Energy Aware Smart Sensing and Implementation in Green Air Pollution Monitoring System

The field-deployed Internet-of-Things (IoT) sensor nodes are powered by rechargeable batteries. The nodes are equipped with energy harvesters to harvest energy from the environment to replenish the batteries and continue the sensing operations. However, due to the high energy consumption of the power-hungry sensors, the nodes still suffer from energy depletion issues. Towards a green IoT system, an energy aware adaptive sensing algorithm is proposed in this paper. For a multi-sensing node, a learning-aided smart sensing strategy is developed to find a set of optimal sensors to be activated in the next measurement cycle depending on the cross-correlation factors and the sensing energy consumption. The parameters of inactive sensors are predicted from cross-correlated parameters of active sensors using Gaussian process regressor model. Further, the algorithm is implemented in a solar powered air pollution monitoring system to analyze the performance of this method. The proposed method saves 68% energy of the node compared to the nearest competitive method, while the sensing error is within the limit.