學術出版;;Publications

Permanent URI for this communityhdl:11536/1

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.

Browse

🏛️國立陽明交通大學機構典藏

Search Results

Now showing 1 - 10 of 40

Extending IoT/M2M Scalability by Network Slicing
(2018-01-01)
de la Bastida, David
;
Lin, Fuchun Joseph
;
資訊工程學系
;
電機資訊國際碩士學位學程
;
Department of Computer Science
In this research, we have extended our initial effort in cloud-based IoT/M2M system scalability and developed a more robust solution by considering diverse QoS requirements from various IoT/M2M traffic patterns. Though our initial effort created a highly scalable architecture for IoT/M2M platforms based on OpenStack, it treated all IoT/M2M traffic without any discrepancy in the same underlying network (i.e. in the same network slice). Now, by leveraging software-defined networking in OpenStack and by using our traffic-slice optimal matching algorithm, we can direct different types of IoT traffic to feasible network slices in terms of QoS. Our experiments show that when compared with a system without network slicing, our scalability system performs better in terms of response time, power consumption, and computational cost.
UAV-Assisted Wireless Charging for Energy-Constrained IoT Devices Using Dynamic Matching
(2020-06-01)
Su, Chunxia
;
Ye, Fang
;
Wang, Li-Chun
;
Wang, Li
;
Tian, Yuan
In the emerging Internet-of-Things (IoT) paradigm, the lifetime of energy-constrained devices (ECDs) cannot be ensured due to the limited battery capacity. In this article, unmanned aerial vehicles (UAVs) are served as carriers of wireless power chargers (WPCs) to charge the ECDs. Aiming at maximizing the total amount of charging energy under the constraints of the UAVs and WPCs, a multiple-period charging process problem is formulated. To address this problem, bipartite matching with one-sided preferences is introduced to model the charging relationship between the ECDs and UAVs. Nevertheless, the traditional one-shot static matching is not suitable for this dynamic scenario, and thus the problem is further solved by the novel multiple-stage dynamic matching. Besides, the wireless charging process is history dependent since the current matching result will influence the future initial charging status, and consequently, the Markov decision process (MDP) and Bellman equation are leveraged. Then, by combining the MDP and random serial dictatorship (RSD) matching algorithm together, a four-step algorithm is proposed. In our proposed algorithm, the local MDPs for the ECDs are set up first. Next, using the RSD algorithm, all possible actions can be presented according to the current state. Then, the joint MDP is built based on the local MDPs and all the possible matching results. Finally, the Bellman equation is utilized to select the optimal branch. Finally, simulation results demonstrate the effectiveness of our proposed algorithm.
An Intelligent Elevator Development and Management System
(2020-06-01)
Van, Lan-Da
;
Lin, Yi-Bing
;
Wu, Tsung-Han
;
Lin, Yu-Chi
;
資訊工程學系
This paper proposes ElevatorTalk, an elevator development and management system based on the Internet of Things (IoT) approach called IoTtalk. This system modularizes the software into elevator components so that we can develop flexible and scalable car scheduling algorithms. ElevatorTalk consists of three subsystems: cars, scheduler, and the elevator car operating (ECO) panel. The first two subsystems are used to develop the elevator systems, and the third subsystem is used to receive requests issued by the passengers. These three subsystems work in parallel, and communicate with each other through sending and receiving messages. ElevatorTalk can connect to a real elevator system to serve as the elevator management center. It can also emulate the existing elevator systems with different car scheduling algorithms. We propose an intelligent aggressive car scheduling with initial car distribution (ACSICD) algorithm in ElevatorTalk. Our paper indicates that ACSICD has better waiting/travel/journey time performance and/or accuracy than the previous proposed algorithms. We also show that in our approach, the car scheduling decision can be quickly made with 0.2010 ms, and therefore good performance in the time complexity is achieved.
Metalworking Industry: Quality Management via Edge Computing-based Cloud Monitoring System
(2019-01-01)
Wu, Zheng-Wei
;
Lin, Shang-Chih
;
Hu, Po-Chun
;
Su, Shun-Feng
;
Huang, Yennun
