Energy-Saving Techniques for Urban Noise WSN With Kalman-Based State Estimation and Green Facade Energy Harvester

Abstract

In this article, a Kalman framework is proposed for dynamic energy-saving in wireless sensor networks used to monitor urban noise pollution. The energy-saving framework implements a dynamic power management strategy (DPMS) with the Kalman algorithm that varies the sensor node’s sleep period according to the measured noise levels. An Internet-of-Things (IoT) edge-based self-sustaining long-range (LoRa) network is developed and used for ubiquitous monitoring and analysis of urban noise pollution. The network consists of a star topology with six LoRa battery-free wireless sensing nodes deployed in Mérida city downtown, each node powered by a green facade structure consisting of an array of plant-microbial fuel cells (P-MFC). The sensor node’s prototype was implemented following Mexican regulations, transmitting the data packets with the open frequency band of 915 MHz, and monitoring the LoRa network from a web page. Experimental results prove a sustainable operation with a green facade P–MFC array power generation of 112.1 mW with an open-circuit voltage of 2.7 V and a short-circuit current of 180 mA. The sensor node’s average power consumption is 11.2 mW; therefore, sufficient energy is generated for continuous monitoring. The efficient Kalman DPMS is also tested with the urban noise measurement estimation and adjusting the sleep period only if the urban measurement state estimation is above the threshold normativity. The system’s low-power consumption allows to perform 70 continuous LoRa transmissions even when the energy harvester source is absent, and the power capacity of the green facade restores a supercapacitor full charge after only 4 min of a LoRa transmission. A 23.6% of energy was saved with Kalman DPMS in comparison with a continuous measurement system of 10-min uniform-sleep period.