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.

Recent advances in wireless communication technologies are having a great impact on people’s life, especially in the field of healthcare industry that provides a remote monitoring enabling medical staff to follow up the patient and diagnose his disease. LoRa wireless technology is becoming one of the most energetic effective solutions for Wireless Body Area Networks (WBANs) communications. In this work, we propose a LoRa-based low power healthcare WBAN platform called HeaLoRa for an adaptive patient monitoring process. Physicians can remotely monitor patient’s temperature, oxygen saturation level, blood pressure and heart rate to prevent any abnormality. Moreover, data acquisition and transmission are controlled based on a power consumption optimization strategy aiming to reduce redundant data. Based on the Early Warning Score (EWS), the system configuration is dynamically adapted, using a Fuzzy logic controller that makes the decision about the system sleep mode duration and the sent data rate. Furthermore, an analytical model of the energy consumption for acknowledged LoRa transmission is presented in this paper. The graphical abstract of the paper illustrates a medical application of a wireless body area network system where LoRa technology is used to transmit the data to a gateway then the data is transmitted to the doctor using IP based technology. By comparing with a reference system, the simulation results indicate that our system consumes 3 to 10 times less of energy depending on the studied scenario.

LPWAN technologies such as LoRa are widely used for the deployment of IoT applications, in particular for use cases requiring wide coverage and low energy consumption. To minimize the maintenance cost, which can become significant when the number of sensors deployed is large, it is essential to optimize the lifetime of nodes, which remains an important research topic. For this reason, it is necessary that it is based on a fine energy consumption model. Unfortunately, many existing consumption models do not take into account the specifications of the LoRaWAN protocol. In this paper, a refined energy consumption model based on in-situ measurements is provided for a LoRaWAN node. This improved model takes into account the number of nodes in the network, the collision probability that depends on the density of sensors, and the number of retransmissions. Results show the influence of the number of nodes in a LoRaWAN network on the energy consumption of a node and demonstrate that the number of sensors that can be integrated into a LoRaWAN network is limited due to the probability of collision.

One of the key issues with the Internet of Things (IoT) is energy consumption. This type of network is mainly composed of connected objects supplied by an unreliable power source. Wi-Fi HaLow is one of the telecommunication technologies that proposes to connect IoT objects by providing energy optimization mechanisms. It allows the integration of a full IP stack and the implementation of recent security mechanisms. In this article, we introduce power consumption measurements performed on the first device available on the market. In our study, we integrate the current consumed by the power amplifier in transmission phase, and we qualify the power-saving mechanisms. We then propose an analytical model to determine the energy consumed by a sensor node depending on the carried payload. Finally, we validate the use of this technology in IoT application in terms of energy consumption.