare cloud platform is constructed by the use of mobile internet, cloud computing and big data analytics. This paper introduces design details, key technologies and practical implementation methods of smart clothing system. Typical applications powered by smart clothing and big data clouds are presented, such as medical emergency response, emotion care, disease diagnosis, and real-time tactile interaction. Especially, electrocardiograph signals collected by smart clothing are used for mood monitoring and emotion detection. Finally, we highlight some of the design challenges and open issues that still need to be addressed to make smart clothing ubiquitous for a wide range of applications.

Mobility-Aware Caching and Computation Offloading in 5G Ultra-Dense Cellular Networks

Recent trends show that Internet traffic is increasingly dominated by content, which is accompanied by the exponential growth of traffic. To cope with this phenomena, network caching is introduced to utilize the storage capacity of diverse network devices. In this paper, we first summarize four basic caching placement strategies, i.e., local caching, Device-to-Device (D2D) caching, Small cell Base Station (SBS) caching and Macrocell Base Station (MBS) caching. However, studies show that so far, much of the research has ignored the impact of user mobility. Therefore, taking the effect of the user mobility into consideration, we proposes a joint mobility-aware caching and SBS density placement scheme (MS caching). In addition, differences and relationships between caching and computation offloading are discussed. We present a design of a hybrid computation offloading and support it with experimental results, which demonstrate improved performance in terms of energy cost. Finally, we discuss the design of an incentive mechanism by considering network dynamics, differentiated user’s quality of experience (QoE) and the heterogeneity of mobile terminals in terms of caching and computing capabilities.

Software defined healthcare networks

With the increasingly serious problem of the aging population, creating an efficient and real-time health management and feedback system based on the healthcare Internet of Things (HealthIoT) is an urgent need. Specifically, wearable technology and robotics can enable a user to collect the required human signals in a comfortable way. HealthIoT is the basic infrastructure for realizing health surveillance, and should be flexible to support multiple application demands and facilitate the management of infrastructure. Therefore, enlightened by the software defined network, we put forward a smart healthcare oriented control method to software define health monitoring in order to make the network more elastic. In this article, we design a centralized controller to manage physical devices and provide an interface for data collection, transmission, and processing to develop a more flexible health surveillance application that is full of personalization. With these distinguished characteristics, various applications can coexist in the shared infrastructure, and each application can demand that the controller customize its own data collection, transmission, and processing as required, and pass the specific configuration of the physical device. This article discusses the background, advantages, and design details of the architecture proposed, which is achieved by an open-ended question and a potential solution. It opens a new research direction of HealthIoT and smart homes.

Green Video Transmission in the Mobile Cloud Networks

Video transmission is an indispensable component of most applications related to the mobile cloud networks (MCNs). However, because of the complexity of the communication environment and the limitation of resources, attempts to develop an effective solution for video transmission in the MCN face certain difficulties. In this paper, we propose a novel green video transmission (GVT) algorithm that uses video clustering and channel assignment to assist in video transmission. A video clustering model is designed based on game theory to classify the different video parts stored in mobile devices. Using the results of video clustering, the GVT algorithm provides the function of channel assignment, and its assignment process depends on the content of the video to improve channel utilization in the MCN. Extensive simulations are carried out to evaluate the GVT with several performance criteria. Our analysis and simulations show that the proposed GTV demonstrates a superior video transmission performance compared with the existing methods.

Data-Driven Computing and Caching in 5G Networks: Architecture and Delay Analysis

Recently, there has been increasing interest of deploying computation-intensive and rich-media applications on mobile devices, and ultra-low latency has become an important requirement to achieve high user QoE. However, conventional mobile communication systems are incapable of providing considerable communication and computation resources to support low latency. Although 5G is expected to effectively increase communication capacity, it is difficult to achieve ultra-low end-to-end delay for the ever growing number of cognitive applications. To address this issue, this article first proposes a novel network architecture using a resource cognitive engine and data engine. The resource cognitive intelligence, based on the learning of network contexts, is aimed at a global view of computing, caching, and communication resources in the network. The data cognitive intelligence, based on data analytics, is critical for the provisioning of personalized and smart services toward specific domains. Then we introduce an optimal caching strategy for the small-cell cloud and the macro-cell cloud. Experimental results demonstrate the effectiveness of the proposed caching strategy, and its latency is lower than that of the two conventional approaches, that is, the popular caching strategy and the greedy caching strategy.

