Han-Chieh Chao

Big data analytics: a survey

AbstractThe age of big data is now coming. But the traditional data analytics may not be able to handle such large quantities of data. The question that arises now is, how to develop a high performance platform to efficiently analyze big data and how to design an appropriate mining algorithm to find the useful things from big data. To deeply discuss this issue, this paper begins with a brief introduction to data analytics, followed by the discussions of big data analytics. Some important open issues and further research directions will also be presented for the next step of big data analytics.

An Adaptive Mode Decision Algorithm Based on Video Texture Characteristics for HEVC Intra Prediction

The latest High Efficiency Video Coding (HEVC) standard could achieve the highest coding efficiency compared with the existing video coding standards. To improve the coding efficiency of the intra frame, a quad-tree-based variable block size coding structure that is flexible to adapt to various texture characteristics of images and up to 35 intra-prediction modes for each prediction unit (PU) is adopted in HEVC. However, the computational complexity is increased dramatically because all the possible combinations of the mode candidates are calculated in order to find the optimal rate distortion cost using the Lagrange multiplier. To alleviate the encoder computational load, this paper proposes an adaptive mode decision algorithm based on texture complexity and direction for HEVC intra prediction. First, an adaptive coding unit selection algorithm according to each depth levels’ texture complexity is presented to filter out unnecessary coding block. Then, the original redundant mode candidates for each PU are reduced according to its texture direction. The simulation results show that the proposed algorithm could reduce around 56% encoding time on average while maintaining the encoding performance efficiently with only a 1.0% increase in BD-rate compared with the test model HM16 of HEVC.

An inferential real-time falling posture reconstruction for Internet of healthcare things

This study constructs an approach to reproduce the real-time falls of humans, which uses a triaxial accelerometer and triaxial gyroscope to detect the occurrence of a fall, and an attitude algorithm to estimate the angles of each part of the human body, where Internet of healthcare things collects the information of each sensor, and a Bayesian Network deduces the next action. Inferential Bayesian probability could present more complete data of a fall to healthcare providers. Even if the data are damaged by the transmission network or equipment, the next action still could be deduced by Bayesian probability, and because the fall could be reproduced in a 3D Model on the client side, the fall occurrence is shown more intuitively, and could thus serve as reference for first aid.

Cooperative Fog Computing for Dealing with Big Data in the Internet of Vehicles: Architecture and Hierarchical Resource Management

As vehicle applications, mobile devices and the Internet of Things are growing fast, and developing an efficient architecture to deal with the big data in the Internet of Vehicles (IoV) has been an important concern for the future smart city. To overcome the inherent defect of centralized data processing in cloud computing, fog computing has been proposed by offloading computation tasks to local fog servers (LFSs). By considering factors like latency, mobility, localization, and scalability, this article proposes a regional cooperative fog-computing-based intelligent vehicular network (CFC-IoV) architecture for dealing with big IoV data in the smart city. Possible services for IoV applications are discussed, including mobility control, multi-source data acquisition, distributed computation and storage, and multi-path data transmission. A hierarchical model with intra-fog and inter-fog resource management is presented, and energy efficiency and packet dropping rates of LFSs in CFC-IoV are optimized.

Toward Belief Function-Based Cooperative Sensing for Interference Resistant Industrial Wireless Sensor Networks

In harsh and heterogeneous wireless environments, the communication reliability and latency of industrial wireless sensor networks (IWSNs) seriously suffer from both intra- and interinterference. This paper presents an interference resistant approach for IWSNs by utilizing cognitive radio techniques. To improve the interference detection performance while uploading data as little as possible, we present a new computationally efficient and effective belief function (BF) theory-based reliability-probability decision fusion rule for cooperative sensing. A factor called reliability degree is introduced to characterize the imprecision of sensor observations, and the basic belief assignments are constructed by combining this reliability degree and local detection performance. Unlike the inefficient existing BF-based fusion schemes, the proposed rule has an explicit form and it is equivalent to the well-known Chair-Vashney (CV) rule in high signal-to-noise ratio conditions. We applied the proposed rule in interference resistant IWSNs to detect and avoid interference. Both numerical results and tests results demonstrate that the proposed rule has significant improvement in detection performance, diversity gains, and throughput compared with existing BF fusion schemes and CV rule.

Big Data Analytics

The age of big data is now coming. But the traditional data analytics may not be able to handle such large quantities of data. The question that arises now is, how to develop a high performance platform to efficiently analyze big data and how to design an appropriate mining algorithm to find the useful things from big data. To deeply discuss this issue, this paper begins with a brief introduction to data analytics, followed by the discussions of big data analytics. Some important open issues and further research directions will also be presented for the next step of big data analytics.

