A Dynamic Plane Prediction Method Using the Extended Frame in Smart Dust IoT Environments
Abstract
:1. Introduction
2. Related Works
2.1. DPDK (Dual Plane Development Kit)
2.2. A High-Performance Implementation of an IoT System Using DPDK
3. The Dynamic Plane Partition Algorithm
3.1. System Overview
3.2. Software Configuration of the System
3.3. A Prediction Algorithm for the Dynamic Plane
- (1)
- Data of variable size must be fixed to pass through the ANN, resulting in overhead.
- (2)
- Data of a fixed size can distinguish the kind of packet by whether the specific byte is set without a full parsing of the packet.
- (3)
- Batching multiple pieces of data together rather than processing a single piece of data has an overall processing speed advantage.
- The Cell is the minimum space to hold a packet.
- The Permuted Frame (PF) is a set of Cells with a Ground Truth (GT).
- The eXtended Permuted Frame (XPF) is a set of PFs to contain multiple PF patterns.
Example of XPF Generation Process
4. Experiment
4.1. Experimental Materials
4.2. Environments
- A dual environment that has both data and control plane on one processing node
- A static partition environment in which one processing node has only one plane (data or control plane) at a moment
- A rated static partition environment that has only one plane (data or control plane) at a moment and the ratio of planes at the same rate as the packets being generated in the system.
4.3. Experimental Results
- (1)
- The dynamic partition environment shows about 88% the performance of the rated static partition environment (12% lower than the rated static partition environment).
- (2)
- The dynamic partition environment shows about 16% higher performance compared to the static partition environment.
- (3)
- The dynamic partition environment shows about 72% higher performance compared to the dual environment.
4.3.1. Comparison with the Dual Environment
4.3.2. Comparison with the Static Partition Environment
4.3.3. Comparison with the Rated Static Partition Environment
5. Conclusions
Author Contributions
Funding
Conflicts of Interest
References
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Observation Point | Time | Temperature (°C) | Humidity (%) | Vapor Pressure (hPa) | Dew Point Temperature (°C) | Sunshine (h) | Solar Radiation (MJ/m2) | Ground Temperature (°C) |
---|---|---|---|---|---|---|---|---|
Daegu(143) | 25 January 2020 8:00 | 6 | 77 | 7.2 | 2.2 | 0 | 0.03 | 3.9 |
Component | Item |
---|---|
CPU | i7-4770 3.4GHz |
Main memory | 16.0 Gb |
GPU | NVIDIA GeForce GTX 1050 3Gb X 2 |
OS | Ubuntu 16.04 |
Component | Item |
---|---|
CPU | i7-4770 3.4GHz |
Main memory | 32.0 Gb |
GPU | NVIDIA GeForce GTX 1050 3Gb |
OS | VM Ware (Ubuntu 16.04) |
Docker(Ubuntu 16.04) |
Rd (%) | Dual Env. | A Static Partitioning Env. (5:5) | A Rated Static Partitioning Env. | A Dynamic Partitioning Env. |
---|---|---|---|---|
10 | 4,062,976 | 4,491,648 | 7,284,352 | 6,315,520 |
20 | 3,948,544 | 5,098,496 | 7,313,664 | 6,384,768 |
30 | 3,800,448 | 5,709,440 | 7,335,552 | 6,675,328 |
40 | 3,535,360 | 6,444,160 | 7,247,872 | 6,479,488 |
50 | 3,445,888 | 7,306,368 | 7,269,760 | 6,477,312 |
60 | 3,549,184 | 6,517,248 | 7,269,760 | 6,302,848 |
70 | 3,687,552 | 5,891,584 | 7,247,872 | 6,399,744 |
80 | 3,783,424 | 5,084,416 | 7,379,328 | 6,663,424 |
90 | 3,991,424 | 4,560,640 | 7,306,368 | 6,385,664 |
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Park, J.; Park, K. A Dynamic Plane Prediction Method Using the Extended Frame in Smart Dust IoT Environments. Sensors 2020, 20, 1364. https://doi.org/10.3390/s20051364
Park J, Park K. A Dynamic Plane Prediction Method Using the Extended Frame in Smart Dust IoT Environments. Sensors. 2020; 20(5):1364. https://doi.org/10.3390/s20051364
Chicago/Turabian StylePark, Joonsuu, and KeeHyun Park. 2020. "A Dynamic Plane Prediction Method Using the Extended Frame in Smart Dust IoT Environments" Sensors 20, no. 5: 1364. https://doi.org/10.3390/s20051364