Spatio-Temporal Optimization of the Perishable Goods Shelf Life by Pro-Active WSN-based Architecture

The waste in the supply-chain of the perishable goods have prompted many global organizations (e.g., FAO and WHO), to develop the Hazard Analysis and Critical Control Points (HACCP) protocol that ensures a high degree of food quality, minimizing the losses in all the farm-to-fork chain stages. It has been proven that a good warehouse management improves the perishable goods average life. The advances in wireless sensors network (WSN) technology offer the possibility of a “smart” storage organization. In this paper, a low cost reprogrammable WSN-based architecture for the functional warehouse management is proposed. The management is based on the continuous environmental parameters monitoring (i.e. temperature, light exposition and relative humidity), and on their combination to extract a spatial real-time prediction of the product shelf life. For each product, the quality decay is computed by using a 1st order kinetic Arrhenius model, on the whole storage site area. It ensures to identify, compatibly with the other products shelf lives, the position within the warehouse that maximizes the food expiration date. The shelf life computing and the “first-expired first-out” logistic problem are entrusted to a Raspberry Pi-based central unit, which manages a set of automated pallet transporters for the products displacement, according to the computed shelf lives. The management unit supports several commercial Light/Temperature/Humidity sensors solutions, implementing ZigBee, Bluetooth and HTTP-request interfaces. A proof of concept of the presented pro-active WSN-based architecture is also shown. Comparing the proposed monitoring system for the storage of e.g., agricultural products with a typical one, the experimental results show an improvement of the expected expiration date of about 1.2±0.5 days, for each pallet, when placed in a not refrigerated environment. In order to stress the versatility of the WSN solution, a section is dedicated to the implemented system user interfaces that highlight detecting critical situations and allow timely automatic or human interventions, minimizing the latter.