Energy Harvesting Models in Urban Environmental IoT Deployments

Abstract

Urban Environmental Internet of Things (E-IoT) deployments—such as air-quality monitors, noise meters, parking sensors, and smart streetlights—are often limited by battery maintenance and grid dependence. Energy harvesting (EH) can transform these constraints by enabling “energy-neutral” operation where nodes harvest, store, and intelligently spend energy to match workload demands. This manuscript develops a unified, deployment-oriented view of EH models for cities, covering solar (outdoor/indoor), wind microturbines, thermoelectric gradients on building facades and manholes, vibration/piezo sources along roads/bridges, and ambient RF from cellular/Wi-Fi infrastructure. We formulate source-specific power models, storage dynamics, and power-management policies, and integrate them into a cross-layer methodology for scheduling, duty-cycling, and link adaptation.

A simulation study for a 1 km² downtown district (500 nodes) compares a conventional battery-only baseline with a hybrid EH design using supercapacitors and model-predictive scheduling. Results indicate that 86% of nodes achieve energy neutrality over 120 simulated days, packet delivery ratio (PDR) increases by 9.7% on average, and expected battery replacements fall by 92%. Statistical testing confirms significant improvements in PDR and lifetime while maintaining application-level latency constraints. Sensitivity analyses show robustness to seasonal irradiance, wind variability, and RF density. The work provides a practical blueprint—models, parameters, and algorithms—for planners seeking to scale urban E-IoT with minimal maintenance and improved sustainability.