Intelligent Cold Chain Monitoring with Sensor Networks and Edge Computing

Abstract

The efficiency and safety of cold chain logistics play a critical role in industries such as pharmaceuticals, food preservation, and biotechnology. Storage and transportation temperature variations may compromise product quality, leading to considerable loss of money and health hazards. Conventionally, cold chain monitoring tends to be centralized, tardy, and inadequate to support real-time decisions. This chapter describes a harmony between real-time cold chains monitoring, in which sensor networks are joined to gather data scattered across the cold chain, and edge computing to handle and react swiftly. The proposed model ensures that integrated storage/environment is constantly being detected, anomalies are timely identified, and the provided operations do not rely on a distant cloud infrastructure, where performance will be affected by latency and internet failure. The contributions of the chapter comprise an architecture of multi-sensor data fusion on edge nodes, adaptive algorithms of anomaly prediction, and smart communication schemes to achieve optimal bandwidth consumption. A set of practical use cases in food and vaccine supply chains is addressed, as well as the challenges of implementation associated with sensor calibration, energy saving, and scale. The chapter will help the readers get a clear structure of developing next- generation cold chain monitoring systems that are efficient, reliable, and resilient by exposing these three domains of the IoT, sensor technologies, and edge intelligence.