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Fan-Hsun Tseng received the M.S. degree in Computer Science and Information Engineering from the National Ilan University, I-Lan, Taiwan, in 2010, and the Ph.D. degree in Computer Science and Information Engineering from the National Central University, Taoyuan, Taiwan, in 2016. From 2018 to 2021, he joined the faculty of the Department of Technology Application and Human Resource Development, National Taiwan Normal University, Taipei, Taiwan. In 2021, he joined the faculty of the Department of Computer Science and Information Engineering, National Cheng Kung University, Tainan, Taiwan, where he is currently an Assistant Professor. Dr. Tseng receives the 2018 MOST Young Scholar Fellowship for his dedication to research of Engineering and Technologies. He has served as Associate Editor-in-Chief of Journal of Computers, Associate Editor of IEEE Access, Human-centric Computing and Information Sciences, Journal of Internet Technology, and IET Networks, and Topic Editor of Sensors and Electronics. His research interests include Mobile Networks, Cloud and Fog Computing, IoT and Big Data, Artificial Intelligence, Machine Learning and Evolutionary Computing.
The sustainable utilization of marine resources is a vital issue to enrich marine life and to prevent species extinction caused by overfishing. Nowadays, it is common that commercial and smaller vessels are equipped with an Automatic Identification System (AIS) and GPS for better vessel tracking to avoid vessel collision as well as mayday calls. Additionally, governments can monitor vessels’ sea activities through AIS messages, stopping them from overfishing or tracking if any vessel has caused marine pollution. However, because AIS devices cannot guarantee data security, they are susceptible to malicious attacks such as message modification or an illegitimate identity faking a distress signal that causes other vessels to change their course. Given the above, a comprehensive network security system of a sustainable marine environment should be proposed to ensure secure communication. In this paper, a stationary IoT-enabled (Internet of Things) vessel tracking system of a sustainable marine environment is proposed. The system combines network security, edge computing, and tracking management. It offers the following functions: (1) The IoT-based vessel tracking system tracks each aquafarmer’s farming zone and issues periodic warning to prevent vessel collision for pursuing a sustainable marine environment; (2) the system can serve as a relay station that evaluates whether a vessel’s AIS data is correct; (3) the system detects abnormal behavior and any irregular information to law enforcement; (4) the system’s network security mechanism adopts a group key approach to ensure secure communication between vessels; and (5) the proposed edge computing mechanism enables the tracking system to perform message authentication and analysis, and to reduce computational burden for the remote or cloud server. Experiment results indicate that our proposed system is feasible, secure, and sustainable for the marine environment, and the tendered network security mechanism can reduce the computational burden while still ensuring security.
Han-Chieh Chao; Hsin-Te Wu; Fan-Hsun Tseng. AIS Meets IoT: A Network Security Mechanism of Sustainable Marine Resource Based on Edge Computing. Sustainability 2021, 13, 3048 .
AMA StyleHan-Chieh Chao, Hsin-Te Wu, Fan-Hsun Tseng. AIS Meets IoT: A Network Security Mechanism of Sustainable Marine Resource Based on Edge Computing. Sustainability. 2021; 13 (6):3048.
Chicago/Turabian StyleHan-Chieh Chao; Hsin-Te Wu; Fan-Hsun Tseng. 2021. "AIS Meets IoT: A Network Security Mechanism of Sustainable Marine Resource Based on Edge Computing." Sustainability 13, no. 6: 3048.
In order to provide a more flexible wireless rechargeable sensor network, a charger and a self-propelled vehicle are integrated into one vehicle in recent years. The path selection problem of mobile chargers can be formulated as the well-known travelling salesman problem. Therefore, metaheuristic algorithms can be applied to solve the planning problem of mobile chargers. Some researches presented planning methods based on the Simulated Annealing (SA) and Tabu Search (TS) algorithms but the results are not satisfied. In this paper, we not only design a novel encoding approach but also the fitness function for proposing an efficient planning algorithm based on the Ant Colony Optimization (ACO) algorithm. Simulation results show that the proposed ACO-based algorithm achieves a shorter planning path for a longer network lifetime compared with that generated by the SA and TS algorithms.