This study uses the edge computing system for fast quality screening in the metal processing industry, and the Internet of Things technology is responsible for delivering data to the cloud for visualization and analysis. First, the system consists of optical components and embedded systems. Further, a fast Fourier transform is used to make the image have frequency characteristics. However, the convolution operation between the random kernel model and the image is the main means of feature extraction. In order to evaluate the performance and convergence of the proposed method, a rapid screening mechanism for good/bad products is defined. Finally, the data is passed to the cloud (ThingSpeak) platform for visualization through the MQTT protocol, and the content is subscribed to the content by the background host to perform the quality decision of the fuzzy inference system. The result is released back to the cloud. The experimental results in the industrial example show that the proposed method can accurately and quickly complete the quality inspection of surface roughness, and the feature distribution is easy to understand. At the same time, the edge computing system has the advantages of instant response and low cost, while the Internet of Things technology brings more management and analysis convenience. In future research, the unsupervised learning algorithm based on convolutional neural networks is a potential application, which can learn the quality of good or bad through a large amount of data.
RiceTalk: Rice Blast Detection Using Internet of Things and Artificial Intelligence Technologies
(2020-02-01)
Chen, Wen-Liang
;
Lin, Yi-Bing
;
Ng, Fung-Ling
;
Liu, Chun-You
;
Lin, Yun-Wei
Rice blast is one of the most serious plant diseases. Many rice blast management approaches require know-how of experienced farmers or agronomists. Monitoring the farm for disease detection is labor intensive and time consuming. By using the Internet of Things (IoT) and artificial intelligence (AI), we are able to detect plant diseases more efficiently. Existing AI and IoT studies detect plant diseases by images or nonimage hyperspectral data, which require manual operations to obtain the photographs or data for analysis. Also, image detection typically is too late as rice blast may already spread to other plants. Based on an IoT platform for soil cultivation, we develop the RiceTalk project that utilizes nonimage IoT devices to detect rice blast. Unlike the image-based plant disease detection approaches, our agriculture sensors generate nonimage data that can be automatically trained and analyzed by the AI mechanism in real time. The beauty of RiceTalk is that the AI model is treated as an IoT device and is managed like other IoT devices. In this way, our approach significantly reduces the platform management cost to provide real-time training and predictions. We also propose an innovative spore germination mechanism as a new feature extraction model for agriculture. In the current implementation, the accuracy of the RiceTalk prediction on rice blast is 89.4%.
Energy-Efficient Activation and Uplink Transmission for Cellular IoT
(2020-02-01)
Liu, Chun-Hung
;
Shen, Yu-Han
;
Lee, Chia-Han
;
電機工程學系
;
電信工程研究所
Consider a large-scale cellular network in which base stations (BSs) serve massive Internet of Things (IoT) devices. Since IoT devices are powered by a capacity-limited battery, how to prolong their working lifetime is a paramount problem for the success of cellular IoT systems. This article proposes how to use BSs to manage the active and dormant operating modes of the IoT devices via downlink signaling in an energy-efficient fashion and how the IoT devices perform energy-efficient uplink power control to improve their uplink coverage. We first investigate the fundamental statistical properties of an activation signaling process induced by BSs that would like to activate the devices in their cells, which helps to derive the neat expressions of the true, false, and total activation probabilities that reveal joint downlink power control and BS coordination is an effective means to significantly improve the activation performance. We then propose an energy-efficient uplink power control for IoT devices which is shown to save power and ameliorate the uplink coverage probability at the same time. We also propose an energy-efficient downlink power control and BS coordination scheme, which is shown to remarkably improve the activation and uplink coverage performances at the same time.
Implementing AI as Cyber IoT Devices: The House Valuation Example
(2020-04-01)
Lin, Yun-Wei
;
Lin, Yi-Bing
;
Liu, Chun-You
;
Lin, Jiun-Yi
;
Shih, Yu-Lin
Internet of Things (IoT) has been widely utilized with artificial intelligence (AI). However, it requires substantial effort to integrate AI and big data with IoT. To mitigate this problem, AItalk was proposed. By treating AI as a cyber IoT device, we do not need to write code of the AI mechanism in the network applications, as traditional AI-based IoT applications did. This article describes how the AI tools such as scikit-learn and TensorLayer are accommodated as cyber IoT devices in AItalk, and extends AItalk for non-IoT applications. We use house valuation as an example to show how AItalk can flexibly include the factors that have significant impact on the house price. We show that by adding extra features other than housing profile features, the accuracy for the prediction (valuation) can be improved by 38 & x0025;. We also investigated the communication overhead of the distributed AItalk structure, which is 3.7 & x0025; for the computation of one house price valuation.
