Create README.md

README.md

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+---
+language:
+- en
+license: cc-by-4.0
+task_categories:
+- time-series-forecasting
+tags:
+- wifi-sensing
+- csi
+- indoor-localization
+- occupancy-detection
+- esp32
+- smart-home
+- indoor-sensing
+pretty_name: "Office Localization — WiFi CSI Indoor Localization (Office)"
+size_categories:
+- 1M<n<10M
+---
+
+# Office Localization — WiFi CSI Indoor Localization (Office Environment)
+
+## Dataset Description
+
+**Office Localization** is a WiFi Channel State Information (CSI) dataset for zone-level indoor localization and occupancy region detection, collected in an office environment using two ESP32-C6 microcontrollers operating as commodity 802.11n access points. It contains **4 region/occupancy classes** recorded across **2 temporal sessions per class**, totaling approximately **1.6 million CSI packets** and **~124 minutes** of continuous recording.
+
+This dataset is part of the research paper:
+
+> **WiFi Sensing-Based Human Activity Recognition For Smart Home Applications Using Commodity Access Points**
+> Gad Gad, Iqra Batool, Mostafa M. Fouda, Shikhar Verma, Zubair Md Fadlullah
+> IEEE, 2026
+
+📄 [Paper](https://gadm21.github.io/WifiSensingESP32HAR/IEEE_2026__wifi_sensing_.pdf) · ⚡ [GitHub](https://github.com/gadm21/WifiSensingESP32HAR) · 🌐 [Project Page](https://gadm21.github.io/WifiSensingESP32HAR/)
+
+## Region / Occupancy Classes
+
+| Label | Description |
+|-------|-------------|
+| `empty` | No human present in the sensing area |
+| `one` | Person present in Zone 1 of the office |
+| `two` | Person present in Zone 2 of the office |
+| `five` | Person present in Zone 5 of the office |
+
+The zone labels correspond to distinct spatial regions within the office. The task is to determine **where** a person is located (or if the room is empty) based solely on how their body perturbs the WiFi channel between the transmitter and receiver.
+
+## Collection Setup
+
+| Parameter | Value |
+|-----------|-------|
+| **Hardware** | 2 × ESP32-C6 (TX: AP mode, RX: STA mode) |
+| **WiFi Standard** | 802.11n, 20 MHz bandwidth, HT-LTF |
+| **Subcarriers** | 64 total (52 LLTF data subcarriers extracted) |
+| **Packet Rate** | ~200 packets/sec (irregular, resampled to 150 Hz) |
+| **Transport** | UART serial @ 115200 baud |
+| **Environment** | Office room with desks, chairs, and typical office furniture |
+| **TX–RX Distance** | ~3 meters, line-of-sight |
+| **Recorded** | October 2025 |
+
+## Data Organization
+
+| File | Label | Split | Approx. Packets |
+|------|-------|-------|-----------------|
+| `empty_1.csv` | empty | Train | ~210K |
+| `empty_2.csv` | empty | Test | ~210K |
+| `five_1.csv` | five | Train | ~150K |
+| `five_2.csv` | five | Test | ~150K |
+| `one_1.csv` | one | Train | ~150K |
+| `one_2.csv` | one | Test | ~150K |
+| `two_1.csv` | two | Train | ~150K |
+| `two_2.csv` | two | Test | ~150K |
+
+**Split strategy**: File-based temporal holdout. The first recording session per label is used for training and the second for testing. This ensures the model generalizes to temporally distinct data collected at a different time.
+
+## CSV Format
+
+Each CSV file contains one row per received CSI packet with the following columns:
+
+| Column | Description |
+|--------|-------------|
+| `type` | Packet type (always `CSI_DATA`) |
+| `seq` | Sequence number / local timestamp |
+| `mac` | Transmitter MAC address |
+| `rssi` | Received Signal Strength Indicator (dBm) |
+| `rate` | PHY rate index |
+| `noise_floor` | Noise floor estimate (dBm) |
+| `fft_gain` | FFT gain applied by hardware |
+| `agc_gain` | Automatic Gain Control value |
+| `channel` | WiFi channel number |
+| `local_timestamp` | ESP32 local timestamp (µs) |
+| `sig_len` | Signal length |
+| `rx_state` | Receiver state |
+| `len` | CSI data length (128 = 64 subcarriers × 2 components) |
+| `first_word` | Header word |
+| `data` | Raw CSI data as `[I₀, Q₀, I₁, Q₁, ..., I₆₃, Q₆₃]` — 128 signed integers representing in-phase and quadrature components for 64 subcarriers |
+
+## Recommended Preprocessing Pipeline
+
+1. **Load** CSV and parse the `data` column into complex I/Q arrays
+2. **Select** 52 LLTF subcarriers (discard guard/null subcarriers)
+3. **Resample** to a uniform 150 Hz sample rate (original rate is irregular ~100–200 Hz)
+4. **Feature extraction**: Rolling variance with window W ∈ {20, 200, 2000} (recommended: W=200)
+5. **Windowing**: Segment into fixed-length windows (e.g., 100 samples = 0.67s at 150 Hz)
+
+## Benchmark Results
+
+Best results from the paper using rolling-variance features (W=200):
+
+| Classifier | Accuracy |
+|-----------|----------|
+| Random Forest | 89.1% |
+| XGBoost | 88.6% |
+| Conv1D | 95.7% |
+| CNN-LSTM | 96.7% |
+| PCA + KNN | 84.1% |
+
+Office Localization achieves excellent results with deep learning models, demonstrating that commodity WiFi CSI can perform zone-level indoor localization without any dedicated infrastructure — just two off-the-shelf ESP32-C6 boards.
+
+## Use Cases
+
+- **Smart building management**: Automatically determine which zones are occupied
+- **Energy optimization**: Zone-aware HVAC and lighting control
+- **Elderly care**: Non-intrusive monitoring of movement between rooms/zones
+- **Security**: Detect unauthorized presence in restricted zones
+
+## License
+
+This dataset is released under the [CC BY 4.0](https://creativecommons.org/licenses/by/4.0/) license.