A Context-Aware Fog Enabled Scheme for Real-Time Cross-Vertical IoT Applications

Roy, Diptendu Sinha; Behera, Ranjit Kumar; Reddy, K. Hemant Kumar; Buyya, Rajkumar

doi:10.1109/jiot.2018.2869323

Cited by 35 publications

(18 citation statements)

References 36 publications

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“…The Smart Context Sharing (SCS) is an algorithm designed for facilitate context sharing among Fog nodes [45] . It focuses on sharing context information of different Fog nodes deployed on different IoT domains (e.g., smart agriculture, smart health, smart traffic).…”

Section: Scsmentioning

confidence: 99%

Context information sharing for the Internet of Things: A survey

et al. 2020

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Section: Scsmentioning

confidence: 99%

Context information sharing for the Internet of Things: A survey

et al. 2020

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“…Without a proper data managing design for the end devices and the fog nodes, data traffic will be an issue for the server that will store all the results. Realtime applications such as urban sound classification will have data that will continuously be streamed to the server by multiple nodes [13,27]. If the receiving end of the server cannot handle the volume of data, then the framework will not work.…”

Section: Open Issues For Iot-fog Frameworkmentioning

confidence: 99%

“…However, cloud services are now running into the same issues that IoT networks had before [12,13]. As networks expanded, so did the demand for its services.…”

Section: Introductionmentioning

confidence: 99%

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A scalable IoT-fog framework for urban sound sensing

Baucas

Spachos

2020

Computer Communications

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Internet of Things (IoT) is a system of interrelated devices that can be used to allow large-scale collection and analysis of data. However, as it grew, IoT networks were not capable of managing the data from these services. As a result, cloud computing was introduced to address the need for datacentres for IoT networks. As the technology evolved, the demand for a proper means of supporting and managing crowdsensing and real-time data increased, and cloud servers could no longer keep up with the large volumes of incoming data. This demand brought rise to fog computing. It became an extension to the cloud and allowed resources to be allocated around the network effectively. Its integration to IoT reduced the strain towards the cloud servers. However, issues in high power consumption at the end device and data management constraints surfaced. This paper proposes two approaches to alleviate these issues to keep fog computing remain as a reliable option for IoT-related applications. We created an IoT-based sensing framework that used an urban sound classification model. Through active low and high power states and resource reallocation, we created a network configuration. We tested this configuration against IoT frameworks that use the default fog and cloud setups. The results improved the framework's end device power consumption and server latency. Overall, with the proposed framework, fog computing was proven to be capable of supporting a scalable IoT framework for urban sound sensing.

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“…During runtime, the model presented takes liability for dynamic escalation of executed appliances. The typical objectives of the work underway are the development of a HLL programming plan [26] and promoting dynamic scalability [27]. Mobile fog appliance is developed on processes configured to distribute computing examples.…”

Section: Fog Computing Programming Modelsmentioning

confidence: 99%

Layered Programming Model for Resource Provisioning in Fog Computing using Yet another Fog Simulator

2020

IJETER

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The huge usage of IoT devices and their deployment in multiple electronic machines need to address complex computing while processing their information from that device to any other network or cloud. Fog computing address those computing needs of IoT devices by managing the connectivity at the edge of their network by allocating resources virtually in cloud. This paper provides a perspective survey on fog computing providing features on multi-tier or distributed architecture concept of it, along with applicability of programming models and management of resource provisioning among the network of IoT devices.

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A Context-Aware Fog Enabled Scheme for Real-Time Cross-Vertical IoT Applications

Cited by 35 publications

References 36 publications

Context information sharing for the Internet of Things: A survey

Context information sharing for the Internet of Things: A survey

A scalable IoT-fog framework for urban sound sensing

Layered Programming Model for Resource Provisioning in Fog Computing using Yet another Fog Simulator

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