# Integrated Evaluation of Reliability and Power Consumption of Wireless Sensor Networks
## 1 Introduction  
Sensor Wireless Networks (WSNs) are a kind of ad hoc network made up of hundreds or thousands sensor nodes that cooperate to route packets to a particular node (sink node) capable of mediating the communication between the WSN and an external network, e.g., the Internet. Commonly deployed in environments whose access is hard, sensor nodes have limited resources of processing, storage, transmission, and battery. In such places, it is painful to replace a sensor node when it fails or when the battery is over. Therefore, to plan the WSN deployment is a crucial activity. An initial step to planning a WSN is to assess the power consumption of application and communication protocols running on the sensor nodes. By analysing this assessment, it is possible to infer the WSN lifetime. However, this is not sufficient as the WSN can have the energy to perform for days, but some nodes may be unable to communicate with the sink node. In other words, some portions of the network can be dead in the case the batteries of some nodes are drained. As existing solutions [1; 2; 3; 4] usually calculate the reliability of WSNs considering the failure of communication links and nodes, if a node fails (stops working) due to the scarcity of energy, the WSN's reliability is affected by the node's power consumption. An approach to face this problem is to evaluate the WSN's reliability integrated with the power consumption. If this evaluation is ignored, the WSN could not perform as planned. A strategy to estimate both the reliability and power consumption of WSNs is to use actual sensor nodes [1]. This solution, however, is harmful because it requires several sensors, e.g., to evaluate a WSN with thousand of nodes. Besides, it is also limited as practical scenarios are difficult to be replicated. Another alternative is to use simulators or formal models. The traditional simulators usedto assess the WSN power consumption can also evaluate its reliability. Some works like [2; 3] consider the percentage of packet loss or corrupted to calculate the WSN reliability. However, these works consider simply the failure of communication links and neglect that the node can also fail. Other works, like [4], propose a simulator and a formal model to assess reliability and WSN power consumption. In this case, it considers the failures of both sensor nodes and communication links. However, they do not consider the power consumption of the application running on the node. By observing the importance of planning WSNs and the problems aforementioned, this paper presents a solution to estimate the power consumption of WSN infrastructure and applications using models and taking in account their reliabilities. The proposal consists of three key elements: a methodology to guide the integrated evaluation, a set of CPN (Coloured Petri Net) [5; 6; 7] and RBD (Reliability Block Diagram) [8] models, a sensitivity analysis-based strategy to select WSN configurations, and a tooling that automatises the steps of the methodology. The methodology guides all steps to plan a WSN considering its power consumption and reliability and uses the models to express both characteristics. Furthermore, a set of tools fully supports the adoption of the methodology. For example, the tooling helps users to improve the power consumption and reliability of WSNs by giving suggestions or evaluating several WSN configurations simultaneously using the sensitivity analysis [9]. This work has the following unique contributions: a methodology that guides the integrated evaluation of power consumption and reliability of WSNs; a tooling that supports the methodology and automatizes the whole assessment process; formal models to represent reliability and power consumption the WSN applications and network stack; and the sensitivity analysis-based strategy to help in selecting WSN configurations. This document is organised as follows. Section 2 presents basic concepts necessary in the remaining of this paper. Next, Section 3 presents the proposed methodology.