Secure Data Storage and Searching for Industrial IoT by Integrating Fog Computing and Cloud Computing

With the fast development of industrial Internet of things (IIoT), a large amount of data is being generated continuously by different sources. Storing all the raw data in the IIoT devices locally is unwise considering that the end devices’ energy and storage spaces are strictly limited. In addition, the devices are unreliable and vulnerable to many threats because the networks may be deployed in remote and unattended areas. In this paper, we discuss the emerging challenges in the aspects of data processing, secure data storage, efficient data retrieval and dynamic data collection in IIoT. Then, we design a flexible and economical framework to solve the problems above by integrating the fog computing and cloud computing. Based on the time latency requirements, the collected data are processed and stored by the edge server or the cloud server. Specifically, all the raw data are first preprocessed by the edge server and then the time-sensitive data (e.g., control information) are used and stored locally. The non-time-sensitive data (e.g., monitored data) are transmitted to the cloud server to support data retrieval and mining in the future. A series of experiments and simulation are conducted to evaluate the performance of our scheme. The results illustrate that the proposed framework can greatly improve the efficiency and security of data storage and retrieval in IIoT.

Green alarm systems driven by emergencies in industrial wireless sensor networks

An alarm system is a fundamental application of IWSNs. Most of the existing literature focuses on reliability and quality of service of alarm systems. In addition, timeliness is another big concern for alarm systems considering the serious consequences of various emergencies. However, with the explosion of energy consumption, energy efficiency is becoming more and more important for IWSNs. Improving energy efficiency effectively without reducing other performance of alarm systems is a severe challenge. In this article, we design a novel green framework of alarm systems for the industrial field based on the IWSN. The proposed framework is composed of two parts, the IWSN and the security sector, which are connected through wireless links. The main responsibility of the IWSN is to monitor and track emergency and deliver the collected data to the security sector; the main responsibility of the security sector is to store and analyze the received data stream, and send requests to the IWSN. In IWSN, sleeping schedules and routing algorithms are designed carefully to improve the energy efficiency, reliability, and timeliness of alarm systems. Tracking emergencies and reducing redundant nodes are also necessary. The security sector stores and analyzes a data stream, and then evaluates an emergency situation. At last, the estimated emergency situation is used to trigger some corresponding actions to tackle the emergency.

A QoS Aware Resource Allocation Strategy for Mobile Graphics Rendering With Cloud Support

With the rapid development of cloud technology, many services have been transferred from local computers to the cloud-based platform, which decreases the amount of computation done on the former. The local computer could thus be developed in the direction of portability and power saving. Graphics processing, apart from providing user interfaces featuring diversified special effects, is also significant in terms of application programs and play interactions. It is exactly on the basis of the concept of graphics processing that cloud-support rendering is developed, which is aimed to improve the graphics efficiency in mobile devices, via the graphics processing units in the cloud-based platform. The cloud-based platform and the mobile devices are usually connected by the Internet; however, as remote rendering might call for greater network bandwidth, its efficiency will be compromised if the network bandwidth is not stable. Given this limitation, this paper sets out to propose a quality-of-service-aware resource allocation strategy for mobile 3D graphics rendering, which is a hybrid rendering technology combining the client-side graphics processing capabilities with the graphics processing units in the cloud-based platform. When network bandwidth is not stable, the technology is able to assess the current network bandwidth, and dynamically configure the rendered frames on the client side and cloud-based platforms. Even when the client side could not access the network, it would still be possible to carry out the drawing through the graphics processing units on the local computer. Three applications are tested in this research: the technology can increase the frame rate by an average of 44.99% when the bandwidth is 10% greater than the minimum limit, by an average of 44.57% when the bandwidth is less than the minimum limit, by an average of 30.86% when the bandwidth is 10% less than the minimum limit, and by an average of 33.74% when the bandwidth is not stable.

Learning-Based Data Envelopment Analysis for External Cloud Resource Allocation

A mature cloud system needs a complete resource allocation policy which includes internal and external allocation. They not only enable users to have better experiences, but also allows the cloud provider to cut costs. In the other words, internal and external allocation are indispensable since a combination of them is only a total solution for whole cloud system. In this paper, we clearly explain the difference between internal allocation (IA) and external allocation (EA) as well as defining the explicit IA and EA problem for the follow up research. Although many researchers have proposed resource allocation methods, they are just based on subjective observations which lead to an imbalance of the overall cloud architecture, and cloud computing resources to operate se-quentially. In order to avoid an imbalanced situation, in previous work, we proposed Data Envelopment Analysis (DEA) to solve this problem; it considers all of a user’s demands to evaluate the overall cloud parameters. However, although DEA can provide a higher quality solution, it requires more time. So we use the Q-learning and Data Envelopment Analysis (DEA) to solve the imbalance problem and reduce computing time. As our simulation results show, the proposed DEA+Qlearning will provide almost best quality but too much calculating time.