Fan-Hsun Tseng; Hsin-Hung Cho; Chin-Feng Lai. Mobile Charger Planning for Wireless Rechargeable Sensor Network Based on Ant Colony Optimization. Lecture Notes in Electrical Engineering 2021, 387 -394.
AMA StyleFan-Hsun Tseng, Hsin-Hung Cho, Chin-Feng Lai. Mobile Charger Planning for Wireless Rechargeable Sensor Network Based on Ant Colony Optimization. Lecture Notes in Electrical Engineering. 2021; ():387-394.
Chicago/Turabian StyleFan-Hsun Tseng; Hsin-Hung Cho; Chin-Feng Lai. 2021. "Mobile Charger Planning for Wireless Rechargeable Sensor Network Based on Ant Colony Optimization." Lecture Notes in Electrical Engineering , no. : 387-394.
Wireless rechargeable sensor network was proposed to extend the lifetime of wireless sensor network. In this paper, a charger is combined together with a self-propelled vehicle to provide a more flexible result of charger deployment. The dynamic chargers path selection problem is defined and mapped into the traveling salesman problem. Four metaheuristic algorithms for Internet of Things (IoT) applications are designed, and the higher fitness value between the charging path and the number of dead IoT device is achieved. However, metaheuristic approaches may spend more time on searching solutions so that many IoT devices overuse limited power and fail to be charged for a long time, leading to power exhaustion. In this paper, edge computing technique is applied to accelerate the obtainment of charging paths with the well-defined edge/centralized unit switching. Moreover, to assure the calculated path trustworthy and will not be tampered with, the blockchain technology is adopted. The proposed architecture maintains a high-level information credibility while transmitting the information of charging paths within cloud and edge. The simulation results showed that the proposed method is capable of achieving better charging efficiency and less deployment cost.
Hsin-Hung Cho; Hsin-Te Wu; Chin-Feng Lai; Timothy K. Shih; Fan-Hsun Tseng. Intelligent Charging Path Planning for IoT Network Over Blockchain-Based Edge Architecture. IEEE Internet of Things Journal 2020, 8, 2379 -2394.
AMA StyleHsin-Hung Cho, Hsin-Te Wu, Chin-Feng Lai, Timothy K. Shih, Fan-Hsun Tseng. Intelligent Charging Path Planning for IoT Network Over Blockchain-Based Edge Architecture. IEEE Internet of Things Journal. 2020; 8 (4):2379-2394.
Chicago/Turabian StyleHsin-Hung Cho; Hsin-Te Wu; Chin-Feng Lai; Timothy K. Shih; Fan-Hsun Tseng. 2020. "Intelligent Charging Path Planning for IoT Network Over Blockchain-Based Edge Architecture." IEEE Internet of Things Journal 8, no. 4: 2379-2394.
Artificial intelligence and deep learning techniques are all around our life. Image recognition and natural language processing are the two major topics. Through using TensorFlow-GPU as backend in convolutional neural network (CNN) and deep learning network, image recognition has been an extreme breakthrough in recent years. However, more and more model parameters result in overfitting problem and computation overhead. In the paper, the performance of image recognition between standard CNN and depthwise separable CNN is experimented and investigated. In addition, data augmentation technique is applied to both standard and depthwise separable CNNs to improve the image recognition accuracy. The experiments are implemented by an open source API called Keras with using CIFAR-10 dataset. Experimental results showed that the depthwise separable CNN improves validation accuracy compared with the standard CNN. Moreover, schemes with data augmentation achieve higher validation accuracy but training accuracy.
Fan-Hsun Tseng; Fan-Yi Kao. A Study of Image Recognition for Standard Convolution and Depthwise Separable Convolution. Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering 2020, 189 -198.
AMA StyleFan-Hsun Tseng, Fan-Yi Kao. A Study of Image Recognition for Standard Convolution and Depthwise Separable Convolution. Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering. 2020; ():189-198.
Chicago/Turabian StyleFan-Hsun Tseng; Fan-Yi Kao. 2020. "A Study of Image Recognition for Standard Convolution and Depthwise Separable Convolution." Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering , no. : 189-198.
