Browsing by Author "Yang, Shun-Ren"

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DEPLOYING THE FIRST PSTN-BASED IOT MECHANISM
(2018-12-01)
Lin, Yi-Bing
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Lin, Rong-Syh
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Chen, Yuan-Kai
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Twu, Chin-Ywu
;
Yang, Shun-Ren
Existing IoT services are based on data communications technologies that do not involve the public switched telephone network (PSTN). Since the telephone numbers have been assigned to machine-type devices, PSTN switches can play a role in IoT service routing. In this article we deploy a PSTN-based IoT mechanism where the interaction between the users and the IoT devices is achieved through PSTN switches. To our knowledge, this is the first PSTN-based IoT solution in the world. With this mechanism, all PSTN customer premises equipment (CPE; fixed-line and mobile phones) can access IoT services without installing any software (mobile apps). By reusing the existing PSTN infrastructure, PSTN-based IoT offers telecom-grade service, security, and network management for IoT, which are very expensive to build in non-PSTN-based IoT. Our approach conveniently enables the existing CPE to access IoT applications, which will significantly promote the IoT service industry.
Modeling UMTS power saving with bursty packet data traffic
(2007-12-01)
Yang, Shun-Ren
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Yan, Sheng-Ying
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Hung, Hui-Nien
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統計學研究所
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Institute of Statistics
The universal mobile telecommunications system ( UMTS) utilizes the discontinuous reception ( DRX) mechanism to reduce the power consumption of mobile stations ( MSs). DRX permits an idle MS to power off the radio receiver for a predefined sleep period and then wake up to receive the next paging message. The sleep/ wake- up scheduling of each MS is determined by two DRX parameters: the inactivity timer threshold and the DRX cycle. In the literature, analytic and simulation models have been developed to study the DRX performance mainly for Poisson traffic. In this paper, we propose a novel semi- Markov process to model the UMTS DRX with bursty packet data traffic. The analytic results are validated against simulation experiments. We investigate the effects of the two DRX parameters on output measures including the power saving factor and the mean packet waiting time. Our study provides inactivity timer and DRX cycle value selection guidelines for various packet traffic patterns.
Performance of mobile telecommunications network with overlapping location area configuration
(2008-03-01)
Yang, Shun-Ren
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Lin, Yung-Chun
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Lin, Yi-Bing
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資訊工程學系
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Department of Computer Science
In a mobile telecommunications network, the location area (LA) of a mobile station (MS) is tracked by an LA update (LAU) mechanism. To reduce the LAU traffic caused by the ping-pong effect, the overlapping LA concept is introduced. In the overlapping LA configuration, an LA selection policy is required to select the new LA at an LAU when the MS enters a new cell covered by multiple LAs. This paper describes four LA selection policies and proposes an analytic model to study the performance of these LA selection policies. Our study provides guidelines to determine an appropriate degree of overlapping among the LAs.
Short-Term Traffic Prediction for Edge Computing-Enhanced Autonomous and Connected Cars
(2019-04-01)
Yang, Shun-Ren
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Su, Yu-Ju
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Chang, Yao-Yuan
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Hung, Hui-Nien
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統計學研究所
Autonomous and connected cars (ACCs), together with edge computing (EC), have been recognized as a promising solution to achieve green intelligent transportation for smart cities. This paper aims to address short-term traffic prediction, a fundamental enabler for the success of ACC applications, under the European Telecommunications Standards Institute multiaccess EC (MEC) architecture that exhibits constraints different from conventional cloud computing. First, a data-centric experiment platform is designed and implemented to facilitate traffic prediction algorithm development. This paper further proposes a novel short-term traffic prediction model that integrates a traffic light model and a vehicle velocity model, considering limited computing resources of MEC servers. We note that the effects of traffic lights are complicated and have not been rigorously examined in most, if not all, of the related work. This work models the queueing time when a driver arrives at a road intersection and faces a red light. Moreover, to forecast the vehicle velocity, we propose a novel low-complexity semiparametric prediction model considering periodic features and spatial/temporal correlations of dynamic road events. The experiment results demonstrate that our vehicle-velocity prediction model achieves almost equivalent accuracy to the well-known Long Short-Term Memory Neural Network model, requiring much lower computational complexity. Our experiment results also confirm that since our integrated model pays attention to the traffic-light effects and the individual driver behavior, it can more effectively capture the real-time changes of traffic conditions.