Browsing by Author "Lin, Bao-Shuh Paul"

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A Cloud-based Outdoor Assistive Navigation System for the Blind and Visually Impaired
(2014-01-01)
Lapyko, Anna N.
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Tung, Li-Ping
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Lin, Bao-Shuh Paul
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資訊工程學系
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Department of Computer Science
Safe navigation in non-familiar outdoor environments is a challenging activity for the blind and visually impaired (BVI) people. This work introduces a cloud-based outdoor assistive navigation system (COANS) for BVI people. The main goal of the system is to provide easy street navigation and help to make outdoor walk in non-familiar environment less stressful. Hardware part from the user-side includes an android-based mobile phone and an external low-cost L1 GPS receiver to improve position accuracy. Applying the technique known as Real Time Kinematic (RTK) ameliorates the issue of the user position estimation. Interaction between the application and the user is based on voice commands (user-side) and voice notifications (system-side), together with the user-friendly "shaking" and "swiping" commands. The test scenarios for the system include traffic light status detection, zebra crossing path detection, and a bench discovering within a short distance from the user. In order to evaluate overall system performance, two groups of tests have been conducted. One is for GPS location accuracy from RTKLib and the other is for server-client communication time. It shows the usability of the proposed COANS system.
A Practical Model for Traffic Forecasting based on Big Data, Machine-learning, and Network KPIs
(2018-01-01)
Le, Luong-Vy
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Sinh, Do
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Tung, Li-Ping
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Lin, Bao-Shuh Paul
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資訊工程學系
Traffic forecasting plays an important role in improving network quality and energy saving of mobile networks. In 5G, traffic forecasting directly influences the self-organizing network (SON) in managing and controlling the network effectively. Especially, long-term traffic forecasting can provide a detailed pattern of future traffic, besides permitting more time for planning and optimizing. Most of the traffic forecasting models used the history of traffic, while the utilization of another network KPIs (key performance indicators) for traffic forecasting is limited. Therefore, the authors propose here a practical platform and process for traffic forecasting, based on big data, machine-learning (ML), and network KPIs that are flexible to forecast accurately different statistical traffic characteristics of different types of cells (GSM, 3G, 4G) for both long-and short-term forecasting. The performance of the proposed model was evaluated by applying it to a real dataset that collected KPIs of more than 6000 cells of a real network during the years, 2016 and 2017
A request control scheme for data recovery in DVB-IPDC systems with spatial and temporal packet loss
(2013-07-01)
Yang, Wen-Hsin
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Wang, You-Chiun
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Tseng, Yu-Chee
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Lin, Bao-Shuh Paul
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資訊工程學系
Recently, digital video broadcasting-handheld (DVB-H) and IP datacast over DVB-H (DVB-IPDC) have been developed to support broadcasting services. DVB-H is designed to support digital video broadcast for handheld devices, whereas DVB-IPDC can integrate with an IP-relay network to complement the data loss problem in DVB-H. Assuming that WiMAX networks are adopted to support DVB-IPDC, this paper points out two critical problems: group packet loss (GPL) and broadcast data handover (BDH). GPL occurs when there is a burst of retransmission requests for the same pieces of data with high spatial or temporal correlation. BDH happens when some devices that made the above requests handover to new serving cells. To solve these problems, we propose lazy wait and group acknowledgement schemes to alleviate duplicate requests by exploiting their spatial and temporal correlations. This not only reduces the requests submitted by neighboring devices in both space and time domains but also avoids handovering devices from sending duplicate requests in new cells. Through mathematical analysis, we show how to adaptively adjust the timers of lazy wait and group acknowledgement based on channel quality. Simulation results prove that our schemes can efficiently reduce retransmission requests and retransmission packets, thus alleviating congestion in the IP-relay network. Copyright (c) 2011 John Wiley & Sons, Ltd.