The aims of this study are to examine the effect of crowdsourced employer ratings and employee recommendations of an employer as an employer of choice, to examine which employer ratings that represent different employee value propositions can predict the overall employer rating through crowdsourcing, to examine whether the Fortune 500 ranking can also influence overall employer ratings, and to mine which keywords are popularly used when employees post a comment about the pros and cons of their employers on a crowdsourced employer branding platform. The study collected crowdsourced employer review data from Glassdoor based on 2019 Fortune 500 companies, and the results found that crowdsourced employer ratings are positively associated with “recommend to a friend,” while culture and values predominantly influence overall employer ratings. The rank of Fortune 500 has less predictive power for overall employer ratings than for other specific employer ratings, except for business outlook. The most popular keywords of Pros on Glassdoor are work–life balance and pay and benefits, whereas the most popular keywords of Cons on Glassdoor are work–life balance and upper management.
Hung-Yue Suen; Kuo-En Hung; Fan-Hsun Tseng. Employer Ratings through Crowdsourcing on Social Media: An Examination of U.S. Fortune 500 Companies. Sustainability 2020, 12, 6308 .
AMA StyleHung-Yue Suen, Kuo-En Hung, Fan-Hsun Tseng. Employer Ratings through Crowdsourcing on Social Media: An Examination of U.S. Fortune 500 Companies. Sustainability. 2020; 12 (16):6308.
Chicago/Turabian StyleHung-Yue Suen; Kuo-En Hung; Fan-Hsun Tseng. 2020. "Employer Ratings through Crowdsourcing on Social Media: An Examination of U.S. Fortune 500 Companies." Sustainability 12, no. 16: 6308.
In recent years, the transmission rate of mobile network becomes insufficient to serve numerous mobile users. Relay technique has been proposed to improve the data rate of mobile networks for many years. In the paper, the planning problem of heterogeneous cellular network is defined and limited in two-hop relaying. The defined problem aims to tackle with three objective functions at the same time. A meta-heuristic planning algorithm is proposed based on Ant Colony Optimization (ACO) algorithm. The proposed ACO-based algorithm optimizes the placement results of macrocells, microcells and femtocells. In the simulation-based result and analysis, the ACO-based algorithm yields the higher capacity and more covered users with the lowest construction cost compared to the two heuristic algorithms, i.e., Top-Down and Bottom-Up algorithms.
Fan-Hsun Tseng; Fan-Yi Kao; Tsung-Ta Liang; Han-Chieh Chao. Ant Colony Optimization Algorithm for Network Planning in Heterogeneous Cellular Networks. Advances in Intelligent Systems and Computing 2020, 11 -19.
AMA StyleFan-Hsun Tseng, Fan-Yi Kao, Tsung-Ta Liang, Han-Chieh Chao. Ant Colony Optimization Algorithm for Network Planning in Heterogeneous Cellular Networks. Advances in Intelligent Systems and Computing. 2020; ():11-19.
Chicago/Turabian StyleFan-Hsun Tseng; Fan-Yi Kao; Tsung-Ta Liang; Han-Chieh Chao. 2020. "Ant Colony Optimization Algorithm for Network Planning in Heterogeneous Cellular Networks." Advances in Intelligent Systems and Computing , no. : 11-19.
Content cache as well as data cache is vital to Content Centric Network (CCN). A sophisticated cache scheme is necessary but unsatisfied currently. Existing content cache scheme wastes router’s cache capacity due to redundant replica data in CCN routers. The paper presents an intelligent data cache scheme, viz content popularity and user location (CPUL) scheme. It tackles the cache problem of CCN routers for pursuing better hit rate and storage utilization. The proposed CPUL scheme not only considers the location where user sends request but also classifies data into popular and normal content with correspond to different cache policies. Simulation results showed that the CPUL scheme yields the highest cache hit rate and the lowest total size of cache data with compared to the original cache scheme in CCN and the Most Popular Content (MPC) scheme. The CPUL scheme is superior to both compared schemes in terms of around 8% to 13% higher hit rate and around 4% to 16% lower cache size. In addition, the CPUL scheme achieves more than 20% and 10% higher cache utilization when the released cache size increases and the categories of requested data increases, respectively.
