A Power Efficient IoT Edge Computing Solution for Cooking Oil Recycling

This paper presents an efficient, battery-powered, low-cost, and context-aware IoT edge computing solution tailored for monitoring a real enterprise cooking oil collecting infrastructure. The presented IoT solution allows the collecting enterprise to monitor the amount of oil deposited in specific barrels, deployed country-wide around several partner restaurants. The paper focuses on the specification, implementation, deployment and testing of ESP32/ESP8266-based end-node components deployed as an edge computing monitoring infrastructure. The achieved low-cost solution guarantees more than a year of battery life, reliable data communication, and enables automatic over-the-air end-node updates. The open-source software libraries developed for this project are shared with the community and may be applied in scenarios with similar requirements.

[1]  Giuseppe Iannaccone,et al.  Low-Power Wearable ECG Monitoring System for Multiple-Patient Remote Monitoring , 2016, IEEE Sensors Journal.

[2]  Carsten Bormann,et al.  The Constrained Application Protocol (CoAP) , 2014, RFC.

[3]  Veronica Idalia Rosa,et al.  A Low-cost IoT System for Environmental Pollution Monitoring in Developing Countries , 2019, 2019 MIXDES - 26th International Conference "Mixed Design of Integrated Circuits and Systems".

[4]  Alexander Lagerqvist,et al.  IoT Latency and Power consumption : Measuring the performance impact of MQTT and CoAP , 2018 .

[5]  Carlos Pereira,et al.  Assessing the ESP8266 WiFi module for the Internet of Things , 2018, 2018 IEEE 23rd International Conference on Emerging Technologies and Factory Automation (ETFA).

[6]  Trong Nhan Le,et al.  Smart-Config Wifi Technology Using ESP8266 for Low-Cost Wireless Sensor Networks , 2018, 2018 International Conference on Advanced Computing and Applications (ACOMP).

[7]  Mahmoud Meribout,et al.  Design and Implementation of a New Nonradioactive-Based Machine for Detecting Oil–Water Interfaces in Oil Tanks , 2007, IEEE Transactions on Instrumentation and Measurement.

[8]  Khurram Shahzad,et al.  A comparative study of in-sensor processing vs. raw data transmission using ZigBee, BLE and Wi-Fi for data intensive monitoring applications , 2014, 2014 11th International Symposium on Wireless Communications Systems (ISWCS).

[9]  Sajal K. Das,et al.  A survey on fog computing for the Internet of Things , 2019, Pervasive Mob. Comput..