An Effective Algorithm for Interest Aware Opportunistic Advertising by Mining Social and Consuming Information
(2014-01-01)
Lin, Chia-Yu
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Jiang, Zhi-Feng
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Wang, Li-Chun
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Lin, Bao-Shuh Paul
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資訊工程學系
Advertisements and coupon forwarding among mobile devices and social platforms become significant in recent years. However, when users are in the crowded places where the network connection is unstable such as shopping malls, forwarding of advertisements to target users in limited time is challenge. Thus, we propose an interest-aware opportunistic advertising by mining social and consuming information system (ISC) and algorithm. In ISC system, we combine social relationship and consuming information to find out the consumers who have similar interest. ISC algorithm adopts the result of ISC system to reduce the delay of broadcasting advertisement and increase the advertisement effect. We also implement and evaluate the performance of ISC system and algorithm on mobile devices and the ONE simulator. Our experiment results show that ISC algorithm is cost effective and intensively magnify the advertising effeteness with many customers under uncertain network and time-limited environments.
An Energy-Efficient Scheme for WiFi-capable M2M Devices in Hybrid LTE Network
(2012)
Singh, Shubhranshu
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Huang, Kuei-Li
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Lin, Bao-Shuh Paul
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資訊工程學系
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Department of Computer Science
Machine to Machine (M2M) communication has gained much interest in the recent past, both within academia and across different industries. Power saving has become a priority issue recently due to accelerated growth of mobile devices with more and more processor power, more storage capacity, better display technology and much richer-applications. It assumes even greater significance when it comes to M2M devices since unlike hand-held mobile devices which has possibility to get re-charged after certain period of time, many of the M2M devices overall life is almost equal to its one-time charged battery life. This paper proposes an energy-efficient solution for M2M devices to minimize overall energy consumption. Simulation results based on OPNET simulator show that the proposed method significantly improves power saving, especially in time-tolerant M2M applications with long periodic report cycles.
Applying Big Data, Machine Learning, and SDN/NFV to 5G Traffic Clustering, Forecasting, and Management
(2018-01-01)
Luong-Vy Le
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Sinh, Do
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Lin, Bao-Shuh Paul
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Tung, Li-Ping
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資訊工程學系
Traffic clustering, forecasting, and management play a crucial role in improving network efficiency, network quality, load balancing (LB), and energy saving of mobile networks. Especially, in 5G networks, a dense heterogeneous architecture of various types of cells (macro cells and small cells) make traffic management become more complicated. Moreover, investigating and understanding traffic patterns of a huge number of cells are challenging issues, but valuable for network operators. On the other hand, big data, machine learning (ML), software defined network (SDN), and network functions virtualization (NFV) have recently been proposed as emerging technologies and the necessary tools for empowering the SON of 5G to address the intensive computation and optimization issues. In this study, the authors applied those technologies to build a practical and powerful framework for clustering, forecasting, and managing traffic behaviors for a huge number of base stations with different statistical traffic characteristics of different types of cells (GSM, 3G, 4G). Besides, several applications based on traffic forecasting were also introduced. Finally, the performance of the proposed models was evaluated by applying them to a real dataset that collected traffic KPIs (key performance indicators) of more than 6000 cells of a real network during the years, 2016 and 2017.
Big Data and Machine Learning Driven Handover Management and Forecasting
(2017-01-01)
Vy, Le Luong
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Tung, Li-Ping
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Lin, Bao-Shuh Paul
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材料科學與工程學系
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資訊工程學系
Handover (HO), as a key aspect of mobility management, plays an important role in improving network quality and mobility performance in mobile networks. Especially, in 5G networks, heterogeneous networks (HetNets) deployment of macro cells and small cells, and the deployment of ultra-dense networks (UDNs) make HO management become more challenging. Besides, the understanding of HO behavior in a cell is quite limited in existing studies, thus the forecasting HO for an individual cell is complicated, even impossible. This challenge led the authors to propose a practical process for managing and forecasting HO for a huge number of cells, based on machine-learning (ML) algorithms and big data. Moreover, based on HO forecasting, the authors also propose an approach to detect any abnormal HO in cells. The performance of the proposed approaches was evaluated by applying it to a real dataset that collected HO KPI of more than 6000 cells of a real network during the years, 2016 and 2017. The results show that the study was successful in identifying, separating HO behavior, forecasting the future number of HO attempts, and detecting abnormal HO behaviors of cells.
