The number of connected devices online surpassed 21 billion in 2026, and the data they generate is now the largest single stream on the public web. Smart thermostats, fleet-mounted sensors, mesh routers, medical wearables, and industrial PLCs all publish telemetry to vendor dashboards, status pages, mobile companion apps, and shadow APIs that quietly leak structured data to anyone willing to look. For product teams, security analysts, researchers, and competitive intelligence operators, that stream is gold — and web scraping is the cheapest way to mine it.
This guide walks through how AI-assisted scraping pipelines harvest IoT firmware releases, device telemetry, smart home APIs, industrial sensor dashboards, vendor security advisories, and connected-device catalogs from the open web. Whether you build consumer smart devices, run an MSSP monitoring a million endpoints, compete with industrial automation vendors, or research the IoT attack surface, see the patterns that turn fragmented device data into structured, queryable intelligence.
IoT data is uniquely hard to consume at scale because it is multi-vendor, multi-protocol, intermittently offline, geographically distributed, and fragmented across thousands of bespoke vendor portals. The challenge isn't finding connected-device data — the public web is overflowing with it. The challenge is making it usable.
Each source updates on its own cadence — from real-time telemetry on a fleet of refrigeration sensors to monthly firmware rollouts. They use wildly different protocols (MQTT over WebSocket, REST, gRPC-Web, vendor-proprietary JSON), authentication schemes, and licensing terms. No single vendor sells a unified IoT feed. That is exactly the problem web scraping is built for: continuous, structured extraction from heterogeneous device sources.
Security teams and product managers need continuous visibility into which firmware versions are shipping on which devices, when vendors push updates, and what CVEs land in each release. Scraping vendor support pages, signed manifest feeds, and CVE databases keeps your SBOM current and your patching pipeline honest.
# Multi-vendor firmware version harvester
import asyncio
import aiohttp
import re
import json
from datetime import datetime
from dataclasses import dataclass, asdict, field
from typing import List, Optional, Dict
@dataclass
class FirmwareRelease:
vendor: str
model: str
version: str
released_at: str
release_notes_url: str
cve_ids: List[str] = field(default_factory=list)
signed: bool = False
size_mb: Optional[float] = None
async def harvest_vendor_firmware(
session: aiohttp.ClientSession,
vendor: str,
model: str,
release_page_url: str,
) -> List[FirmwareRelease]:
"""Scrape a vendor support page for firmware releases."""
headers = {
"User-Agent": "Mozilla/5.0 (X11; Linux x86_64) AppleWebKit/537.36",
"Accept": "text/html,application/xhtml+xml",
}
async with session.get(release_page_url, headers=headers, timeout=30) as r:
html = await r.text()
releases = []
# Pattern: model + version + release date
pattern = re.compile(
rf"{re.escape(model)}[\s_-]+v?(\d+\.\d+\.\d+(?:\.\d+)?)[\s_-]+(\d{{4}}-\d{{2}}-\d{{2}})",
re.IGNORECASE,
)
for m in pattern.finditer(html):
version, date = m.group(1), m.group(2)
cves = re.findall(r"CVE-\d{4}-\d{4,7}", html)
signed = "signed" in html.lower() or "sha256" in html.lower()
releases.append(
FirmwareRelease(
vendor=vendor,
model=model,
version=version,
released_at=date,
release_notes_url=release_page_url,
cve_ids=sorted(set(cves)),
signed=signed,
)
)
return releases
async def cross_reference_cve_kev(session: aiohttp.ClientSession, releases: List[FirmwareRelease]):
"""For known-exploited CVE IDs, surface them in the output."""
kev_url = "https://www.cisa.gov/sites/default/files/feeds/known_exploited_vulnerabilities.json"
async with session.get(kev_url, timeout=30) as r:
kev = await r.json()
kev_ids = {v["cveID"] for v in kev.get("vulnerabilities", [])}
for rel in releases:
rel.cve_ids = sorted(set(rel.cve_ids))
rel.__dict__["in_kev"] = bool(set(rel.cve_ids) & kev_ids)
return releases
# Example
# async with aiohttp.ClientSession() as s:
# rels = await harvest_vendor_firmware(
# s, "tp-link", "Tapo C200",
# "https://www.tp-link.com/en/support/download/tapo-c200/",
# )
# rels = await cross_reference_cve_kev(s, rels)
# print(json.dumps([asdict(r) for r in rels], indent=2))
Product strategists at smart home platforms need to know which devices integrate with which ecosystems, what capabilities each pairing exposes, and how the partner catalogs shift week to week. Scraping Works with Alexa, Works with Google Home, Matter-certified product registries, and vendor skill catalogs turns a moving target into a queryable graph.
Energy managers, logistics operators, and predictive-maintenance teams need continuous visibility into factory-floor sensors, agricultural probes, and cold-chain refrigerators. Many industrial dashboards publish public or weakly-gated read endpoints that scrapers can poll, normalize, and feed into time-series stores alongside your private telemetry.
Analysts who track the connected-device market need shipment estimates, ASP trends, certification timelines, and vendor partnership churn. Scraping FCC filings, retailer listings, press release archives, and trade-show exhibitor catalogs gives market intelligence teams a continuous read on the competitive landscape without waiting for paid analyst reports.
Threat intelligence teams and red teams continuously scan the internet for exposed IoT devices — default credentials, open Telnet ports, unpatched firmware banners. While they often rely on dedicated platforms like Censys and Shodan, scraping the public mirrors, paste sites, and dark-web-adjacent forums where exposed device lists surface unlocks a defensive edge.
