# A System-Level Methodology for the Design of Reliable Low-Power Wireless Sensor Networks
While omitting the code generation capability of the tool, a model-based approach for the design of ultra-low power wireless sensor nodes, along with a high-level of abstraction modeling framework based on Stateflow, is also introduced in [36]. This work is a continuation of the latter. Presently, the comprehensive energy and power model can lead to a fast and effective method of designing low-power wireless sensing systems by serving as a guideline for choosing the right COTS components and node configuration. Moreover, given that a SN is intended to operate within a large group of other SNs, having a realistic model for path loss (PL) in order to estimate operating range is of interest. The authors in [11] model the signal PL between two endpoints acting in an open space such as a rural field by taking into account the line-of-sight (LOS) component and the reflected signal due to the ground. In [37], a three-slope log-normal path loss model was proposed in order to model a narrowband radio channel in rural scenarios where the radios operate under near-ground conditions, such as is the case for smart agriculture applications. Moreover, a survey of LOS and non-line-of-sight (NLOS) wireless PL models is presented in [38]. For LOS links, a fitting factor is added to the free-space PL in an attempt to avoid underestimations. In this work, we proposed a PL model that more accurately estimates signal attenuation in the sub-1 GHz ISM band. Based on previous works in the literature [11; 38] and field measurements, it takes into account the free-space PL, ground reflection, and a fitting factor with a variable coefficient. In addition to accurate energy and range estimations, this work also focuses on finding the energy-latency-reliability trade-off, which is very important in WSN applications since it captures the interdependence of key parameters from a quality of service (QoS) point of view. To ensure reliable data communication, automatic repeat request (ARQ) protocols [39] and forward error correction (FEC) techniques [40; 41], or a combination of both [42; 43; 44], are mostly used. In this analysis, a simple point-to-point communication link is considered. Readers can consult [8; 42] in order to further understand the effects of the used medium access protocol, multi-hop routing, and the broadcast nature of the WSN, which are beyond the scope of this work. Moreover, recently, research on limiting energy consumption while meeting stringent QoS requirements has taken a considerable leap, especially in industrial automation applications [5; 8; 45]. Work in [46] investigates the adaptive data rate algorithm implemented in long range wide area network (LoRaWAN) and its theoretical bounds of link and network capacity. The work explains how the data rate is dynamically adjusted such that a node close to the gateway would use a small spreading factor in order to increase the raw data rate and be able to decrease latency and radio output power. Therefore, the closest node to the gateway transmits with the maximum data date and lowest output power. In LoRaWAN compliant devices, the adaptive rate-power allocation is based on received signal strength indicator (RSSI) and signal-to-noise readings of the last received packets of static devices. As it will be demonstrated in this work, the studied power/data rate allocation technique used in LoRaWAN can drastically enhance the link performance and energy efficiency. While considering that, in harsh industrial application scenarios, factors such as transmission power level, communication range, and random ambient noise affect radio link quality, a network-level reliability model for estimating and increasing the reliability performance and deployment parameters of industrial WSN is presented in [5]. The work suggests a new approach where nodes measure and estimate link parameters, such as the packet reception ratio (PRR) and received signal strength (RSS), and then optimize the lower-bound reliability value.