## 2 Related Work  
Industry is responsible for the use of a large quantity of the overall energy resources, and concerns on efficiency are driving actions towards optimizing energy consumption in industrial processes. To optimize this energy use, it is necessary to identify the specific actuation points in industrial plants: i.e., it is crucial to know the actual performance of the machinery, and all other energy consumers distributed within factories. WSANs present a great flexibility that makes them specially appropriate for applications requiring the acquisition and management of distributed data in almost any environment [7]. Along recent years, most of the developed work in this area has focused on Wireless Sensor Networks (WSNs) in the form of centralized networks for data collection. But the inclusion of actuation nodes is driving their evolution towards WSAN and opens the door to networks with intelligent decision-making capabilities that operate in a distributed way [8; 9]. Some recent research has been carried out addressing all levels of WSAN technology: from the enhancement of the internal network organization, such as shown in [10] where different solutions for managing nodes and their interactions are analyzed and classified, or that in [11] where a system based on Deep Q-Network is proposed for mobile actor node control in WSANs; to the optimization of the nodes placement as proposed in [12], or the allocation of tasks to actuators found in [13]. All these ideas can be complemented with improvements in communications between nodes such as those presented in [14], where the use of a network over an IPv6 infrastructure is proposed. Also with the use of new algorithms to enhance the traffic forwarding capacity of nodes as described in [15], with proposals for reducing congestion and improving the network performance as in [16], a novel channel hopping methods for the exchange of data packages presented in [17], or an approach for enhancing the lossy link localization in [18]. But most of these works are based on simulations and few real results have been obtained from empirical environments, which may limit their direct applicability. Some additional work is being focused on reducing energy consumption of the WSANs; for example, in [19] PID control and fuzzy neural network algorithms are applied in nodes to control data transfer as well as node consumption. In [20], a thermal energy harvesting circuit for self-powered sensorsis presented. In [21] several methods are proposed for choosing the actor node according to the task required to find the most efficient solution. WSANs are suitable for applications in different scenarios, providing solutions to environmental challenges [22] such as energy saving for the reduction of greenhouse gas emissions. One of the most relevant applications in this context is the use of WSANs for smart buildings. For example, in [23] a WSAN is used for controlling the ventilation system in a building, and in [24] a self-powered WSAN enhanced the energy efficiency of the heat distribution system, through the use of energy harvesting techniques. As well as for new constructions, these systems can also improve the performance of old buildings, because they are easily adaptable and require little invasive installation. This is the case of [25], where a WSAN is employed to achieve savings in the energy consumption of the illumination system in a building, but the application of WSANs for energy savings does not only include big set-ups, as they can also be directly used to monitor and control the energy waste of electric appliances [26]. Or for providing support to more advanced control techniques such as in [27], where fuzzy control methods are applied over a WSAN in an industrial environment. Furthermore, a straightforward application is that it helps improve the control and management systems for air conditioning in buildings as shown in [28].