English (United Kingdom)

https://curated-unify.zendy.io/wp-json/zendy-region/v1/featured_content/oa?rat=en

https://curated-unify.zendy.io/wp-json/zendy-region/v1/highlighted_journal/

Global: Zendy Tools annual

Presents the keyphrase highlighting as premium feature

Keyphrase Highlighting

Presents the summarisation as premium feature

Summarisation

Insights

Presents the pdf analysis as premium feature

PDF Analysis

Presents the zaia usage as premium feature

ZAIA

Global: Zendy Tools monthly

Global: Zendy Plus monthly

Presents the access of premium content as premium feature

Premium Content

Global: Zendy Plus annual

Global: Zendy Free Plan

Fog computing has been introduced to extend the cloud services by bringing the services near to the user&#x2019;s proximity. However, the distributed location of the fog servers requires a proper management to ensure the network to provide a service resilience during disruption while preserving the energy consumption of the networking and processing equipment. In this paper,a 1+1 server protection scheme where a primary and a secondary processing server are used to serve Electrocardiogram (ECG) monitoring IoT applications concurrently has been considered at the fog networking infrastructure. The infrastructure is designed to be resilient against server failures related to the geographic location of primary and secondary servers and against both server and network failures. A Mixed Integer Linear Programming (MILP) model is developed to optimize the number and locations of the processing servers for energy-efficient resilient fog infrastructure. The results reveal that considering server protection without geographical constraints resulted in network and processing energy penalties as the traffic is doubled compared to the non-resilient scenario. Meanwhile, considering geographical constraints for server protection at low demands resulted in high network energy penalty as more nodes are used to host the processing servers. Interestingly, increasing the resilience level to consider network protection with link and node disjoints selection at high demand resulted in low network energy penalty due to the activation of a large part of the network in any case to serve the demands. The results also reveal that the network energy penalty was reduced when more processing servers are allowed at each fog node while the same processing energy is consumed regardless of the increased resilience level. A heuristic was developed for each resilience scenario for verification and to enable real-time operation of the network, servers and IoT devices, and the results of the heuristic approach those of the MILP.

Resilient Energy Efficient IoT Infrastructure with Server and Network Protection for Healthcare Monitoring Applications