Location-specific optimization of energy harvesting environmental monitoring systems

Environmental sensing is necessary for air quality monitoring, assessment of ecosystem health, or climate change tracking. Environmental monitoring systems can take a form of standalone monitoring stations or networks of individual sensor nodes with wireless connectivity. The latter approach allows high resolution mapping of spatiotemporal characteristics of the environment. To allow their autonomous operation and to minimize their maintenance costs, such systems are often powered using energy harvested from the environment itself. Due to the scarcity and intermittency of the environmental energy, operation of energy harvesting monitoring systems is not a trivial task. Their sensing, transmitting, and housekeeping activities must be carefully managed to extend their lifetime while providing desired quality of service. As the environmental conditions change with the region of deployment, the strategies for energy management must change accordingly to match the energy availability. In this work, we examine how geographic location affects the operations and quality of data collected by a solar-powered monitoring system. In particular, we use node/network simulation tools to follow the performance of energy-harvesting environmental monitoring sensor nodes at different latitudes, from equator to the pole. Static parameters of the simulated sensor nodes are determined for each latitude using an intelligent optimization method. The results show a clear dependence of the monitoring system performance on its deployment location. This encourages location-specific optimization of sensor node properties and parameters.