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Author

Olivia J. Smith

Other affiliations: Telstra, University of Melbourne
Bio: Olivia J. Smith is an academic researcher from IBM. The author has contributed to research in topics: Lift (data mining) & Shortest path problem. The author has an hindex of 6, co-authored 20 publications receiving 151 citations. Previous affiliations of Olivia J. Smith include Telstra & University of Melbourne.

Papers
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Journal ArticleDOI
TL;DR: The weight constrained shortest path problem with replenishment (WCSPP-R) is reviewed, preprocessing methods are developed, existing WCSPP algorithms are extended, and new algorithms that exploit the inter-replenishment path structure are presented.

76 citations

Proceedings ArticleDOI
22 Aug 2017
TL;DR: Cognitive IoT gateways powered by cognitive analytics and machine learning to improve the performance of IoT applications will enable the IoT applications to be optimized for multiple objectives including task performance.
Abstract: Fog computing, also known as Edge computing, is an emerging computational paradigm, increasingly utilized in Internet of Things (IoT) applications, particularly those that cannot be served efficiently using Cloud computing due to limitations such as bandwidth, latency, Internet connectivity. At present, the norm is the static allocation of tasks by developers of an application, where some IoT applications are allocated to be performed on the Cloud, some on the Fog, and some on a hybrid Cloud-Fog. The applications are pre-programmed and predefined to be run on a platform, and this is unchangeable at run-time. IoT gateways, which are devices that bridge the IoT local network and the Internet, are in a position to make dynamic adjustments and allocation decision between platforms based upon real-time conditions such as an IoT applications' performance. However, currently there is no (or very little) intelligence embedded into IoT gateways. This paper proposes cognitive IoT gateways powered by cognitive analytics and machine learning to improve the performance of IoT applications. These IoT devices are able to automatically learn and decide when and where to run an application, be that on the Cloud or on the Fog. The dynamic task sharing and platform interchanging will enable the IoT applications to be optimized for multiple objectives including task performance.

32 citations

Proceedings ArticleDOI
08 Jul 2019
TL;DR: A new platform, DEFT (Dynamic Edge-Fabric environmenT), that can automatically learn where best to execute each task based on real-time system status and task requirements, along with learned behavior from past performance of the available resources is proposed.
Abstract: The number of IoT devices at the edge of the network is increasing rapidly. Data from IoT devices can be analysed locally at the edge, or they may be sent to the cloud. Currently, the decision to deploy a task for execution at the edge or in the cloud is not decided as the data are received. Instead the decision is usually based on pre-defined system design and corresponding assumptions about locality and connectivity. However, the mobile environment has rapid, sometimes unpredictable changes and requires a system that can dynamically adapt to these changes. An intelligent platform is required that can discover available resources (both nearby and in the cloud) and autonomously orchestrate a seamless and transparent task allocation at runtime to help the IoT devices achieve their best performance given the available resources. We propose a new platform, DEFT (Dynamic Edge-Fabric environmenT), that can automatically learn where best to execute each task based on real-time system status and task requirements, along with learned behavior from past performance of the available resources. The task allocation decision in this platform is powered by machine learning techniques such as regression models (linear, ridge, Lasso) and ensemble models (random forest, extra trees). We have implemented this platform on heterogeneous devices and run various IoT tasks on the devices. The results reveal that choosing proper machine learning approaches based on the tasks properties and priorities can significantly improve the overall performance of selecting resources (either from the edge or cloud) dynamically at runtime.

20 citations

Journal ArticleDOI
TL;DR: A multiple objective mixed integer programming model of this problem can provide insightful information to decision makers in the hospital whether they can meet their KPIs with their current resources and also the effect of increasing resources on various KPIs.

17 citations

Journal ArticleDOI
TL;DR: A tree search algorithm for managing the stockyard aimed at maximizing the throughput is developed, which relies on the analysis and use of geometric properties of a coal assembly plan when represented in a space–time diagram.

15 citations


Cited by
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Journal ArticleDOI
TL;DR: An extensive and diverse classification of wearables, based on various factors, a discussion on wireless communication technologies, architectures, data processing aspects, and market status, as well as a variety of other actual information on wearable technology are provided.

197 citations

Journal ArticleDOI
01 Oct 2013-Networks
TL;DR: This article surveys the main contributions that have appeared in the scientific literature addressing resource constrained shortest path problems to provide a starting point for researchers who want to address the problems at hand.
Abstract: This article surveys the main contributions that have appeared in the scientific literature addressing resource constrained shortest path problems. The aim of this work is twofold: to give a structured survey of the literature on this topic; to provide a starting point for researchers who want to address the problems at hand. The study is focused on the relevant contributions dealing with exact solution approaches. © 2013 Wiley Periodicals, Inc. NETWORKS, Vol. 62(3), 183-200 2013

121 citations

Journal ArticleDOI
TL;DR: This work proposes an exact solution method for the constrained shortest path capable of handling large-scale networks in a reasonable amount of time and obtained significant speedups against alternative column generation schemes that solve the auxiliary problem with state-of-the-art commercial (linear) optimizers.

118 citations

Journal ArticleDOI
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

Journal ArticleDOI
TL;DR: An IoT ecosystem encompassing key enabling IoT technologies, building blocks, and a service-oriented architecture (SoA) is proposed as a potential component for accelerating the implementation of PI.
Abstract: The Physical Internet (PI, or $\pi $ ) paradigm has been developed to be a global logistics system that aims to move, handle, store, and transport logistics products in a sustainable and efficient way. To achieve the goal, the PI requires a high-level interconnectivity in the physical, informational, and operational aspects enabled by an interconnected network of intermodal hubs, collaborative protocols, and standardized, modular, and smart containers. In this context, PI is a key player poised to benefit from the Internet-of-Things (IoT) revolution since it potentially provides an end-to-end visibility of the PI objects, operations, and systems through ubiquitous information exchange. This article is to investigate opportunities of application of the IoT technology in the PI vision. In addition, an IoT ecosystem ( $\pi $ -IoT) encompassing key enabling IoT technologies, building blocks, and a service-oriented architecture (SoA) is proposed as a potential component for accelerating the implementation of PI. The major challenges regarding the deployment of IoT into the emerging logistics concept are also discussed intensively for further research.

93 citations