TOLA: Topic-oriented learning assistance based on cyber-physical system and big data

Abstract Massive open online courses (MOOC) is a novel educational model emerging in recent years, which is assisted by advanced techniques such as cloud computing, big data and Cyber-Physical Systems (CPS). Through adequate analysis assisted by big data, the quality of education is expected to be extensively improved. Unfortunately, the MOOC data are not fully utilized for educational innovation, because the existing research focuses on the data generated in the online learning but neglects other related data, such as the forum data. In this article, we propose a big-data-driven approach named TOLA for online learning evolution to discover students’ learning pattern and guide courses improvement. Specifically, topic feature is extracted from MOOC forum through Latent Dirichlet Allocation, which is incorporated with other hybrid features. Through experiments, TOLA exhibits good performance in terms of complexity, efficiency and accuracy, facilitating the improvement of the quality of online education.

Smart Home 2.0: Innovative Smart Home System Powered by Botanical IoT and Emotion Detection

Advances in human-centric technologies, such as artificial intelligence (AI), application-oriented sensing and smart home, along with recent developments in internet of things (IoT) and machine-to-machine (M2M) networks are enabling the design and development of a smarter home with cognitive intelligence. In this paper, we first investigate the integration of smart home and botanical IoT for creating a better living environment to increase people’s quality of life. First, we point out that traditional smart home solution (Smart Home 1.0) only achieves the interaction between users and home appliances in indoor environment, while ignoring the relation between users and indoor greeneries. Then, we discuss the benefits of indoor greeneries for improving indoor living environment which leads a better physical health and mental health for home users. As greeneries are integrated into Smart Home 1.0, we propose an evolution from traditional smart home solution to Smart Home 2.0 to achieve the organic integration between users and greeneries. To verify our proposal, a prototype system of Smart Home 2.0 is designed and implemented. The experimental results show the smooth data flows from sensors deployed in smart green house to data center. A mobile cloud system is built to store, manage and visualize the data for the affective interaction between greeneries and home users. It is foreseeable that the emotion-aware capability of smart home 2.0 will bring more intelligent and interactive healthcare applications for urban residents in the future.

E2FS: an elastic storage system for cloud computing

In cloud storage, replication technologies are essential to fault tolerance and high availability of data. While achieving the goal of high availability, replication brings extra number of active servers to the storage system. Extra active servers mean extra power consumption and capital expenditure. Furthermore, the lack of classification of data makes replication scheme fixed at the very beginning. This paper proposes an elastic and efficient file storage called E2FS for big data applications. E2FS can dynamically scale in/out the storage system based on real-time demands of big data applications. We adopt a novel replication scheme based on data blocks, which provides a fine-grained maintenance of the data in the storage system. E2FS analyzes features of data and makes dynamic replication decision to balance the cost and performance of cloud storage. To evaluate the performance of proposed work, we implement a prototype of E2FS and compare it with HDFS. Our experiments show E2FS can outperform HDFS in elasticity while achieving guaranteed performance for big data applications.

System Design for Big Data Application in Emotion-Aware Healthcare

As the living standards improve and the health consciousness enhances, the healthcare industry has become a hot spot in nowadays society and some health monitoring systems emerge one after another in recent years. However, the mostly existing systems only focus on the logic reasoning but ignore the factor of the users emotion, which is regarded as an important factor to impact human health. In this paper, we design a system for big data application in emotion-aware healthcare (BDAEH), which pays attention to both the logic reasoning and the emotion computing. Meanwhile, the SDN the and 5G technologies are adopted in the BDAHE system to improve the resource utilization and the overall network performance of the system. The BDAEH system includes the following functions: healthcare data collection, healthcare data transmission, healthcare data storage, healthcare data analysis, and human-machine interaction. The healthcare data are generated by wearable devices or sensing-less sensors, and these healthcare data are regarded as the foundation to expand a series of data processing. The healthcare data transmission is performed through leveraging the SDN and the 5G technologies. In the data center, the related technologies based on cloud computing are utilized to store and analyze healthcare data, which obtains both the emotion and the health state of the users, and the relation between the emotion and the illness. Finally, the BDAEH system returns the analysis result to the users or the doctors for further treatment schemes or rehabilitation advice. The presented system is expected to validly improve the healthcare services by considering the emotion factor.

Wireless Fractal Ultra-Dense Cellular Networks

With the ever-growing number of mobile devices, there is an explosive expansion in mobile data services. This represents a challenge for the traditional cellular network architecture to cope with the massive wireless traffic generated by mobile media applications. To meet this challenge, research is currently focused on the introduction of a small cell base station (BS) due to its low transmit power consumption and flexibility of deployment. However, due to a complex deployment environment and low transmit power of small cell BSs, the coverage boundary of small cell BSs will not have a traditional regular shape. Therefore, in this paper, we discuss the coverage boundary of an ultra-dense small cell network and give its main features: aeolotropy of path loss fading and fractal coverage boundary. Simple performance analysis is given, including coverage probability and transmission rate, etc., based on stochastic geometry theory and fractal theory. Finally, we present an application scene and discuss challenges in the ultra-dense small cell network.