As SNs are energy-constrained devices, a substantial research effort has been devoted to finding solutions that contribute to the optimization of energy consumption. In [5], a software-based information-processing approach has been followed, while in [6], a combination of software and hardware-based approaches have been proposed to reduce energy consumption and meet certain maintenance requirements. To extend the WSN lifetime, energy conservation has also been studied from other perspectives as communication protocols, routing algorithms, Medium Access Control (MAC) schemes, and packet aggregation schemes [7; 8; 9]. Considering that energy conservation is a key factor, the modeling and analysis of energy consumption is of fundamental importance to better understand, manage, and design IoT WSNs. To this end, several models have been proposed to analyze energy consumption in WSN networks for IoT applications. For example, in [10], a very detailed deterministic model is developed, where a simple MAC is considered, focusing more on the consumption of the physical layer and considering three operating modes. In [11], the authors present a stochastic model, where the energy consumption is modeled as a random variable with a given probability distribution, as well as maximum and minimum expected energy values. The model mainly takes into account physical layer issues, while a relatively simple MAC is used. Physical layer and routing issues are studied in [12]. A stochastic model is proposed to evaluate different topologies. Different random variables and their corresponding probability distributions are used to model communication hops between SNs, distances, and the number of SNs in the network. The analytical model is validated by simulation. However, the model uses a generic MAC protocol that is not even specified. Different studies relay on Markov chains to model and analyze the energy consumption in WSNs [13] and to predict the energy consumed by SNs [14]. These models determine the energy consumed by an SN considering how a packet transmission evolves through different states. However, only very basic MAC features are incorporated into the models. In [15], the model of a WSN is presented, where SNs deploy a generic synchronous duty-cycle (SDC)-based MAC protocol. To determine the energy consumption, two principal operating periods for SNs are considered, the sleep as well as the other two additional intermediate periods. Their authors used a joint probability distribution to model the number of data packets and a multidimensional Markov process to model the SN phases. However, no specific MAC protocol or any synchronization scheme is incorporated into the model. We claim that the MAC protocol is a fundamental part of a WSN and, therefore, it must be considered by any model aimed at evaluating its performance and energy consumption. Depending on traffic conditions and SN density, the nature of the contention to access a shared medium leads to conflicting events that can lead to packet collisions and have a great impact on the network performance, as well as on the energy consumed by SNs [16]. The studies mentioned above have in common that they only consider very basic MAC features, if any, and the impact of collisions was not considered. In addition, neither SN heterogeneity nor priority assignments were considered. To determine energy consumption in the above models, different operational states of a SN were defined. However, the models consider that states of SNs in a WSN are independent of each other, i.e., the energy consumed by a sensor is not influenced by the states of the rest of the SNs of the WSN. In our opinion this simplifying assumption does not hold in real scenarios. Clearly, the access to a shared medium leads to a certain degree of dependency among the SN states, and consequently, it has an important impact on the energy consumed by SNs.