Author
Bo-Lung Tsai
Other affiliations: National Taiwan University
Bio: Bo-Lung Tsai is an academic researcher from University of California, Irvine. The author has contributed to research in topics: Object detection & Activity recognition. The author has an hindex of 2, co-authored 8 publications receiving 30 citations. Previous affiliations of Bo-Lung Tsai include National Taiwan University.
Papers
More filters
••
TL;DR: The proposed edge intelligence framework pushes the streaming processing capability from cloud core to edge devices, in order to better support timely and reliable streaming data analytics in smart IoT applications.
33 citations
••
06 Jun 2015TL;DR: WuKong middleware is extended to interface with context engine, to learn the context based on the history of user behaviors, and to command the devices in the system according to the context.
Abstract: In this work, we extend WuKong middleware to interface with context engine, to learn the context based on the history of user behaviors, and to command the devices in the system according to the context. With the enhanced WuKong framework, one can design and implement context-ware/user-behavior-aware IoT applications using FBP in WuKong middleware.
4 citations
•
15 Mar 2013TL;DR: This work leverages persuasive mechanism for increasing incentives to change human behavior in a problem solving framework by linking feedback to the actions, and is able to increase the incentives to trigger behavior change.
Abstract: There are many problems around us need to be solved by human agents. It is very challenging to persuade people to change behavior implicitly, especially for solving public problems. We leverage persuasive mechanism for increasing incentives to change human behavior in a problem solving framework. By linking feedback to the actions, we've been able to increase the incentives to trigger behavior change. We deploy two persuasive feedback system in a building to support energy-saving scenario. By integrating sound feedback to window closing behavior to make people aware of the energy problem in the public space.
2 citations
••
01 Nov 2019TL;DR: A new picture-based localization service PicPose is presented that relies on the feature points extracted from a camera-captured image and conducts feature point matching with the original wall picture to conduct pose calculation, which is impossible for ArPico and ArUco.
Abstract: Device self-localization is an important capability for many IoT applications that require mobility in service capabilities. In our previous work, we have designed the ArPico method for robot indoor localization. By placing and recognizing pre-installed pictures on walls, robots can use low-cost cameras to identify their positions by referencing to pictures' precise locations. However, using ArPico, all pictures need to have clear rectangular borders for the pose computation. But some real-world pictures does not have clear thick borders. Moreover, some pictures may have odd shapes or are only partially visible. To address these problems, a new picture-based localization service PicPose is presented. PicPose relies on the feature points extracted from a camera-captured image and conducts feature point matching with the original wall picture to conduct pose calculation. Using PicPose, even partially visible pictures can be used for localization, which is impossible for ArPico and ArUco. We present our implementation and experiment results in this paper.
1 citations
••
TL;DR: An autonomous moving robot that can self-localize itself using its on-board camera and the PicPose technology is built and shows that the localization methods are practical, have very good accuracy, and can be used for real time robot navigation.
Abstract: Localization is an important technology for smart services like autonomous surveillance, disinfection or delivery robots in future distributed indoor IoT applications. Visual-based localization (VBL) is a promising self-localization approach that identifies a robot’s location in an indoor or underground 3D space by using its camera to scan and match the robot’s surrounding objects and scenes. In this study, we present a pictorial planar surface based 3D object localization framework. We have designed two object detection methods for localization, ArPico and PicPose. ArPico detects and recognizes framed pictures by converting them into binary marker codes for matching with known codes in the library. It then uses the corner points on a picture’s border to identify the camera’s pose in the 3D space. PicPose detects the pictorial planar surface of an object in a camera view and produces the pose output by matching the feature points in the view with that in the original picture and producing the homography to map the object’s actual location in the 3D real world map. We have built an autonomous moving robot that can self-localize itself using its on-board camera and the PicPose technology. The experiment study shows that our localization methods are practical, have very good accuracy, and can be used for real time robot navigation.
1 citations
Cited by
More filters
••
TL;DR: This article analyzes the main features of MEC in the context of 5G and IoT and presents several fundamental key technologies which enable MEC to be applied in 5Gs and IoT, such as cloud computing, software-defined networking/network function virtualization, information-centric networks, virtual machine (VM) and containers, smart devices, network slicing, and computation offloading.
