Wireless sensor networks (WSNs) have been adopted in many fields of application, such as industrial, civil, smart cities, health, and the surveillance domain, to name a few. Fateway and sensor nodes conform to WSN, and each node integrates processor, communication, sensor, and power supply modules, sending and receiving information of a covered area across a propagation medium. Given the increasing complexity of a WSN system, and in an effort to understand, comprehend and analyze an entire WSN, different metrics are used to characterize the performance of the network. To reduce the complexity of the WSN architecture, different approaches and techniques are implemented to capture (model) the properties and behavior of particular aspects of the system. Based on these WSN models, many research works propose solutions to the problem of abstracting and exporting network functionalities and capabilities to the final user. Modeling an entire WSN is a difficult task for researchers since they must consider all of the constraints that affect network metrics, devices and system administration, holistically, and the models developed in different research works are currently focused only on a specific network layer (physical, link, or transport layer), making the estimation of the WSN behavior a very difficult task. In this context, we present a systematic and comprehensive review focused on identifying the existing WSN models, classified into three main areas (node, network, and system-level) and their corresponding challenges. This review summarizes and analyzes the available literature, which allows for the general understanding of WSN modeling in a holistic view, using a proposed taxonomy and consolidating the research trends and open challenges in the area.

Several wireless communication technologies, including Wireless Sensor Networks (WSNs), are essential for Internet of Things (IoT) applications. WSNs employ a layered framework to govern data exchanges between sender and recipient, which facilitates the establishment of rules and standards. However, in this conventional framework, network data sharing is limited to directly stacked layers, allowing manufacturers to develop proprietary protocols while impeding WSN optimization, such as energy consumption minimization, due to non-directly stacked layer effects on network performance. A Cross-Layer (CL) framework addresses implementation, modeling, and design challenges in IoT systems by allowing unrestricted data and parameter sharing between non-stacked layers. This holistic approach captures system dynamics, enabling network design optimization to address IoT network challenges. This paper introduces a novel CL modeling methodology for wireless communication systems, which is applied in two case studies to develop models for estimating energy consumption metrics, including node and network lifetime. Each case study validates the resulting model through experimental tests, demonstrating high accuracy with less than 3% error.

En este articulo se presenta el diseñó e implementación de un manejador eléctrico con factor de potencia unitario de un motor de inducción trifásico controlado de forma digital mediante la técnica de control escalar.Se describe en detalle la metodología usada para obtener los modelos lineales de todo el sistema y los resultados experimentales de la implementación. El sistema implementado obtuvo factor de potencia unitario en el rango de operación (Pin <200W), regulación de velocidad menor 5% y una eficiencia de la unidad de procesamiento de potencia de 82%.

Accurate current-consumption estimation in IEEE 802.15.4 Wireless Sensor Networks requires linking network operation to the hardware states that determine node energy use. This paper proposes a cross-layer energy-modeling methodology that combines experimentally characterized Current Consumption Profiles (CCPs), protocol-derived state-duration models, topology-dependent workload, and a two-stage probabilistic model for retransmissions. The methodology distinguishes structurally constrained states, empirically modeled state durations, and discrete stochastic communication events. In the evaluated CC2538–ContikiMAC implementation, transmission duration is modeled from payload size, ContikiMAC strobe activity, and Clear Channel Assessment (CCA) timing, while sleep duration is obtained from the residual communication-cycle constraint. Retransmission occurrence is represented by a Ridge-regularized logistic model using measured CCA duration, whereas the positive retry count is represented by its calibrated conditional mean. The resulting probabilistic component is integrated with the deterministic CCP formulation to estimate the additional charge associated with retry-enabled communication events. Full-network verification across 30 node–condition observations yielded an overall MAPE of 10.81%, WMAPE of 10.51%, and condition-specific MAPEs ranging from 4.60% to 15.78%.