Hsin-Te Wu; Hsin-Hung Cho; Sheng-Jie Wang; Fan-Hsun Tseng. Intelligent data cache based on content popularity and user location for Content Centric Networks. Human-centric Computing and Information Sciences 2019, 9, 1 -16.
AMA StyleHsin-Te Wu, Hsin-Hung Cho, Sheng-Jie Wang, Fan-Hsun Tseng. Intelligent data cache based on content popularity and user location for Content Centric Networks. Human-centric Computing and Information Sciences. 2019; 9 (1):1-16.
Chicago/Turabian StyleHsin-Te Wu; Hsin-Hung Cho; Sheng-Jie Wang; Fan-Hsun Tseng. 2019. "Intelligent data cache based on content popularity and user location for Content Centric Networks." Human-centric Computing and Information Sciences 9, no. 1: 1-16.
By collecting and analyzing a vast quantity and different categories of information, traffic flow and road congestion can be predicted and avoided in intelligent transportation system. However, how to tackle with these big data is vital but challenging. Most of the existing literatures utilized batch method to process a bunch of road data that cannot achieve real-time traffic prediction. In this paper, we use the spouts and bolts in Apache Storm to implement a real-time traffic prediction model by analyzing enormous streaming data, such as road density, traffic events, and rainfall volume. The proposed SVM-based real-time highway traffic congestion prediction (SRHTCP) model collects the road data from the Taiwan Area National Freeway Bureau, the traffic events reported by road users from the Police Broadcasting Service in Taiwan, and the weather data from the Central Weather Bureau in Taiwan. We use fuzzy theory to evaluate the traffic level of road section in real time with considering road speed, road density, road traffic volume, and the rainfall of road sections. In addition, the SRHTCP model predicts the road speed of next time period by exploring streaming traffic and weather data. Results showed that the proposed SRHTCP model improves 25.6% prediction accuracy than the prediction method based on weighted exponential moving average method under the measurement of mean absolute relative error.
Fan-Hsun Tseng; Jen-Hao Hsueh; Chia-Wei Tseng; Yao-Tsung Yang; Han-Chieh Chao; Li-Der Chou. Congestion Prediction With Big Data for Real-Time Highway Traffic. IEEE Access 2018, 6, 57311 -57323.
AMA StyleFan-Hsun Tseng, Jen-Hao Hsueh, Chia-Wei Tseng, Yao-Tsung Yang, Han-Chieh Chao, Li-Der Chou. Congestion Prediction With Big Data for Real-Time Highway Traffic. IEEE Access. 2018; 6 ():57311-57323.
Chicago/Turabian StyleFan-Hsun Tseng; Jen-Hao Hsueh; Chia-Wei Tseng; Yao-Tsung Yang; Han-Chieh Chao; Li-Der Chou. 2018. "Congestion Prediction With Big Data for Real-Time Highway Traffic." IEEE Access 6, no. : 57311-57323.
The demand for satisfying service requests, effectively allocating computing resources, and providing service on-demand application continuously increases along with the rapid development of the Internet. Edge computing is used to satisfy the low latency, network connection, and local data processing requirements and to alleviate the workload in the cloud. This paper proposes a gateway-based edge computing service model to reduce the latency of data transmission and the network bandwidth from and to the cloud. An on-demand computing resource allocation can be achieved by adjusting the task schedule of the edge gateway via the lightweight virtualization technology, Docker. The edge gateway can also process the service requests in the local network. The proposed edge computing service model not only eliminates the computation burden of the traditional cloud service model but also improves the operation efficiency of the edge computing nodes. This model can also be used for various innovation applications in the cloud-edge computing environment for 5G and beyond.
Chia-Wei Tseng; Fan-Hsun Tseng; Yao-Tsung Yang; Chien-Chang Liu; Li-Der Chou. Task Scheduling for Edge Computing with Agile VNFs On-Demand Service Model toward 5G and Beyond. Wireless Communications and Mobile Computing 2018, 2018, 1 -13.
AMA StyleChia-Wei Tseng, Fan-Hsun Tseng, Yao-Tsung Yang, Chien-Chang Liu, Li-Der Chou. Task Scheduling for Edge Computing with Agile VNFs On-Demand Service Model toward 5G and Beyond. Wireless Communications and Mobile Computing. 2018; 2018 ():1-13.
