ORCID Profile
0000-0001-8932-1174
Current Organisation
University of Melbourne
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Publisher: IEEE
Date: 12-2016
Publisher: IGI Global
Date: 10-2017
DOI: 10.4018/IJMCMC.2017100101
Abstract: A common design of the Internet of Things (IoT) system relies on distant Cloud for management and processing, which faces the challenge of latency, especially when the application requires rapid response in the edge network. Therefore, researchers have proposed the Fog computing architecture, which distributes the computational data processing tasks to the edge network nodes located in the vicinity of data sources and end-users to reduce the latency. Although the Fog computing architecture is promising, it still faces a challenge in mobility when the tasks come from ubiquitous mobile applications in which the data sources are moving objects. In order to address the challenge, this article proposes a proactive Fog service provisioning framework, which hastens the task distribution process in Mobile Fog use cases. Further, the proposed framework provides an optimization scheme in task allocation based on runtime context information. A proof-of-concept prototype has been implemented and tested on real devices.
Publisher: Emerald
Date: 07-04-2015
DOI: 10.1108/IJPCC-03-2014-0019
Abstract: – Recent smart mobile devices are capable of letting users produce various digital content, and share/upload the content to many social network services (SNS) directly via wireless network connections. The phenomenon has increased the number of people using mobile SNS applications. Although the applications have become more popular, mobile users have been restricted in the virtual communities of online SNS and are not aware of the social opportunities available to them in real-time surrounding. While they spend most of their time accessing online SNS, they have missed many opportunities to interact with others for new friendships, business opportunities or information sharing. Consequently, a new breed of mobile social network (MSN) system has arisen to assist mobile users to interact with proximal people and perform various social activities. Such a proximal-based MSN environment is termed a Mobile Social Network in Proximity (MSNP). – Developing an MSNP system needs to address a number of issues and challenges, such as heterogeneity, content/service discovery, privacy and trust, resource management, and so on. This paper identifies and describes these challenges, and reviews a number of related solutions from existing literature. In the follow up, this paper addresses a number of open challenges in the MSNP domain. – Although various works have been proposed to enable and overcome challenges in MSNP, there are still many unsolved open challenges in terms of identification, content management, social-aware discovery, trust in public environment, adaptation, quality of service and the development of MSNP. We have addressed these challenges in this paper as future research directions in the MSNP domain. – This paper provides an original literature review in MSNP and identifies a number of open challenges as research direction in the MSNP domain.
Publisher: Emerald
Date: 03-09-2018
Abstract: The distant data centre-centric Internet of Things (IoT) systems face the latency issue especially in the real-time-based applications, such as augmented reality, traffic analytics and ambient assisted living. Recently, Fog computing models have been introduced to overcome the latency issue by using the proximity-based computational resources, such as the computers co-located with the cellular base station, grid router devices or computers in local business. However, the increasing users of Fog computing servers cause bottleneck issues and consequently the latency issue arises again. This paper aims to introduce the utilisation of Mist computing (Mist) model, which exploits the computational and networking resources from the devices at the very edge of the IoT networks. This paper proposes a service-oriented mobile-embedded Platform as a Service (mePaaS) framework that allows the mobile device to provide a flexible platform for proximal users to offload their computational or networking program to mePaaS-based Mist computing node. The prototype has been tested and performance has been evaluated on the real-world devices. The evaluation results have shown the promising nature of mePaaS. The proposed framework supports resource-aware autonomous service configuration that can manage the availability of the functions provided by the Mist node based on the dynamically changing hardware resource availability. In addition, the framework also supports task distribution among a group of Mist nodes.
Publisher: IEEE
Date: 12-2016
Publisher: Elsevier BV
Date: 06-2019
Publisher: IEEE
Date: 03-2014
Publisher: Elsevier BV
Date: 08-2020
Publisher: Association for Computing Machinery (ACM)
Date: 20-12-2016
DOI: 10.1145/3012000
Abstract: The Internet of Things (IoT) represents a comprehensive environment that consists of a large number of smart devices interconnecting heterogeneous physical objects to the Internet. Many domains such as logistics, manufacturing, agriculture, urban computing, home automation, ambient assisted living, and various ubiquitous computing applications have utilized IoT technologies. Meanwhile, Business Process Management Systems (BPMSs) have become a successful and efficient solution for coordinated management and optimized utilization of resources/entities. However, past BPMSs have not considered many issues they will face in managing large-scale connected heterogeneous IoT entities. Without fully understanding the behavior, capability, and state of the IoT entities, the BPMS can fail to manage the IoT integrated information systems. In this article, we analyze existing BPMSs for IoT and identify the limitations and their drawbacks based on a Mobile Cloud Computing perspective. Later, we discuss a number of open challenges in BPMS for IoT.
Publisher: Elsevier BV
Date: 06-2014
Publisher: ACM
Date: 28-11-2016
Publisher: IEEE
Date: 06-2016
DOI: 10.1109/SCC.2016.90
Publisher: IEEE
Date: 12-2016
Publisher: AICIT
Date: 30-04-2013
Publisher: IEEE
Date: 12-2015
Publisher: ACM
Date: 25-11-2014
Publisher: ACM
Date: 13-11-2028
Publisher: IEEE
Date: 06-2015
DOI: 10.1109/ICWS.2015.90
Publisher: IEEE
Date: 03-2017
DOI: 10.1109/AINA.2017.18
Publisher: IEEE
Date: 03-2011
Publisher: IEEE
Date: 06-2015
DOI: 10.1109/SCC.2015.27
Publisher: IEEE
Date: 12-2016
Publisher: IEEE
Date: 06-2015
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 2017
No related grants have been discovered for Chii Chang.