Abstract
Nowadays, healthcare industry is leveraging the technical innovations for providing better facilities to the patients. A number of high quality medical devices are available to record a patient’s health based on numerous parameters. Such sensor-based health monitoring devices generate high volume of data which is analyzed to provide the appropriate treatment. Such monitoring requires the storage and analysis of data on a remote cloud. Though cloud-based services provide efficient storage, they suffer from the delays incurred while sending the data and retrieving the analysis. Fog computing has proven to be an efficient solution to this problem. A fog node can be considered as an edge node, network device, healthcare equipment, etc., having a limited computation power. These devices are located in proximity to the sensor nodes. Fog nodes can be used to perform data analysis in a distributed manner without adding network delay. However, without any proper infrastructure, it is difficult to identify a fog node having sufficient resources to analyze a set of data. This problem can be addressed by using publish/subscribe paradigm over distributed hash tables (DHTs). Publish/subscribe system provides an event triggered approach which can be used to identify a fog node capable to service a data processing request. Further, a DHT is a peer-to-peer overlay network which is used for efficient resource sharing among the peer nodes. In this chapter, a DHT-based peer-to-peer network of fog nodes is proposed. The objective of the proposed networking infrastructure is to create an overlay of physical fog nodes to provide efficient resource discovery. It is achieved by using publish/subscribe communication and peer-to-peer overlays enabling the nodes to share their computation capabilities with each other.
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Shukla, N., Gandhi, C. (2021). Efficient Resource Discovery and Sharing Framework for Fog Computing in Healthcare 4.0. In: Tanwar, S. (eds) Fog Computing for Healthcare 4.0 Environments. Signals and Communication Technology. Springer, Cham. https://doi.org/10.1007/978-3-030-46197-3_16
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