Chicago/Turabian StyleChia-Wei Tseng; Fan-Hsun Tseng; Yao-Tsung Yang; Chien-Chang Liu; Li-Der Chou. 2018. "Task Scheduling for Edge Computing with Agile VNFs On-Demand Service Model toward 5G and Beyond." Wireless Communications and Mobile Computing 2018, no. : 1-13.
The trend of 5G mobile networks is increasing with the number of users and the transmission rate. Many operators are turning to small cell and indoor coverage of telecom network service. With the emerging Software Defined Networking and Network Function Virtualization technologies, Internet Service Provider is able to deploy their networks more flexibly and dynamically. In addition to the change of the wireless mobile network deployment model, it also drives the development trend of the Micro Operator (μO). Telecom operators can provide regional network services through public buildings, shopping malls, or industrial sites. In addition, localized network services are provided and bandwidth consumption is reduced. The distributed architecture ofμO tackles computing requirements for applications, data, and services from cloud data center to edge network devices or to the micro data center ofμO. The service model ofμO is capable of reducing network latency in response to the low-latency applications for future 5G edge computing environment. This paper addresses the design pattern of 5G micro operator and proposes a Decision Tree Based Flow Redirection (DTBFR) mechanism to redirect the traffic flows to neighbor service nodes. The DTBFR mechanism allows differentμOs to share network resources and speed up the development of edge computing in the future.
Chia-Wei Tseng; Yu-Kai Huang; Fan-Hsun Tseng; Yao-Tsung Yang; Chien-Chang Liu; Li-Der Chou. Micro Operator Design Pattern in 5G SDN/NFV Network. Wireless Communications and Mobile Computing 2018, 2018, 1 -14.
AMA StyleChia-Wei Tseng, Yu-Kai Huang, Fan-Hsun Tseng, Yao-Tsung Yang, Chien-Chang Liu, Li-Der Chou. Micro Operator Design Pattern in 5G SDN/NFV Network. Wireless Communications and Mobile Computing. 2018; 2018 ():1-14.
Chicago/Turabian StyleChia-Wei Tseng; Yu-Kai Huang; Fan-Hsun Tseng; Yao-Tsung Yang; Chien-Chang Liu; Li-Der Chou. 2018. "Micro Operator Design Pattern in 5G SDN/NFV Network." Wireless Communications and Mobile Computing 2018, no. : 1-14.
In order to optimize the resource utilization of physical machines (PMs), the workload prediction of virtual machines (VMs) is vital but challenging. Most of existing literatures focus on either resource prediction or allocation individually, but both of them are highly correlated. In this paper, we propose a multiobjective genetic algorithm (GA) to dynamically forecast the resource utilization and energy consumption in cloud data center. We formulate a multiobjective optimization problem of resource allocation, which considers the CPU and memory utilization of VMs and PMs, and the energy consumption of data center. The proposed GA forecasts the resource requirement of next time slot according to the historical data in previous time slots. We further propose a VM placement algorithm to allocate VMs for next time slot based on the prediction results of GA. In our simulation-based analysis, the optimal solution for resource prediction under stable and unstable utilization tendency is found by the proposed GA. The prediction result is superior to the previous proposed Grey forecasting model. Results show that the proposed VM placement algorithm not only increases the average utilization level of CPU and memory but also decreases the energy consumption of cloud data center.
Fan-Hsun Tseng; Xiaofei Wang; Li-Der Chou; Han-Chieh Chao; Victor C. M. Leung. Dynamic Resource Prediction and Allocation for Cloud Data Center Using the Multiobjective Genetic Algorithm. IEEE Systems Journal 2017, 12, 1688 -1699.
AMA StyleFan-Hsun Tseng, Xiaofei Wang, Li-Der Chou, Han-Chieh Chao, Victor C. M. Leung. Dynamic Resource Prediction and Allocation for Cloud Data Center Using the Multiobjective Genetic Algorithm. IEEE Systems Journal. 2017; 12 (2):1688-1699.
