Router geolocation through signal analysis relies on several measurable parameters:

• Received Signal Strength Indicator (RSSI)
• Signal-to-Noise Ratio (SNR)
• Time of Flight (ToF) measurements
• Angle of Arrival (AoA) techniques

Here's a basic implementation using scapy for WiFi packet analysis:

from scapy.all import *
import numpy as np

def packet_handler(pkt):
    if pkt.haslayer(Dot11):
        if pkt.type == 0 and pkt.subtype == 8:  # Beacon frame
            bssid = pkt.addr2
            rssi = -(256 - ord(pkt.notdecoded[-4:-3]))
            channel = int(ord(pkt.notdecoded[-2:-1]))
            print(f"Found BSSID: {bssid} with RSSI: {rssi} dBm on channel {channel}")

sniff(iface="wlan0mon", prn=packet_handler)

With multiple signal strength measurements, we can estimate position:

import math

def calculate_position(rssi_readings):
    # Known access point positions
    ap_positions = [
        {'x': 0, 'y': 0, 'rssi': -40},
        {'x': 10, 'y': 0, 'rssi': -60},
        {'x': 5, 'y': 8.66, 'rssi': -55}
    ]
    
    # Path loss exponent (typically 2-4 for indoor environments)
    n = 2.5
    
    # Reference distance and signal strength (1m)
    d0 = 1.0
    rssi0 = -30
    
    # Calculate distances
    distances = []
    for ap in ap_positions:
        distance = d0 * 10**((rssi0 - ap['rssi'])/(10 * n))
        distances.append(distance)
    
    # Trilateration calculation would go here
    # (Implementation depends on math library used)
    
    return estimated_position

Several factors affect accuracy:

• Multipath propagation in urban environments
• Signal absorption by building materials
• Dynamic interference from other devices
• Antenna orientation and gain patterns

More advanced methods include:

# Using WiFi fingerprinting with machine learning
from sklearn.neighbors import KNeighborsClassifier

# Sample dataset format:
# [RSSI_AP1, RSSI_AP2, RSSI_AP3, ..., X_coord, Y_coord]

def train_model(fingerprint_data):
    X = fingerprint_data[:, :-2]  # RSSI values
    y = fingerprint_data[:, -2:]   # Coordinates
    
    model = KNeighborsClassifier(n_neighbors=3)
    model.fit(X, y)
    return model

Important caveats for developers:

• Check local laws regarding wireless signal monitoring
• Never attempt to geolocate networks without permission
• Secured networks should not be targeted
• Consider privacy implications of location data

Determining a wireless router's physical location (latitude/longitude) using only its broadcast signal is technically possible but comes with significant limitations. The process typically involves analyzing signal strength (RSSI), MAC address, and other available network characteristics.

There are several methods to estimate a router's location:

• Trilateration using signal strength from multiple measurement points
• Fingerprinting techniques comparing against known location databases
• Time-of-flight measurements (though this requires specialized hardware)

Here's a basic Python script that demonstrates signal strength collection:

import subprocess
import re

def get_wifi_signal_strength():
    try:
        scan_output = subprocess.check_output(["netsh", "wlan", "show", "network", "mode=bssid"])
        networks = []
        
        for line in scan_output.decode('utf-8').split('\n'):
            if 'SSID' in line:
                ssid = line.split(':')[1].strip()
            elif 'Signal' in line:
                signal = int(re.search(r'\d+%', line).group()[:-1])
                networks.append({'SSID': ssid, 'Signal': signal})
        
        return networks
    except Exception as e:
        print(f"Error: {e}")
        return []

if __name__ == "__main__":
    networks = get_wifi_signal_strength()
    for network in networks:
        print(f"{network['SSID']}: {network['Signal']}%")

Several factors affect geolocation accuracy:

• Signal strength varies with environmental conditions
• Obstacles like walls significantly impact readings
• Most consumer devices lack precise signal measurement capabilities
• Without multiple measurement points, accuracy is limited

For more accurate results, consider:

# Using existing geolocation databases (example with WiGLE API)
import requests

def query_wigle(api_key, mac_address):
    url = f"https://api.wigle.net/api/v2/network/detail?netid={mac_address}"
    headers = {'Authorization': f'Basic {api_key}'}
    
    try:
        response = requests.get(url, headers=headers)
        if response.status_code == 200:
            return response.json()
        return None
    except Exception as e:
        print(f"API Error: {e}")
        return None

When implementing router geolocation:

• Respect privacy laws and network usage policies
• Understand that MAC addresses can be randomized in modern devices
• Consider using professional tools like Ekahau for enterprise solutions