This study presents a comprehensive design and implementation of an ESP32-based WiFi packet sniffer system optimized for real-time monitoring and analysis of 802.11 network traffic, adopting a modular architecture that integrates hardware abstraction, packet processing, and user interface layers to ensure scalability, maintainability, and efficient resource utilization while leveraging the ESP32's dual-core processor, integrated WiFi, and advanced memory management strategies to achieve high performance in packet capture, filtering, and storage with low power consumption; key features include support for industry-standard PCAP file formats, microsecond-precision timestamps, and compatibility with MQTT and WebSocket protocols, enabling seamless integration into diverse IoT applications, with the system capable of capturing all supported 802.11 frame types and applying real-time filtering based on MAC addresses, frame types, signal strength, and protocol-specific parameters to reduce storage and processing overhead, while addressing memory limitations through efficient buffer management techniques such as circular buffers and dynamic memory allocation, ensuring adaptability to traffic patterns and resources, with performance evaluations demonstrating its ability to handle high traffic loads with minimal latency and memory overhead, making it suitable for applications like disaster monitoring, factory automation, and environmental sensing, further highlighting the ESP32's versatility through compatibility with solar-powered systems and renewable energy sources for long-term deployment in remote environments, while future work will focus on enhancing energy efficiency and exploring AI-driven analytics for edge computing scenarios, bridging the gap between expensive commercial solutions and accessible educational/research tools to demonstrate practical viability in real-world applications.
Sanantagraha et al. (Sat,) studied this question.