CrowdSMILE: A Crowdsourcing-based Social and Mobile Integrated System for Learning by Exploration
(2013-01-01)
Punjabi, Dennis Mohan
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Tung, Li-Ping
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Lin, Bao-Shuh Paul
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資訊工程學系
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Department of Computer Science
Lifelong Learning (LLL) can be enabled by Personal Learning Environments (PLEs), which are usually composed of a mixed set of loosely coupled tools, applications and systems. In such a mixed environment, managing learning content across the PLE can often be challenging and lead to disorganized content and as such, makes content reuse difficult while also possibly causing learners to lose content over time. In this paper, we propose an integrated system, called CrowdSMILE, using web, social and mobile technologies to provide a unified system that allows for easy organization, accessing, and reuse of learning content within a social and collaborative environment to support LLL. Content in the CrowdSMILE system is authored and organized by crowdsourcing the task to users of a Social Networking Service (SNS) platform. The learning content will then be organized according to location-based contexts so that any learner may easily find and retrieve it. Content may then be searched and accessed via an outdoor exploratory activity using a mobile phone application that employs an intuitive mobile augmented reality (MAR) user interface. The system usability of the CrowdSMILE is evaluated by applying the Computer System Usability Questionnaire (CSUQ). The results show that the proposed system received high scores throughput which indicates positive attitude and usability of the proposed CrowdSMILE system.
Deterministic Quality of Service Guarantee for Dynamic Service Chaining in Software Defined Networking
(2017-12-01)
Chen, Yu-Jia
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Wang, Li-Chun
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Lin, Feng-Yi
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Lin, Bao-Shuh Paul
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交大名義發表
In this paper, we present a systemic approach to provide deterministic delay guarantee for dynamic service chaining in software defined networking (SDN). The delay performance of service chaining in SDN is affected by signaling message exchange in control plane and packet transmissions in data plane, respectively. First, we develop an analytical method to characterize the delay performance of control plane when handling traffic with different priorities according to network calculus (NC) and queuing theory. Second, taking into account the estimated delay in control plane, we propose a novel service traversal mechanism to calculate the optimal traversal path for the service chain. We demonstrate that NC delay analysis can provide deterministic quality of service (QoS)-guaranteed service chaining for any specified delay requirements, whereas theoretical queueing delay analysis can only provide statistical QoS guarantee. In summary, the proposed NC delay analysis can help to understand the network design for a future delay sensitive Internet in which deterministic latency must be guaranteed.
Empirical Experience and Experimental Evaluation of Open5GCore over Hypervisor and Container
(2018-01-01)
Chang, Hung-Cheng
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Qiu, Bo-Jun
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Chen, Jyh-Cheng
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Tan, Tze-Jie
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Ho, Ping-Fan
One of the most important technologies for future 5G networks is to utilize Network Function Virtualization (NFV) to virtualize the network components. NFV provides flexibility, short time to market, and low cost solution to build network services, which are important features of 5G networks. Although the idea of virtualization is just being applied to cellular networks, it has been used in the community of cloud computing. There are two main virtualization techniques, hypervisor and container. In this paper, we present our practical experience of virtualizing Open5GCore, a commercial product of SDN-enabled Evolved Packet Core (EPC), over hypervisor and container. In addition to describing how to virtualize Open5GCore, we also present the experimental performance evaluation of the systems. Finally, some important lessons learned are provided.
iPEKS: Fast and Secure Cloud Data Retrieval from the Public-key Encryption with Keyword Search
(2013)
Tseng, Fu-Kuo
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Chen, Rong-Jaye
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Lin, Bao-Shuh Paul
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資訊工程學系
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Department of Computer Science
In recent years, considerable concern has arisen over the security of the data stored in the cloud. A number of studies have suggested the use of cryptographic primitives to protect the data. As these tools transform the data into an unintelligible form, secure and efficient retrieval of the encrypted data from the cloud becomes a major challenge. The public-key encryption with keyword search (PEKS) scheme and many of its variants have been proposed to respond to this challenge. However, given a large number of data (or searchable keywords) would be tested sequentially in these PEKS schemes, previous search results should be employed to improve the efficiency of future searches. In this paper, we present an interactive construction named iPEKS where the search time is linear to the total number of distinct searched keywords instead of the total number of the searchable keywords. The more the keywords have been searched previously, the better the efficiency can be improved. We provide theoretical analysis to show the security and privacy. In addition, implementation and performance experiments exhibit a great improvement in efficiency compared with the previous schemes.
