Datasets:
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 · ⚡ GitHub · 🌐 Project Page
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
- Load CSV and parse the
datacolumn into complex I/Q arrays - Select 52 LLTF subcarriers (discard guard/null subcarriers)
- Resample to a uniform 150 Hz sample rate (original rate is irregular ~100–200 Hz)
- Feature extraction: Rolling variance with window W ∈ {20, 200, 2000} (recommended: W=200)
- 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 license.