Building Network-Level Ad Blocking with ESP32 Microcontrollers

A fascinating project just hit the top of Hacker News: an ESP32-C3-based ad blocking solution that runs entirely on a $5 microcontroller. While Pi-hole has dominated the network-level ad blocking space for years, this lean approach demonstrates something every IT professional should understand—you don’t need a full Linux stack to perform sophisticated DNS filtering. The ESP32-C3 Adblock project proves that embedded systems can handle real network infrastructure tasks with minimal power draw and maximum efficiency.

This isn’t just a novelty hack. It’s a window into practical DNS manipulation, embedded networking, and the kind of resource-constrained problem-solving that translates directly to IoT deployments, edge computing, and infrastructure optimization. Let’s dig into how network-level ad blocking actually works, why the ESP32 makes sense for this task, and how you can implement similar DNS filtering techniques in your own environment.

How DNS-Based Ad Blocking Actually Works

Before you can appreciate what makes the ESP32 implementation clever, you need to understand the fundamental mechanism. When your browser requests ads.example.com, it first queries a DNS server to resolve that hostname into an IP address. Network-level ad blockers intercept this process, becoming the authoritative DNS server for your local network.

The blocker maintains a list of known advertising and tracking domains. When a query matches one of these domains, instead of returning the legitimate IP address, it returns either 0.0.0.0 or 127.0.0.1—addresses that go nowhere. The browser receives what looks like a valid DNS response, attempts to connect, fails immediately, and moves on. No bandwidth wasted downloading ads, no tracking pixels loaded, no third-party scripts executed.

This approach has distinct advantages over browser-based blocking. It works for every device on your network—smart TVs, mobile apps, IoT devices—without requiring individual configuration. It operates at the network layer, meaning applications can’t detect or circumvent it the way they can browser extensions. For IT professionals managing home labs or small office networks, it’s infrastructure-level control.

Why the ESP32 Makes Sense for Network Services

The ESP32 family has become the go-to platform for embedded networking projects, and for good reason. The ESP32-C3 variant features a RISC-V processor, built-in WiFi, enough RAM to handle network buffers, and crucially—extremely low power consumption. Unlike a Raspberry Pi pulling 2-5 watts, an ESP32 can run this workload on under 500mW.

For IT professionals, this opens interesting deployment scenarios. Edge locations where power is constrained, always-on services where electricity costs matter, or redundant DNS filtering that doesn’t require full compute resources. The ESP32 can run for days on a small battery backup, ensuring your network filtering survives brief power outages without requiring a full UPS.

The hardware constraints also force elegant software design. With only 400KB of RAM and 4MB of flash storage, you can’t just import a bloated library or load millions of domain rules. This teaches the kind of optimization mindset that Coursera embedded systems courses emphasize—every byte counts, every CPU cycle matters, and efficient data structures become mandatory rather than optional.

Implementing DNS Filtering: The Technical Deep Dive

Let’s look at the core DNS server implementation. While the ESP32-C3 Adblock project uses Arduino framework code, the concepts translate to any embedded platform. Here’s a simplified DNS response handler that demonstrates the filtering logic:

// DNS packet handler for ESP32
void handleDNSRequest(WiFiUDP &udp) {
  char packetBuffer[512];
  int packetSize = udp.parsePacket();
  
  if (packetSize) {
    udp.read(packetBuffer, 512);
    
    // Extract queried domain from DNS packet
    String domain = extractDomain(packetBuffer);
    
    // Check against blocklist (stored in flash memory)
    if (isBlocked(domain)) {
      // Return null route response (0.0.0.0)
      sendBlockedResponse(udp, packetBuffer);
    } else {
      // Forward to upstream DNS (e.g., 1.1.1.1)
      forwardToUpstream(udp, packetBuffer, packetSize);
    }
  }
}

The critical performance bottleneck is the isBlocked() function. With blocklists containing hundreds of thousands of domains, a naive linear search would make DNS resolution painfully slow. This is where data structure choice becomes crucial—hash tables, bloom filters, or radix trees all offer different tradeoffs between memory usage, lookup speed, and false positive rates.

