| --- |
| 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. |
|
|
