- Components: Edge devices and Edge nodes
- Workflow:
- Each device collect vector.
- Each device generate fingerprints for each vector and find outliers
- Upload fingerprint to the nearest node.
- Nodes collect fingerprints from all its devices.
- Interacts with all nodes on the network and derive the support devices for all its edge devices
- Based on the information from edge node, each device ask its dependent devices for necessary vector
- After receiving all vector, run the rest streaming detecting algorithm
Name | # vector points | # Dim | k | r | s | w | Size | Link |
---|---|---|---|---|---|---|---|---|
GAU(gauss) | 1M | 1 | 50 | 0.028 | 5000 | 100000 | 7.74MB | link |
STK | 1.05M | 1 | 50 | 0.45 | 5000 | 100000 | 7.57MB | link |
TAO | 0.58M | 3 | 50 | 1.9 | 500 | 10000 | 10.7MB | link |
HPC | 1M | 7 | 50 | 6.5 | 5000 | 100000 | 28.4MB | link |
GAS | 0.93M | 10 | 50 | 2.75 | 5000 | 100000 | 70.7MB | link |
EM | 1M | 16 | 50 | 115 | 5000 | 100000 | 119MB | link |
FC | 1M | 55 | 50 | 525 | 500 | 10000 | 72.2MB | link |
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Cluster the datasets, and assign the different clusters to different devices (case 1)
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Cluster the datasets, and distribute the same cluster to different devices (case 2)
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Cluster the datasets, and mix a% vector of each cluster then assign the different clusters to different devices
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Expected result:
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No transfer between devices in case 1
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All devices exchange vector in case 2
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Other cases lie in between
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original datasets: \NETS\Datasets
vector with device ID: \NETS\Datasets\DeviceId _data
vector with timestamp: \NETS\Datasets\Timestamp_data
注意:若每次重新准备数据,需要删除DeviceId _data和Timestamp_data下的文件夹
- 修改 NETS\src\utils\Constants.java的参数
- 运行 \utils\PrepareDatasets.java (for the first time only)
- 运行 \test\testNetwork.java
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生成k_means的datasets:
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生成random cluster vector
- utils\GenerateRandomClusteringData.java 修改参数生成特定的cluster
- 现在统一命名成RC,后期可根据需要修改
- constants.java 中的参数也要对应修改
- 接着运行run Original Datasets下的指令
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subdim
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每个维度的最大值最小值 this.maxValues, this.minValues
- random cluster需要运行 utils\decideMaxMin.java,再修改NewNETS中的值
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维度的优先级:this.priorityList