Abstract
The Internet of Things (IoT) has seen a surge in mobile devices with the market and technical expansion. IoT networks provide end-to-end connectivity while keeping minimal latency. To reduce delays, efficient data delivery schemes are required for dispersed fog-IoT network orchestrations. We use a Spark-based big data processing scheme (BDPS) to accelerate the distributed database (RDD) delay efficient technique in the fogs for a decentralized heterogeneous network architecture to reinforce suitable data allocations via IoTs. We propose BDPS based on Spark-RDD in fog-IoT overlay architecture to address the performance issues across the network orchestration. We evaluate data processing delays from fog-IoT integrated parts using a depth-first-search-based shortest path node finding configuration, which outperforms the existing shortest path algorithms in terms of algorithmic (i.e., depth-first search) efficiency, including the Bellman–Ford (BF) algorithm, Floyd–Warshall (FW) algorithm, Dijkstra algorithm (DA), and Apache Hadoop (AH) algorithm. The BDPS exhibits low latency in packet deliveries as well as low network overhead uplink activity through a map-reduced resilient data distribution mechanism, better than in BF, DA, FW, and AH. The overall BDPS scheme supports efficient data delivery across the fog-IoT orchestration, outperforming faster node execution while proving effective results, compared to DA, BF, FW and AH, respectively.
Highlights
The growing Internet of Things (IoT) network [1,2,3], end-to-end packet distribution, finds mesh networks for end devices, along with short-range communication [4,5,6]
For rapid data delivery and efficient in-memory scheduling across the network ends, a low latency-based data distribution scheme is still needed for fog-IoT overlay networks
We propose big data processing scheme (BDPS), a delay optimization scheme to run with in-memory Spark processing to reduce the Apache Hadoop [34] extra delay problem for the verification, validation, and execution of files caused by distribution of data processing
Summary
The growing IoT network [1,2,3], end-to-end packet distribution, finds mesh networks for end devices, along with short-range communication [4,5,6]. For efficient data delivery at the IoT hop ends, a resilient data distribution-based mapping mechanism [22] is a better choice, and fog service deliverables (i.e., gateways, and microservice extenders) act here as a beneficiary helper for the three-tier architecture [23]. For rapid data delivery and efficient in-memory scheduling across the network ends, a low latency-based data distribution scheme is still needed for fog-IoT overlay networks. Deploying the Spark framework activity through resilient distributed data can help to speed up deliveries of data within the overlay network and improve network efficiency by reducing the extended delay problem on mesh interconnections at the hop-to-hop ends. We implement the delay efficient high-speed data processing algorithmic scheme for a hierarchical tree-based overlay mesh architecture in the cloud–fog and IoT ecosystem.
Sign-in/Register to view highlights & summary Sign-in/Register to access full text options 
Free) 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a call
