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收集所有交通大學研究人員與學生個人或團體所發表之各項學術著作，涵蓋期刊論文、教師專書、學位論文等項。;;Collections of Publications of NCTU Researchers and Students Like Journal Papers and Thesis.

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Now showing 1 - 5 of 5

Design and Implementation of Image Electronic Fence with 5G technology for Smart Farms
(2019-01-01)
Hsu, Ching-Kuo
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Chiu, Yen-Hao
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Wu, Kun-Ru
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Liang, Jia-Ming
;
Chen, Jen-Jee
The 5G era brings the rapid development of the Internet of Things (IoT). New technologies, such as image recognition, have promoted the traditional agriculture to a new milestone. Through advanced research and development of image recognition, it can intelligently monitor the growth of crops and effectively reduce agricultural damages, while avoiding crops being stolen and reducing the manpower of farms. In this paper, we design and implement an image electronic fence based on the technology of image recognition and sensor fusion with 5G technology for smart farms. By applying cameras and beacon tags, our system can identify whether the incoming/leaving people are authorized or not at the entry and exit of farms. In addition, with the high speed and low latency of 5G technology, the video data of cameras can be transmitted in a flash and fused with the beacon detection. In this way, the incoming/leaving people can be identified efficiently so as to avoid the farms being damaged. Based on field trials, we validate that the identification accuracy of our system approaches 90% in average.
Energy-efficient scheduling scheme with spatial and temporal aggregation for small and massive transmissions in LTE-M networks
(2019-01-01)
Liang, Jia-Ming
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Chang, Po-Yen
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Chen, Jen-Jee
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資訊工程學系
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Department of Computer Science
Machine-to-machine (M2M) communication is one of the key technologies to realize Internet of Things (IoT). Since IoT applications are mainly for smart sensing, such as metering, home surveillance, disaster detection, and e-health, their special sensing/uploading behaviors will result in periodic and/or event-driven small data transmissions, which may potentially decrease the radio resource efficiency. On the other hand, due to the frequent communication nature of sensing data for IoT applications, the power consumption is increasing dramatically. To reduce the power consumption of IoT devices, the 3rd Generation Partnership Project (3GPP) has defined the discontinuous reception/discontinuous transmission (DRX/DTX) mechanism to allow devices to turn off their radio interfaces and go to sleep in various patterns. However, how to optimize the DRX/DTX scheduling while improving the resource efficiency is still an open issue. In this paper, we investigate an uplink resource allocation problem over long-term evolution machine-to-machine (LTE-M) networks, which is standardized by 3GPP to improve performance on IoT. In this network, we consider the periodic, event-driven, and query-based IoT traffic while minimizing the devices' power consumption. We prove this problem to be NP-complete and propose an aggregation-efficient DRX/DTX scheduling (AEDS) scheme. This scheme takes advantage of both spatial and temporal data aggregation while applying DRX/DTX for energy saving. Specifically, the scheme consists of three phases. The first phase exploits long-term static scheduling for periodic data to ensure the latency and data rate while minimizing the devices' wake-up time. The second phase tries to decrease devices' power consumption through precisely determining their DRX/DTX configurations. Finally, the third phase employs short-term dynamic scheduling for event-driven and query-based data to improve transmission efficiency. Therefore, both small data and power consumption problems are relieved. Extensive simulation results show that the proposed scheme can improve resource efficiency, enlarge network capacity while reducing power consumption compared to the existing schemes. (C) 2018 Elsevier B.V. All rights reserved.
Spatial and Temporal Aggregation for Small and Massive Transmissions in LTE-M Networks
(2017-01-01)
Chang, Po-Yen
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Liang, Jia-Ming
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Chen, Jen-Jee
;
Wu, Kun-Ru
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Tseng, Yu-Chee
Machine-to-machine (M2M) communication is one of the key technologies to realize Internet of Things (IoT). Since IoT applications are mainly for smart sensing, such as metering, home surveillance, disaster detection, and e-health, their special sensing/uploading behaviors will result in periodic and/or eventdriven small data transmissions, which may potentially decrease the radio resource efficiency. On the other hand, the widespread deployment of IoT raises the concurrent massive connectivity of IoT devices. How to solve these two problems is a critical issue. In this paper, we investigate an uplink resource allocation problem which considers the periodic, event-driven, and query-based IoT traffic behaviors over LTE-M. The proposed approach takes advantage of data aggregation and both spatial and temporal reuse. Our solution exploits long-term static scheduling for periodic data to ensure the latency and data rate, and employs short-term dynamic scheduling for event-driven, query-based data to improve transmission efficiency. Therefore, both small data and massive connectivity problems are relieved. Extensive simulation results show that the proposed scheme can improve resource efficiency and enlarge network capacity effectively.
Aggregating Small Packets in M2M Networks: An OM2M Implementation
(2016-01-01)
Lee, Sheng-Chieh
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Wu, Kun-Ru
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Hsu, Ching-Kuo
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Chang, Po-Yen
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Liang, Jia-Ming
IoT (Internet of Things) and M2M (machine to machine) have attracted a lot of attention since more and more devices are expected to connect to the Internet for special purposes such as environment monitoring, home automation, industrial surveillance, and e-Health care. Currently, the OM2M (Open source platform for M2M communication) is a promising project which implements oneM2M and SmartM2M standards as an open-source platform for integrating various M2M services, applications, and devices. However, the individual data generated from those IoT/M2M devices is usually quite small, which incurs a lot of control overhead and thus decreases network performance significantly. Therefore, in this work we design and implement an OM2M 'plugin' that can aggregate small IoT data effectively. We will show how the plugin works and verify the effectiveness on the network bandwidth in this demonstration.
Energy-Efficient Uplink Radio Resource Management in LTE-Advanced Relay Networks for Internet of Things
(2014-01-01)
Chen, Jen-Jee
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Liang, Jia-Ming
;
Chen, Zeng-Yu
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資訊工程學系
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Department of Computer Science
For M2M (Machine-to-Machine) machines in cellular networks, employing high transmission rates or transmitting in large power actually cost them much energy. This is harmful to the machines, especially they are operated by batteries. The Relay Node (RN) in Long-Term Evolution-Advanced (LTE-A) networks is used to enhance the coverage of high data rate and solve the coverage hole problem. Considering the limited energy nature of machines, connecting to the RN instead of the BS is a better choice for cell-edge machines. In this paper, we consider an uplink resource and power allocation problem for energy conservation in LTE-A relay networks. The objective is to minimize the total energy consumption of machines while guarantee their quality of service (QoS). We prove this uplink resource and power allocation problem to be NP-complete and develop an energy-conserved resource and power allocation method to solve the problem. Simulation results show that our algorithm can effectively reduce the energy consumption of machines and guarantee their required service qualities.

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