Context-aware inference model for cold-chain logistics monitoring

Monitoring in logistics has been a big concern in supply chain management, especially in the temperature-controlled supply chain or cold-chain. Perishable goods, like fruits and vegetables, really need real-time monitoring during storage and transport period for quality assurance. In ubiquitous computing era like now, there are literally hundreds or even thousands sensors deployed in logistics operation. A huge amount of data and numerous kinds of data will be generated by this wireless sensor network (WSN). It is becoming important to make an inference from these data to deliver relevant information to user. This paper addresses context-aware inference model for logistics monitoring. Context-aware inference model can combine multi-sensor data from wireless sensor network (WSN) and RFID implemented to deliver relevant information to user. The context-aware inference model designed has to be easy to configure, fast to process, and support high traffic of data but also can adapt with change and adversary that may happen.