For those coming from web development or data science backgrounds, working with these constraints feels different. Unlike the environment you’d encounter in DataCamp courses focused on high-level data manipulation, embedded networking forces you to think about memory alignment, pointer arithmetic, and hardware-specific optimizations. It’s closer to systems programming than application development.

Handling DNS Protocol Details

DNS isn’t just simple request-response. The protocol includes query IDs for matching responses to requests, support for multiple question and answer sections, compression using pointer offsets, and various record types (A, AAAA, CNAME, MX, etc.). Your embedded DNS server needs to handle at minimum A and AAAA records for IPv4 and IPv6 addresses.

// Construct DNS response packet for blocked domain
void sendBlockedResponse(WiFiUDP &udp, char* request) {
  char response[512];
  
  // Copy query ID and flags from request
  memcpy(response, request, 2);  // Transaction ID
  
  // Set response flags: standard query response, no error
  response[2] = 0x81;  // QR=1 (response), Opcode=0, AA=1
  response[3] = 0x80;  // RA=1 (recursion available)
  
  // Questions=1, Answers=1, Authority=0, Additional=0
  response[4] = request[4]; response[5] = request[5];  // QDCOUNT
  response[6] = 0x00; response[7] = 0x01;  // ANCOUNT
  response[8] = 0x00; response[9] = 0x00;  // NSCOUNT
  response[10] = 0x00; response[11] = 0x00;  // ARCOUNT
  
  // Copy question section, add answer pointing to 0.0.0.0
  int qlen = copyQuestion(response + 12, request + 12);
  addNullAnswer(response + 12 + qlen);
  
  udp.write(response, 12 + qlen + 16);
}

Understanding these packet-level details matters for troubleshooting. When DNS resolution mysteriously fails, being able to capture packets with tcpdump and decode the flags, counts, and section layouts separates senior engineers from junior ones who only know how to restart services and hope.

Managing and Updating Block Lists

Static blocklists become stale quickly. New advertising networks emerge, tracking domains rotate, and previously blocked legitimate services need whitelisting. The ESP32’s limited storage means you can’t just download the full EasyList with millions of entries.

Practical approaches include curated compact lists (10,000-50,000 domains covering the most common offenders), aggressive domain wildcarding (blocking *.doubleclick.net instead of individual subdomains), and periodic updates fetched over WiFi. The update process itself requires careful design—you can’t afford to brick the device with a corrupted download.

Versioning your blocklist and implementing rollback capability prevents self-inflicted outages. A configuration option to temporarily disable filtering (returning to straight DNS forwarding) gives you an escape hatch when troubleshooting whether the blocker is causing connectivity issues.

Performance and Memory Constraints

Real-world DNS performance requirements are demanding. Users expect sub-10ms response times for cached results and sub-100ms for upstream forwarding. The ESP32’s 160MHz CPU is plenty fast for packet processing, but memory becomes the limiting factor. Each open UDP socket, DNS query in flight, and network buffer consumes precious RAM.

Profiling becomes essential. The Arduino framework includes tools for tracking heap usage, stack depth, and identifying memory leaks. For production deployments, implement monitoring that tracks query rates, cache hit ratios, upstream timeouts, and memory watermarks. Log anomalies to syslog or a lightweight time-series database.

Consider DNS caching carefully. It multiplies effectiveness by avoiding repeated upstream queries, but each cache entry consumes memory. A simple LRU (Least Recently Used) cache with 100-500 entries provides significant benefit without exhausting resources. TTL (Time To Live) values from upstream responses determine how long entries remain valid.

The beauty of this project is that it combines network administration, embedded programming, protocol understanding, and resource optimization—skills that span IT disciplines. Whether you’re managing corporate infrastructure, building IoT products, or just want cleaner home network traffic, the principles translate directly.