IoT portals are notoriously hard to scrape with traditional selectors. Here is why AI-assisted extraction wins for connected-device data:
The pattern is the same across every IoT source: the layout changes constantly, the data is hidden in JavaScript-rendered dashboards, and traditional scrapers break weekly. AI-powered extraction sends a URL, learns the document, and returns the JSON your pipeline expects — even when the vendor ships a redesign overnight.
Many IoT vendor dashboards (especially white-label Tuya/SmartLife deployments) expose a small set of public read APIs that any logged-in user can hit. Use a headless browser to log in, capture the network traffic on the device-list and telemetry routes, and you'll have a stable JSON spec for a hundred different vendors.
Define a single canonical record per device: {device_id, vendor, model, firmware_version, last_seen_at, online, telemetry: {...}}. Every vendor-specific parser translates to this schema; downstream consumers only ever see canonical records.
When a vendor ships a redesign or a new device class lands, delegate the new extraction to an AI scraping API. Papalily accepts a URL plus a JSON description of the fields you want back, and returns structured records regardless of layout changes.
Connected-device state changes constantly. After every run, diff the new snapshot against the previous one to detect firmware upgrades, new devices, devices going offline, and anomalous telemetry spikes. Delta-driven updates keep your event bus clean.
# Delta detector for IoT snapshots
from collections import defaultdict
from typing import Dict, List, Tuple
def diff_snapshots(prev: Dict, curr: Dict) -> Dict[str, list]:
"""Compare two device snapshots and emit events."""
events = defaultdict(list)
prev_d = {d["device_id"]: d for d in prev["devices"]}
curr_d = {d["device_id"]: d for d in curr["devices"]}
# New devices
for did in curr_d.keys() - prev_d.keys():
events["new_device"].append(curr_d[did])
# Removed devices
for did in prev_d.keys() - curr_d.keys():
events["device_offline"].append(prev_d[did])
# Changed devices
for did in curr_d.keys() & prev_d.keys():
a, b = prev_d[did], curr_d[did]
if a.get("firmware_version") != b.get("firmware_version"):
events["firmware_updated"].append({
"device_id": did,
"vendor": b["vendor"],
"from": a.get("firmware_version"),
"to": b.get("firmware_version"),
})
if a.get("online") != b.get("online"):
events["status_changed"].append({
"device_id": did,
"from": a.get("online"),
"to": b.get("online"),
})
return dict(events)
# Example
# events = diff_snapshots(yesterday_snapshot, today_snapshot)
# for category, items in events.items():
# print(category, len(items), "items")
The same backend (Tuya, SmartLife, Mi Home, Apple HomeKit) powers hundreds of branded apps. Each brand ships its own dashboard with slightly different field names and layouts.
Solution: Pick the canonical IoT platform — usually the white-label backend rather than the consumer-facing brand site — and scrape the underlying API. Fall back to per-brand scrapers only for branded experiences the white-label platform does not expose.
Live IoT data often flows over MQTT, WebSocket, or vendor-proprietary push channels rather than REST. Plain HTTP scraping misses the live stream.
Solution: Combine polling (for state, configuration, and history endpoints) with a lightweight MQTT or WebSocket subscriber (for live telemetry). Most platforms expose both: a REST-friendly history endpoint and a live channel. Build both, merge on device_id and timestamp.
Major IoT platforms deploy bot mitigation that fingerprints headless browsers and rate-limits suspicious traffic, especially on routes that poll millions of devices.
Solution: Combine residential proxy rotation, realistic browser fingerprints, and human-paced request timing. For most teams, it's cheaper to delegate this complexity to a managed scraping API than to maintain proxy infrastructure in-house.
Vendor A publishes JSON, vendor B publishes CSV, vendor C publishes an HTML table, vendor D publishes gRPC-Web binary frames. Each format has its own field names, units, and missing-value markers.
Solution: Define a canonical device schema and map each source to it with a combination of rules and LLM-based classification. Validate mappings against a human-reviewed gold set on a recurring schedule. Standardize units (Celsius, kWh, dBm) at ingestion.
IoT platforms rightly gate telemetry behind user authentication. Scraping logged-in user data without consent crosses legal and ethical lines.
Solution: Stick to public sources: support pages, press releases, certification filings, marketplace listings, vendor catalogs, and community wikis. For authorized personal telemetry, prefer official APIs (Tuya Open API, Philips Hue Remote API, SmartThings Schema) over scraping. Anything in between is a privacy regime you do not want to walk into.
Papalily's AI-powered scraping API turns any IoT or connected-device URL into structured JSON in a single call. Firmware registries, vendor dashboards, smart home catalogs, regulatory filings, telemetry endpoints — one API, one schema, no proxy headaches.
Start a Free Trial →Three trends will reshape how teams collect IoT and connected-device data over the next 24 months:
Web scraping is the connective tissue that lets IoT-driven teams see connected devices as they actually are — not as they appear in last quarter's static dashboard. From firmware releases and vulnerability disclosures to industrial telemetry and smart home ecosystem mapping, automated IoT extraction delivers the breadth, unit discipline, and provenance that manual research cannot match.
The teams that win in 2026 and beyond will treat the open web as a continuously refreshed IoT database. Whether you're a security analyst tracking firmware CVEs, a product manager mapping smart home ecosystems, an industrial operator aggregating sensor telemetry, or a threat intelligence team measuring the IoT attack surface, the right scraping architecture turns external device noise into reliable, queryable intelligence.
Start small: pick one high-value IoT source, build a reliable extractor that handles device-id reconciliation and license attribution, and prove the workflow end-to-end. Then expand the surface area methodically. IoT data is high-leverage and long-lived — the operational advantage compounds quietly but relentlessly.