Chicago/Turabian StyleFan-Hsun Tseng; Xiaofei Wang; Li-Der Chou; Han-Chieh Chao; Victor C. M. Leung. 2017. "Dynamic Resource Prediction and Allocation for Cloud Data Center Using the Multiobjective Genetic Algorithm." IEEE Systems Journal 12, no. 2: 1688-1699.
Data center benefits cloud applications in providing high scalability and ensuring service availability. However, virtual machine (VM) placement in data center poses new challenges for service provisioning. For many cloud services such as storage and video streaming, present placement approaches are unable to support network-demanding services due to overwhelming communication traffic and time. Therefore VM placement concerning link capacity is vital to cloud data centers. In this paper, we define the network-aware VM placement optimization (NAVMPO) problem based on integer linear programming. The objective function of NAVMPO problem aims to minimize communication time for VMs of the same service type. Then we propose the service-oriented physical machine (PM) selection (SOPMS) algorithm and link-aware VM placement (LAVMP) algorithm. The SOPMS algorithm selects the most appropriate PM based on service-oriented architecture, and then the LAVMP algorithm deploys the most suitable VM to target PM regarding to the link capacity between them. Simulation results show that the proposed placement approach significantly decreases communication time compared to existing non-service-oriented and service-oriented VM placement algorithms, and also improves the average utility rate of PMs with lower power consumption
Fan-Hsun Tseng; Yong-Ming Jheng; Li-Der Chou; Han-Chieh Chao; Victor C.M. Leung. Link-Aware Virtual Machine Placement for Cloud Services based on Service-Oriented Architecture. IEEE Transactions on Cloud Computing 2017, 8, 989 -1002.
AMA StyleFan-Hsun Tseng, Yong-Ming Jheng, Li-Der Chou, Han-Chieh Chao, Victor C.M. Leung. Link-Aware Virtual Machine Placement for Cloud Services based on Service-Oriented Architecture. IEEE Transactions on Cloud Computing. 2017; 8 (4):989-1002.
Chicago/Turabian StyleFan-Hsun Tseng; Yong-Ming Jheng; Li-Der Chou; Han-Chieh Chao; Victor C.M. Leung. 2017. "Link-Aware Virtual Machine Placement for Cloud Services based on Service-Oriented Architecture." IEEE Transactions on Cloud Computing 8, no. 4: 989-1002.
Cloud computing provides the scalable computation capability based on a virtualization technique. The energy conservation for green computing is one of the vital issues while allocating resources. To improve energy efficiency, the dynamic power-saving resource allocation (DPRA) mechanism based on a particle swarm optimization algorithm is proposed. The DPRA mechanism not only considers the energy consumption of physical machine (PM) and virtual machine (VM) but also newly tackles the energy efficiency ratio of air conditioner. Moreover, the least squares regression method is utilized to forecast PMs resource utilization for allocating VM and eliminating VM migrations. In simulation, the proposed DPRA mechanism is compared with three familiar allocation schemes and one previous solution. Simulation results show that in the presence of VM number, DPRA outperforms traditional schemes and previous solution in terms of total energy consumption (includes PMs and air conditioners), total electric bill, VM migration, and the number of shutdown PMs, chosen as objective performance metrics.
Li-Der Chou; Hui-Fan Chen; Fan-Hsun Tseng; Han-Chieh Chao; Yao-Jen Chang. DPRA: Dynamic Power-Saving Resource Allocation for Cloud Data Center Using Particle Swarm Optimization. IEEE Systems Journal 2016, 12, 1554 -1565.
AMA StyleLi-Der Chou, Hui-Fan Chen, Fan-Hsun Tseng, Han-Chieh Chao, Yao-Jen Chang. DPRA: Dynamic Power-Saving Resource Allocation for Cloud Data Center Using Particle Swarm Optimization. IEEE Systems Journal. 2016; 12 (2):1554-1565.
Chicago/Turabian StyleLi-Der Chou; Hui-Fan Chen; Fan-Hsun Tseng; Han-Chieh Chao; Yao-Jen Chang. 2016. "DPRA: Dynamic Power-Saving Resource Allocation for Cloud Data Center Using Particle Swarm Optimization." IEEE Systems Journal 12, no. 2: 1554-1565.