On the Classification of Mobile Broadband Applications
(2016)
Hsieh, I-Ching
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Tung, Li-Ping
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Lin, Bao-Shuh Paul
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材料科學與工程學系
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資訊工程學系
In the past decade, the Internet has been widely used in everyday life. Different types of mobile broadband applications are created and require an increasing amount of network resources. However, Internet service providers must maximize the use of these limited resources to provide users with different levels of quality of service. The first step toward traffic engineering is to perform traffic classification. In this work, we propose a classification method for identifying mobile applications executed at an early stage. We first collected traffic traces from a WiFi access point and then developed a hidden Markov model based on the packet size and transmission direction of the first 20 packets. In a series of our experiments, we evaluated the number of hidden states through 10-fold cross validation, and classified six types of mobile applications. The accuracy of the proposed method achieved 99.17%. In addition, we set specific threshold values for different application models and identified 91.33% of flows in the testing data set, which comprised unknown traffic flows. These experimental results demonstrate that our proposed method is effective for classifying Internet flows as well as unknown traffic in a real network.
On Tile-and-Energy Allocation in OFDMA Broadband Wireless Networks
(2011-12-01)
Liang, Jia-Ming
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Chen, Jen-Jee
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Liu, Cheng-Wen
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Tseng, Yu-Chee
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Lin, Bao-Shuh Paul
The Orthogonal Frequency Division Multiple Access (OFDMA) technique has been widely applied in broadband wireless networks, such as IEEE 802.16 (WiMAX) and 3GPP Long Term Evolution (LTE). Existing studies have targeted at improving network throughput by increasing the transmission rates of mobile stations (MSs). Nevertheless, this is at the cost of higher energy consumption of MSs. In the letter, we consider the tile-and-energy joint allocation problem for uplink transmissions in an OFDMA wireless network. We formulate this problem as a mixed integer programming, where the objective is to minimize MSs' energy consumption subject to satisfying their traffic demands. We show this to be NP-hard and develop a heuristic taking advantage of the water-filling technique. Simulation results show that the performance of our heuristic is close to the optimum, especially when the network is under non-saturated condition.
Performance Evaluation of Open5GCore over KVM and Docker by Using Open5GMTC
(2018-01-01)
Chang, Hung-Cheng
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Qiu, Bo-Jun
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Chiu, Chen-Hao
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Chen, Jyh-Cheng
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Lin, Fuchun Joseph
Virtualizing the EPC (Evolved Packet Core) in modern carrier-grade networks provides more flexible and reliable network services by decoupling dependence on the hardware. Hypervisor and container are two popular virtualization techniques nowadays to enable virtualization of EPC. In this paper, we compared the Open5GCore, a 5G EPC prototype, over KVM and Docker, which are representatives of the hypervisor and container techniques. Moreover, to verify how each supports new services for Internet of Things (IoT) and Machine-to-Machine (M2M) communications, we also analyze the performance of two systems with the IoT and M2M traffic generated by Open5GMTC, a prototype tool for IoT/M2M platforms.
PERFORMANCE MEASUREMENTS OF TD-LTE, WIMAX AND 3G SYSTEMS
(2013-06-01)
Lin, Yi-Bing
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Lin, Pin-Jen
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Sung, Yingrong Coral
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Chen, Yuan-Kai
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Chen, Whai-En
Long Term Evolution (LTE) and Worldwide Interoperability for Microwave Access (WiMAX) are two major technologies toward next-generation mobile broadband standards, which are both expected to provide higher throughput and lower transmission latency for mobile users. This article measures and compares the latency and the throughput of Time Division - Long Term Evolution (TD-LTE), WiMAX, and third-generation (3G) systems based on several technical trials. Our quantitative measures and comparisons provide guidelines for the operators to deploy their future networks.