Mobility Predictions for IoT Devices Using Gated Recurrent Unit Network
(2020-01-01)
Belay, Abebe
;
Lin, Hsin-Piao
;
Wang, Li-Chun
;
電機工程學系
;
Department of Electrical and Computer Engineering
Wireless and mobile technologies, as well as their users, are growing rapidly as the Internet of Things (IoT) products, such as sensor-network technologies, mobile devices, and supporting applications, become widely dispersed. Owing to the dynamic changes in the wireless networks and the exponential growth of the IoT products, which make it difficult to locate large quantities of users and devices, providing accurate tracking and trajectory predictions in open and highly condensed wireless networks is extremely difficult. An adaptive and scalable system is required to offer accurate location-based services (LBSs) for the success of IoT. To enhance the attainment of IoT, we propose a hybrid of principal component analysis (PCA) and gated recurrent unit (GRU) algorithms for mobility predictions in a wireless urban area. During the system development processes, we first collect an LTE signal from three unmanned aerial vehicle base stations (UAV-BSs), the Wi-Fi signal strength from each reachable Wi-Fi access points (APs), and channel information from the Wi-Fi signal media. We then apply PCA to reduce the number of Wi-Fi features and to decrease signal noise. Next, we train the GRU algorithm to develop models that can predict the mobility of IoT device users. Finally, we evaluate the tracking and trajectory models. To evaluate the proposed techniques, we compare the common parameters of the GRU with those of other deep learning types. The proposed technique provides plausible and state-of-the-art results for mobility predictions of IoT devices in a wireless environment.
Three-Dimensional Internet-of-Things Deployment With Optimal Management Service Benefits for Smart Tourism Services in Forest Recreation Parks
(2019-01-01)
Lin, Chun-Cheng
;
Liu, Wan-Yu
;
Lu, Yu-Wen
;
工業工程與管理學系
;
Department of Industrial Engineering and Management
In a large-scale national forest recreation park, it is difficult for park managers to maintain facilities and provide timely or emergency services to visitors in need (e.g., elderly adults, children, or lost visitors without communication devices). To implement smart tourism services, park managers have introduced the Internet of things (IoT) to their parks, which has enabled the provision of diversified intelligent information services. However, most of the previous studies focused on 2D deployment of the IoT system to cover all targets, ignoring actual 3D topographic differences; their objectives rarely included the effectiveness of services generated from introducing the IoT; they assumed targets to be equally crucial, regardless of differences in tourism attractiveness (e.g., tourist attractions, view trails, and recreation facilities). Therefore, this study investigates 3D deployment of an IoT system to cover targets with different scores based on tourism attractiveness in a forest recreation park with optimal management service benefits, represented as a weighted sum of service quality index (SQI) and managerial setting attributes index (MSAI), so that the system collects the data from visitors equipped with wearable devices, and applies it to tasks such as physiological detection and positioning. This problem belongs to deployment and coverage problems, which have been shown to be NP-hard, and thus is also NP-hard. Therefore, this study further solves this problem by improved simulated annealing (ISA), including three neighborhood searching operators and the dynamic probability adjustment scheme. Experimental results under various parameter settings indicate that ISA has an excellent optimization ability.
Situation Awareness and Conflict Resolution in Smart Home with Multiple Users
(2019-01-01)
Lee, Ya-Hua
;
Lin, Fuchun Joseph
;
資訊工程學系
;
Department of Computer Science
There are many research efforts on situation awareness in smart home with a single user. However, a harder problem is situation awareness when multiple users are present. We investigate the problem of recognizing multiuser activities using wearable devices in a home environment. The objective is to provide situation awareness so that a smart home can respond to the needs of its residents based on their activities. An important part of the research is to detect whether there is any service conflict resulting from the ongoing activities. If so, a conflict resolution algorithm needs to be applied so that an acceptable service by multiple users can be achieved. To verify both the activity detection and the conflict resolution algorithms, two experimental systems are constructed with different architecture alternatives to evaluate the algorithms and decide the most efficient implementation design.

學術出版;;Publications

Browse

Filters

Settings

Sort By

Results per page

🏛️國立陽明交通大學機構典藏

Search Results