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A novel hardware approach with four physical layers and several integrated and add-on sensors for a comprehensive physical and chemical environmental parameter (toxic gases, sound level, air pressure, humidity, temperature, and motion tracking) monitoring is introduced in this paper. To provide flexibility, the system is modular and each sensor functions independently. The whole solution is small, compact, light, and wrist worn. It is working in low power consumption mode and operates for several hours.

The device has two layers to implement the sensors and one layer for a warning system driver to enable the vibrating motor and beeper in emergency status. The forth layer is the hardware flex interface that is connected to the display and sound module and provides the possibility of the hardware extension for further development. The gas sensor node includes the sensor attached to the driver (located at the top) and is replaceable with other target gas sensors from the same family. The warning system is located at the bottom of the proposed device.

The sampled data from the sensors are monitored in real time via the display and are sent to an Android smartphone for permanent storage via Bluetooth Low Energy(BLE) 4.1. Consequently, these data will be directed to a cloud for further medical analyses. Power consumption, results, device efficiency, and packet protocol justification are evaluated in this paper.

Performance Evaluation of Energy-Autonomous Sensors Using Power-Harvesting Beacons for Environmental Monitoring in Internet of Things (IoT)

The waste in the perishable goods supply-chain has 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 stages of the farm-to-fork chain. It has been proven that good warehouse management practices improve the average life of perishable goods. The advances in wireless sensors network (WSN) technology offers the possibility of a “smart” storage organization. In this paper, a low cost reprogrammable WSN-based architecture for functional warehouse management is proposed. The management is based on the continuous monitoring of environmental parameters (i.e., temperature, light exposure 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 to the whole storage site area. It strives to identify, in a way compatible 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 displacement of products, according to the computed shelf lives. The management unit supports several commercial light/temperature/humidity sensor 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 non-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.