## 1 Introduction  
Wireless Sensor Networks (WSNs) consist of thousands of sensor nodes that collect data from the environment and send them to the sink node, which in turn forwards it to a remote user. WSNs have limited resources (e.g., battery, processing capacity), and should work as long as possible in the environment while collecting and sending data to a sink node. Several strategies have been developed to optimize the power consumption (increasing the network lifetime) and increase the reliability (increasing the probability of a packet being delivered) of WSNs. However, strategies to decrease the power consumption negatively affect the reliability of the network (and _vice versa_). We can mention two strategies implemented in the protocol stack. In the first, part of the network should work while others should sleep. This strategy is excellent for power consumption, but not for reliability [1]. Part of the network may become inaccessible because a WSN node may be sleeping. The second strategy creates multiple paths (via a routing algorithm) between a particular WSN node and the sink node. Unlike the previous strategy, this strategy is excellent for reliability, but not for the power consumption; because it will use more than one way (more WSN nodes) to transmit the same packet. Hence, it is important to evaluate the WSN reliability considering the power consumption. One way to evaluate the reliability of WSN is doing experiments with actual WSN nodes, inserting faults artificially [2] or analyzing them in actual scenarios [3]. For example, [PERSON] and [PERSON] [4] analyzed the impact of physical and MAC layers on the reliability of a WSN under different conditions (e.g., indoors and outdoors). However, these experiments tend to be boring, because they are manual and require a long time to get the data. The other way to evaluate the reliability is by using simulation or analytical modeling. Works on simulation evaluate the WSN reliability by expressing the network behavior (e.g., create, send, receive, forward and discard packets). They use models that consider irregular propagation and interference by other elements (e.g., Radio Irregularity Model [5]) and which packets are discarded mainly due to environmental conditions (e.g., noise, interference). This is ideal to define and evaluate communication protocols. For example, [PERSON] _et al._[6] proposed a congestion control protocol called Priority-based Congestion Control Protocol (PCCP) for WSNs. However, this work does not consider that a fault can occur in the sensor node, and a simulation can take a long time to evaluate the entire network. These disadvantages are solved by analytical modeling, which evaluates the network through mathematical representations, such as Markov Chain, RBD and Tree Fault [1]. For example, [PERSON] _et al._[7] evaluated the cost and availability of a WSN using Markov Chain and [PERSON] _et al._[8] used RBD for evaluating the reliability of two transport protocols (ESRT and RMST). However, these models consider fixed reliability values for all WSN nodes and evaluate the reliability of the entire WSN (instead of part of the network). The methodology proposed by [PERSON] _et al._[9] does not have some these drawbacks; however, they do not consider battery level as a factor that affects the sensor node reliability and they did not assess the power consumption before evaluating the reliability. Additionally, they do not build reliability models considering the routing algorithm used in the WSN in such way that is difficult to evaluate some existing strategies (like multiple paths). Additionally, this model does not consider that different routing protocols interfere with the reliability of WSN. In this context, this paper proposes a model (called Region Model) to evaluate the reliability of each part (region) of a WSN. The proposed model is constructed considering routing algorithms used in WSNs.