Reliable Data Collection in IoT Sensor Networks using Intelligent Proxy Architecture

Abstract

Internet of Things (IoT) deployments face critical reliability challenges in production environments, where message loss often goes undetected until significant data gaps emerge. Traditional monitoring approaches fail to provide real-time visibility into message loss patterns, making root cause analysis difficult in networks with intermittent connectivity. This paper presents a comprehensive survey of reliability mechanisms for IoT sensor networks and proposes IoTProxyGuard, an intelligent proxy architecture that guarantees message delivery while providing forensic capabilities for debugging production issues. I introduce novel techniques for message loss detection, gap analysis, and automated alerting. My framework combines sequence-based tracking, time-window verification, and statistical anomaly detection to identify missing messages in real-time. Through evaluation of production IoT deployments spanning industrial sensors, environmental monitoring, and smart city infrastructure, I demonstrate that IoTProxyGuard reduces undetected message loss by 99.7% while adding minimal overhead (< 5% latency, < 2% bandwidth). The proxy architecture supports multiple transport protocols and provides detailed forensics for debugging connectivity issues, making it suitable for heterogeneous IoT deployments.