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Chi-Sheng Shih

Bio: Chi-Sheng Shih is an academic researcher from National Taiwan University. The author has contributed to research in topics: Scheduling (computing) & Middleware. The author has an hindex of 21, co-authored 141 publications receiving 1630 citations. Previous affiliations of Chi-Sheng Shih include National Taiwan University of Science and Technology & Center for Information Technology.


Papers
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Journal ArticleDOI
TL;DR: A novel mobile cloud execution framework is proposed to execute mobile applications in a cloud-based virtualized execution environment controlled by mobile applications and users, with encryption and isolation to protect against eavesdropping from cloud providers.
Abstract: Modern mobile devices, such as smartphones and tablets, have made many pervasive computing dreams come true. Still, many mobile applications do not perform well due to the shortage of resources for computation, data storage, network bandwidth, and battery capacity. While such applications can be re-designed with client-server models to benefit from cloud services, the users are no longer in full control of the application, which has become a serious concern for data security and privacy. In addition, the collaboration between a mobile device and a cloud server poses complex performance issues associated with the exchange of application state, synchronization of data, network condition, etc. In this work, a novel mobile cloud execution framework is proposed to execute mobile applications in a cloud-based virtualized execution environment controlled by mobile applications and users, with encryption and isolation to protect against eavesdropping from cloud providers. Under this framework, several efficient schemes have been developed to deal with technical issues for migrating applications and synchronizing data between execution environments. The communication issues are also addressed in the virtualization execution environment with probabilistic communication Quality-of-Service (QoS) technique to support timely application migration.

106 citations

Journal ArticleDOI
01 Dec 2016
TL;DR: In this study, the authors study the development and challenges in five topics: middleware, computation model, fault tolerance, quality of data, and virtual run-time environment.
Abstract: Internet of Things (IoT) and cyber-physical systems (CPS) technologies can be applied to many application domains. Examples include intelligent green house, intelligent transportation system, power distribution grid, smart home, smart building, and smart city. Among these application domains, some of them have been extensively studied, e.g., smart home and intelligent transportation systems. In the meantime, smart buildings and smart cities attract researchers and industries to investigate these two use scenarios. Well-designed IoT/CPS can reduce energy consumption, enhance safety in buildings and cities, or can increase the comfortability in the building. In the last few years, the research communities and industrial partners started to study and investigate these two use scenarios to develop prototype or commercial services for these two scenarios. Although many works have been conducted on these two scenarios, many challenges remain open. In this study, the authors study the development and challenges in five topics. They are middleware, computation model, fault tolerance, quality of data, and virtual run-time environment.

98 citations

Proceedings ArticleDOI
01 Aug 1998
TL;DR: A system that extracts and generalizes terms from Internet documents to represent classification knowledge of a given class hierarchy and presents a polynomialtime inference algorithm to promote a term, strongly associated to a known keyword, to a keyword.
Abstract: In this paper, we present a system that extracts and generalizes terms from Internet documents to represent classification knowledge of a given class hierarchy. We propose a measurement to evaluate the importance of a term with respect to a class in the class hierarchy, and denote it as support. With a given threshold, terms with high supports are sifted as keywords of a class, and terms with low supports are filtered out. To further enhance the recall of this approach, Mining Association Rules technique is applied to mine the association between terms. An inference model is composed of these association relations and the previously computed supports of the terms in the class. To increase the recall rate of the keyword selection process. we then present a polynomialtime inference algorithm to promote a term, strongly associated to a known keyword, to a keyword. According to our experiment results on the collected Internet documents from Yam search engine, we show that the proposed methods In the paper contribute to refine the classification knowledge and increase the recall of keyword selection.

97 citations

Proceedings ArticleDOI
30 Jun 2004
TL;DR: This paper proposes a fast online resource allocation algorithm (CoRAl) to dynamically reconfigure a sensor network whenever a new hot spot occurs or a node's activity changes, and shows that CoRAl provides always near-optimal resource allocation while keeping its online overhead low.
Abstract: Traditional real-time resource allocation algorithms assume that the available resources in a system such as total CPU and network bandwidth do not change over time. However, in wireless sensor networks, the amount of available resources on the devices and the communication channel may not be constant for all times: for instance, a node can be turned off in some time intervals to increase its battery lifetime. Since sensor networks have limited network capacity and computational capabilities, it is crucial to optimally assign the available resources among all the active tasks. In this paper, we propose a fast online resource allocation algorithm (CoRAl) to dynamically reconfigure a sensor network whenever a new hot spot occurs (e.g., a new intruder is detected) or a node's activity changes (i.e., sleep vs. active mode). Our experimental results show that CoRAl provides always near-optimal resource allocation while keeping its online overhead low.

75 citations

Proceedings ArticleDOI
06 Mar 2014
TL;DR: The fault tolerance mechanism for IoT is developed, which is distributed and takes into account the dynamics within IoT, and Strip is introduced to store a list of duplicated services, and, each service peer maintains a consistent view of duplicate services in the strip.
Abstract: Failover for service-oriented distributed networks is a prerequisite to enabling Internet-of-Things (IoT) in the sense of deploy-once, run forever. Resource reconfiguration is required to achieve failover mechanisms upon replacement of devices or failure of services. It can be particularly challenging when services in applications have more than end-to-end transmissions between devices that are heterogeneous or versatile, for which duplications can be costly and redundant. Specifically, a device with a failed service shall be taken over by another service peer without users', including developers and installers, involvement. We develop the fault tolerance mechanism for IoT, which is distributed and takes into account the dynamics within IoT. Strip is introduced to store a list of duplicated services, and, each service peer maintains a consistent view of duplicated services in the strip. In combination with the heartbeat protocol, recovery from failure can be achieved by manipulating strips in a distributed manner. Experiments using Arduino Mega 2560 compatible devices show that our approach is capable of failover in small networks, whereas experiments in larger networks are underway. The results show that the faulures can be recovered within few seconds without administrator or developers in the loop.