SDN/NFV - A new approach of deploying network infrastructure for IoT
(2018-01-01)
Do Sinh
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Le, Luong-Vy
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Lin, Bao-Shuh Paul
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Tung, Li-Ping
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資訊工程學系
The Internet of Things (IoTs) is a recent concept in which variety of smart devices such as smartphones, tablets, and sensors can interact with one another through the Internet. Hence, a plethora of data is generated and grown rapidly. They have posed various requirements of high throughput, high availability, lower latency, and high performance in heterogeneous connectivity environments. However, existing networks often struggle with such of limitations such as the complexity of control protocols and the internetworking of a huge number of smart devices. These obstacles become substantial barriers to deploy IoT services from different providers on the same network. In this context, SDN/NF V not only are considered as the key innovations in technology and novel services but also are expected to be a new model of network infrastructure that meets the requirement of developing IoT applications. In this study, the authors focus on the following contributions: i) investigate the roles of SDN/NFV in deploying IoT services and introduce an SDN/NFV architecture for deploying IoT framework; ii) build applications to slice end to -end multiple network segments to meet requirements of deploying IoT services from different providers; iii) build an application to recover an IoT service when it is out of order.
SDN/NFV, Machine Learning, and Big Data Driven Network Slicing for 5G
(2018-01-01)
Le, Luong-Vy
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Lin, Bao-Shuh Paul
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Tung, Li-Ping
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Sinh, Do
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資訊工程學系
5G networks are expected to be able to satisfy a variety of vertical services for mobile users, business demands, and automotive industry. Network slicing is a promising technology for 5G to provide a network as a service (NaaS) for a wide range of services that run on different virtual networks deployed on a shared network infrastructure. Moreover, the SON (self-organizing network) in 5G is expected as a significant evolution to guarantee for full intelligence, automatic, and faster management and optimization. To deal with those requirements, recently, software-defined networking (SDN), network functions virtualization (NFV), big data, and machine learning have been proposed as emerging technologies and the necessary tools for 5G, especially, for network slicing. This study aims to integrate various machine learning (ML) algorithms, big data, SDN, and NFV to build a comprehensive architecture and an experimental framework for the future SONs and network slicing. Finally, based on this framework, we successfully implemented an early state traffic classification and network slicing for mobile broadband traffic applications implemented at Broadband Mobile Lab (BML), National Chiao Tung University (NCTU).
UE Performance Evaluation on LTE Indoor and Outdoor Testbeds
(2013-01-01)
Hung, Jui-Tsun Jason
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Ku, Chia-Yu
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Cheng, Hsiao-Chin
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Tung, Li-Ping
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Lin, Bao-Shuh Paul
As the ever increasing demands for higher data rate, lower system latency, and high speed in mobile communications, 4G/LTE systems widely deployed provide better support for these requirements. This increased the complexity of UE (user equipment) and eNBs (evolved Node B) structures, but with simplified core networks. A more flat architecture is achieved to raise data rate and low system latency that are appreciated by users using real-time or high throughput applications such as MAR (mobile augmented reality) or video game applications. The work introduces indoor and outdoor LTE (Long Term Evolution) testbeds for UE performance evaluation via IOT (interoperability) tests and field trials. Throughput for distinct UL-DL (uplink-downlink) configurations were measured to evaluate devices' performance.
Vision-Based Road Bump Detection Using a Front-Mounted Car Camcorder
(2014-01-01)
Chen, Hua-Tsung
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Lai, Chun-Yu
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Hsu, Chun-Chieh
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Lee, Suh-Yin
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Lin, Bao-Shuh Paul
Advanced vehicle safety is a recently emerging issue, appealed from the rapidly explosive population of car owners. Increasing driver assistance systems have been designed for warning drivers of what should be noticed by analyzing surrounding environments with sensors and/or cameras. As one of the hazard road conditions, road bumps not only damage vehicles but also cause serious danger, especially at night or under poor lighting conditions. In this paper we propose a vision-based road bump detection system using a front-mounted car camcorder, which tends to be widespread deployed. First, the input video is transformed into a time-sliced image, which is a condensed video representation. Consequently, we estimate the vertical motion of the vehicle based on the time-sliced image and infer the existence of road bumps. Once a bump is detected, the location fix obtained from GPS is reported to a central server, so that the other vehicles can receive warnings when approaching the detected bumpy regions. Encouraging experimental results show that the proposed system can detect road bumps efficiently and effectively. It can be expected that traffic security will be significantly promoted through the mutually beneficial mechanism that a driver who is willing to report the bumps he/she meets can receive warnings issued from others as well.