MIST: Mobility-inspired software-defined fog system

Gebre-Amlak, Haymanot; Lee, Seoungjin; Jabbari, Abdoh; Chen, Yu; Choi, Baek‐Young; Huang, Chin‐Tser; Song, Sejun

doi:10.1109/icce.2017.7889243

Cited by 8 publications

(6 citation statements)

References 14 publications

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“…MIST fog-based scheme performs better than traditional cloud computing as the demand for real-time service rises. A fog computing framework supporting software defined networks for mobile sensor applications and android based smart devices is proposed in [35]. Proof of concept MIST architecture is built on linux and android based smart devices.…”

Section: F Energy Efficiency In Mistmentioning

confidence: 99%

A Novel Fog-Based Multi-Level Energy-Efficient Framework for IoT-Enabled Smart Environments

et al. 2020

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The Internet of Things (IoT) has emerged as a promising paradigm to enhance the living standard of human life by employing varied smart devices including smart phones, smart watches, sensors, on-board units and other networking equipment. However, these devices consume a considerable amount of energy to perform their operations that has a significant impact on the environment, product cost and life of the device. Given this fact, energy-efficient solutions for smart environments have gained great attention from researchers and the industrial community. In this context, a novel fog-based multi-level energy-efficient framework for IoT-enabled smart environments has been proposed. To achieve this, the proposed framework adds additional two layers in the existing IoT-fog-cloud architecture -sensors-based energy-efficient hardware layer and policy layer, to monitor the energy consumption and to enable the energy-aware decision making. Initially, the main sources of energy consumption in an IoT-enabled smart environment are identified. Further, the energy requirements of a device to perform a specific task are estimated. Moreover, the alternative devices to perform the same task using less energy are searched out. Finally, a device or a set of devices, to process the job consuming lower energy while ensuring the job requirements, is selected. To validate the proposed framework, four case studies are considered -smart parking, smart hospital specifically ICU, smart agriculture and smart airport. Simulations are conducted using iFogsim toolkit and results show that a significant amount of energy can be conserved by employing the proposed framework.

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Section: F Energy Efficiency In Mistmentioning

confidence: 99%

A Novel Fog-Based Multi-Level Energy-Efficient Framework for IoT-Enabled Smart Environments

et al. 2020

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“…Also, SDN controller along with virtualization hypervisor, i.e. fog cloud is responsible for adjusting the energy level of each underwater sensor node [79].…”

Section: E Cloud and Fog Computingmentioning

confidence: 99%

An Overview of Next-Generation Underwater Target Detection and Tracking: An Integrated Underwater Architecture

Ghafoor

Noh

2019

IEEE Access

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Military forces of every country are trying their best to protect their motherland from the attackers. With advancement in marine technology, it has become critical to detect and track the target by obtaining active measurements before it is close enough to attack. The utilization of unmanned underwater vehicles for target tracking behavior is gaining great attention due to continuous advancement of underwater vehicular technology. Nevertheless, safe and stable communications issues among different acoustic devices are still under active investigation to reach a robust, secure, and flexible underwater networking. Moreover, due to harsh underwater environment, acoustic simulations are also time-consuming; therefore, an accurate model for target detection and tracking is a necessity. Apart from the harsh environment of underwater networks, various technologies emerging for terrestrial networking are also becoming the part of underwater networking. For instance, cognitive acoustic networks, software-defined networks, network function virtualization, cloud computing, fog computing, and internet of underwater things; all are leading to trusted next-generation underwater networks. In this paper, we first provide a comprehensive survey of unmanned underwater vehicles and different ray tracing models essential in target detection and tracking that answers several questions regarding the current necessities of underwater networks and finally, provides a solution that opens several doors for research community to excel in this area.INDEX TERMS Internet of underwater things (IoUT), ray tracing models, underwater acoustic networks (UAN), unmanned underwater vehicles (UUV).

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“…Software defined networking has inspired a host of software defined programming frameworks for IoT 26,27,28 , edge 29 , fog 30,31 and cloud computing 32 . A software defined IoT framework encompassing software defined storage 33 and software defined security 34 was developed in 26 .…”

Section: Software Defined Programmingmentioning

confidence: 99%

“…A similar three-layered architecture was proposed in 27 for smart urban sensing. MIST 31 , a software defined fog system leveraging software defined mobility 35 for real-time surveillance was developed to provide device mobility and change adaptively based on context. Nodes are classified as being controllers, hosts or switches to enable seamless execution even in the presence of frequent changes in membership.…”

Section: Software Defined Programmingmentioning

confidence: 99%

A Language for Programming Edge Clouds for Next Generation IoT Applications

Maheswaran¹,

Wenger²,

Olaniyan³

et al. 2019

Preprint

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For effective use of edge computing in an IoT application, we need to partition the application into tasks and map them into the cloud, fog (edge server), device levels such that the resources at the different levels are optimally used to meet the overall quality of service requirements. In this paper, we consider four concerns about application-to-fog mapping: task placement at different levels, data filtering to limit network loading, fog fail-over, and data consistency, and reacting to hotspots at the edge. We describe a programming language and middleware we created for edge computing that addresses the above four concerns. The language has a distributednode programming model that allows programs to be written for a collection of nodes organized into a cloud, fog, device hierarchy. The paper describes the major design elements of the language and explains the prototype implementation. The unique distributed-node programming model embodied in the language enables new edgeoriented programming patterns that are highly suitable for cognitive or data-intensive edge computing workloads. The paper presents result from an initial evaluation of the language prototype and also a distributed shell and a smart parking app that were developed using the programming language.

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MIST: Mobility-inspired software-defined fog system

Cited by 8 publications

References 14 publications

A Novel Fog-Based Multi-Level Energy-Efficient Framework for IoT-Enabled Smart Environments

A Novel Fog-Based Multi-Level Energy-Efficient Framework for IoT-Enabled Smart Environments

An Overview of Next-Generation Underwater Target Detection and Tracking: An Integrated Underwater Architecture

A Language for Programming Edge Clouds for Next Generation IoT Applications

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