Energy efficiency is the key requirement to maximize sensor node lifetime. Sensor nodes are typically powered by a battery source that has finite lifetime. Most Internet of Thing (IoT) applications require sensor nodes to operate reliably for an extended period of time. To design an autonomous sensor node, it is important to model its energy consumption for different tasks. Each task consumes a power consumption amount for a period of time. To optimize the consumed energy of the sensor node and have long communication range, Low Power Wide Area Network technology is considered. This paper describes an energy consumption model based on LoRa and LoRaWAN, which allows estimating the consumed power of each sensor node element. The definition of the different node units is first introduced. Then, a full energy model for communicating sensors is proposed. This model can be used to compare different LoRaWAN modes to find the best sensor node design to achieve its energy autonomy.

Solar energy harvesting constitutes an attractive solution to provide energy for communicating objects. The advantage of this energy over other forms of renewable energy is that the available solar power can be predicted with reasonable accuracy, which allows the implementation of efficient power management techniques. Nevertheless, harvested solar energy comes from a non‐controllable source. Therefore, the prediction of the solar radiation and energy availability is a critical issue, as the amount of the harvested energy may vary over time. In this study, a novel solar radiation and energy predictor (Solar Energy Predictor for Communicating Sensors: SEPCS) is proposed. This prediction model uses past energy observations to forecast future energy availability in short term. To assess the performance of the proposed algorithm, authors used database providing the solar radiation evolution for one year. Then, a comparative performance evaluation shows that the SEPCS predictor significantly outperforms the state‐of‐the‐art energy predictors, by decreasing the average prediction error from 28 to 6.5%.