Abstract: To satisfy the increasing demand of mobile data traffic and meet the stringent requirements of the emerging Internet-of-Things (IoT) applications such as smart city, healthcare, and augmented/virtual reality (AR/VR), the fifth-generation (5G) enabling technologies are proposed and utilized in networks As an emerging key technology of 5G and a key enabler of IoT, multiaccess edge computing (MEC), which integrates telecommunication and IT services, offers cloud computing capabilities at the edge of the radio access network (RAN) By providing computational and storage resources at the edge, MEC can reduce latency for end users Hence, this article investigates MEC for 5G and IoT comprehensively It analyzes the main features of MEC in the context of 5G and IoT and presents several fundamental key technologies which enable MEC to be applied in 5G and IoT, such as cloud computing, software-defined networking/network function virtualization, information-centric networks, virtual machine (VM) and containers, smart devices, network slicing, and computation offloading In addition, this article provides an overview of the role of MEC in 5G and IoT, bringing light into the different MEC-enabled 5G and IoT applications as well as the promising future directions of integrating MEC with 5G and IoT Moreover, this article further elaborates research challenges and open issues of MEC for 5G and IoT Last but not least, we propose a use case that utilizes MEC to achieve edge intelligence in IoT scenarios
303 citations
••
TL;DR: This paper proposes a peer-to-peer (P2P) knowledge market to make knowledge tradable in edge-AI enabled IoT, and develops a knowledge consortium blockchain for secure and efficient knowledge management and trading for the market.
Abstract: Nowadays, benefit from more powerful edge computing devices and edge artificial intelligence (edge-AI) could be introduced into Internet of Things (IoT) to find the knowledge derived from massive sensory data, such as cyber results or models of classification, and detection and prediction from physical environments. Heterogeneous edge-AI devices in IoT will generate isolated and distributed knowledge slices, thus knowledge collaboration and exchange are required to complete complex tasks in IoT intelligent applications with numerous selfish nodes. Therefore, knowledge trading is needed for paid sharing in edge-AI enabled IoT. Most existing works only focus on knowledge generation rather than trading in IoT. To address this issue, in this paper, we propose a peer-to-peer (P2P) knowledge market to make knowledge tradable in edge-AI enabled IoT. We first propose an implementation architecture of the knowledge market. Moreover, we develop a knowledge consortium blockchain for secure and efficient knowledge management and trading for the market, which includes a new cryptographic currency knowledge coin, smart contracts, and a new consensus mechanism proof of trading. Besides, a noncooperative game based knowledge pricing strategy with incentives for the market is also proposed. The security analysis and performance simulation show the security and efficiency of our knowledge market and incentive effects of knowledge pricing strategy. To the best of our knowledge, it is the first time to propose an efficient and incentive P2P knowledge market in edge-AI enabled IoT.
142 citations
••
TL;DR: To evolve with the new computing and communication paradigms, theCIoT ecosystem has to update by absorbing new capabilities such as deep learning, the CIoT sensing system, data analytics, and cognitiion in providing human-like intelligence.
Abstract: A new network paradigm, CIoT, has been proposed by applying cognitive computing technologies, which is derived from cognitive science and artificial intelligence in combination with the data generated by connected IoT devices and the actions that these devices perform. The development of cognitive computing is very important in the above process to meet key technical challenges, such as generation of big sensory data, efficient computing/storage at the CIoT edge, and integration of multiple data sources and types. On the other hand, to evolve with the new computing and communication paradigms, the CIoT ecosystem has to update by absorbing new capabilities such as deep learning, the CIoT sensing system, data analytics, and cognitiion in providing human-like intelligence.
118 citations
••
TL;DR: This special issue presents novel research approaches related to Big Data, IOT and cloud computing and discusses the encountered problems and open issues.
87 citations
••
TL;DR: A Big Data architecture supporting typical cultural heritage applications and a novel user-centered recommendation strategy for cultural items suggestion, exploiting jointly recommendation techniques and edge artificial intelligence facilities are presented.
Abstract: Recommender systems are increasingly playing an important role in our life, enabling users to find “what they need” within large data collections and supporting a variety of applications, from e-commerce to e-tourism. In this paper, we present a Big Data architecture supporting typical cultural heritage applications. On the top of querying, browsing, and analyzing cultural contents coming from distributed and heterogeneous repositories, we propose a novel user-centered recommendation strategy for cultural items suggestion. Despite centralizing the processing operations within the cloud, the vision of edge intelligence has been exploited by having a mobile app ( Smart Search Museum ) to perform semantic searches and machine-learning-based inference so as to be capable of suggesting museums, together with other items of interest, to users when they are visiting a city, exploiting jointly recommendation techniques and edge artificial intelligence facilities. Experimental results on accuracy and user satisfaction show the goodness of the proposed application.
60 citations