gadgadgad/OfficeLocalization

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

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 license.