66 citations


Cited by
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Journal ArticleDOI
TL;DR: This paper outlines a set of requirements for IoT middleware, and presents a comprehensive review of the existing middleware solutions against those requirements, and open research issues, challenges, and future research directions are highlighted.
Abstract: The Internet of Things (IoT) envisages a future in which digital and physical things or objects (e.g., smartphones, TVs, cars) can be connected by means of suitable information and communication technologies, to enable a range of applications and services. The IoT’s characteristics, including an ultra-large-scale network of things, device and network level heterogeneity, and large numbers of events generated spontaneously by these things, will make development of the diverse applications and services a very challenging task. In general, middleware can ease a development process by integrating heterogeneous computing and communications devices, and supporting interoperability within the diverse applications and services. Recently, there have been a number of proposals for IoT middleware. These proposals mostly addressed wireless sensor networks (WSNs), a key component of IoT, but do not consider RF identification (RFID), machine-to-machine (M2M) communications, and supervisory control and data acquisition (SCADA), other three core elements in the IoT vision. In this paper, we outline a set of requirements for IoT middleware, and present a comprehensive review of the existing middleware solutions against those requirements. In addition, open research issues, challenges, and future research directions are highlighted.

805 citations

Proceedings ArticleDOI
26 Aug 2001
TL;DR: This paper proposes an unsupervised method for discovering inference rules from text, based on an extended version of Harris' Distributional Hypothesis, which states that words that occurred in the same contexts tend to be similar.
Abstract: In this paper, we propose an unsupervised method for discovering inference rules from text, such as "X is author of Y a X wrote Y", "X solved Y a X found a solution to Y", and "X caused Y a Y is triggered by X". Inference rules are extremely important in many fields such as natural language processing, information retrieval, and artificial intelligence in general. Our algorithm is based on an extended version of Harris' Distributional Hypothesis, which states that words that occurred in the same contexts tend to be similar. Instead of using this hypothesis on words, we apply it to paths in the dependency trees of a parsed corpus.

621 citations

Journal ArticleDOI
TL;DR: This paper presents an unsupervised algorithm for discovering inference rules from text based on an extended version of Harris’ Distributional Hypothesis, which states that words that occurred in the same contexts tend to be similar.
Abstract: One of the main challenges in question-answering is the potential mismatch between the expressions in questions and the expressions in texts. While humans appear to use inference rules such as ‘X writes Y’ implies ‘X is the author of Y’ in answering questions, such rules are generally unavailable to question-answering systems due to the inherent difficulty in constructing them. In this paper, we present an unsupervised algorithm for discovering inference rules from text. Our algorithm is based on an extended version of Harris’ Distributional Hypothesis, which states that words that occurred in the same contexts tend to be similar. Instead of using this hypothesis on words, we apply it to paths in the dependency trees of a parsed corpus. Essentially, if two paths tend to link the same set of words, we hypothesize that their meanings are similar. We use examples to show that our system discovers many inference rules easily missed by humans.

585 citations

Journal ArticleDOI
01 Dec 2013
TL;DR: The state-of-the-art artificial intelligence (AI) methodologies used for developing AmI system in the healthcare domain are summarized, including various learning techniques (for learning from user interaction), reasoning techniques ( for reasoning about users' goals and intensions), and planning techniques (For planning activities and interactions).
Abstract: Ambient Intelligence (AmI) is a new paradigm in information technology aimed at empowering people's capabilities by means of digital environments that are sensitive, adaptive, and responsive to human needs, habits, gestures, and emotions. This futuristic vision of daily environment will enable innovative human-machine interactions characterized by pervasive, unobtrusive, and anticipatory communications. Such innovative interaction paradigms make AmI technology a suitable candidate for developing various real life solutions, including in the healthcare domain. This survey will discuss the emergence of AmI techniques in the healthcare domain, in order to provide the research community with the necessary background. We will examine the infrastructure and technology required for achieving the vision of AmI, such as smart environments and wearable medical devices. We will summarize the state-of-the-art artificial intelligence (AI) methodologies used for developing AmI system in the healthcare domain, including various learning techniques (for learning from user interaction), reasoning techniques (for reasoning about users' goals and intensions), and planning techniques (for planning activities and interactions). We will also discuss how AmI technology might support people affected by various physical or mental disabilities or chronic disease. Finally, we will point to some of the successful case studies in the area and we will look at the current and future challenges to draw upon the possible future research paths.

565 citations

Journal Article
TL;DR: This work proposes a tiered system architecture in which data collected at numerous, inexpensive sensor nodes is filtered by local processing on its way through to larger, more capable and more expensive nodes.
Abstract: As new fabrication and integration technologies reduce the cost and size of micro-sensors and wireless interfaces, it becomes feasible to deploy densely distributed wireless networks of sensors and actuators. These systems promise to revolutionize biological, earth, and environmental monitoring applications, providing data at granularities unrealizable by other means. In addition to the challenges of miniaturization, new system architectures and new network algorithms must be developed to transform the vast quantity of raw sensor data into a manageable stream of high-level data. To address this, we propose a tiered system architecture in which data collected at numerous, inexpensive sensor nodes is filtered by local processing on its way through to larger, more capable and more expensive nodes.We briefly describe Habitat monitoring as our motivating application and introduce initial system building blocks designed to support this application. The remainder of the paper presents details of our experimental platform.

454 citations