## 1 Introduction  
Advances in microelectronics have boosted the development of small, low-cost, and low-power electronic devices capable of sensing the environment and with considerable processing capacity. These developments have been, in part, fueled by the rapid advances of the Internet of Things (IoT) concept and our necessity for connectivity, making possible applications such as Smart Cars, Traffic Management Systems, Smart Houses, Digital Twins, and many others. These applications need to collect data from the monitored process and transport the information to a computational site. This has made WSNs one of the most rapidly evolving technological domains, mainly because of the many advantages they present compared to equivalent wired networks. The most significant advantage of WSNs is the low cost of implementation and faster deployment of wireless devices [1]. There are many applications for this type of technology, notably in the industrial realm, where the term Industrial Internet of Things (IIoT)) was coined. This technology is an attractive choice for smart solutions, such as industry and home automation, machinery monitoring for early fault detection and predictive maintenance, and other control applications. A WSN consists of a spatially distributed collection of sensor nodes, router nodes, and one or more Base Stations (BS), also called Edge Gateways, because they represent the border line between wired and wireless communication devices, as depicted in Figure 1. Sensor nodes can measure physical properties, such as temperature, vibration, motion, air quality, and many others, using special probes to produce sensory data [2]. In some situations, the sensor node needs to interact with the monitored process. This interaction uses some type of mechanical and electrical actuator, always under the control of a higher hierarchy level entity, usually above the BS level. The router nodes are in charge of building the mesh network infrastructure, and sometimes, they can also collect data, effectively emulating a sensor node inside the router node itself. The data collected by sensor nodes or specially programmed routers are transported through the mesh network infrastructure to one or more Base Stations depending on the adopted wireless protocol. WSNs can also be organized into clusters. Clusters have a leader, also called the Cluster Head (CH). Clustering helps to stabilize the network, avoid packet losses, and reduce the number of packet collisions [3]. CHs usually have more energy and processing capacity than regular sensors. In a clustered network, sensors connect only with their CHs; therefore, they are not affected by changes in other parts of the network. While wireless sensor networks offer many advantages, they also present some drawbacks. A major problem is the energy consumption of a sensor node, as most devices use batteries as the primary source of energy. Sometimes the sensor node depends on harvesting the energy from the monitored process. Therefore, it is very important to extend the battery life as much as possible. Considering a well-behaved sensor node that is allowed to sleep in a deep low-power mode between measurements, approximately 80% of energy is consumed in transmission and the rest in sensing and processing [4]. In a WSN, data prediction stands out as a possible solution to this problem by predicting part of the sensed data without triggering transmission, thereby saving energy and reducing congestion traffic in the wireless network [5; 6; 7; 8]. This paper proposes the use of end-to-end deep learning strategies to approach the problem of Multivariate Time Series predictions in WSNs in a dual prediction scheme to reduce the amount of transmitted data and therefore mitigate the consumption of energy. The methods are compared using multiple error metrics during forecasts and by measuring the effective number of transmitted points per model. The results show that the Attention model was the most effective when performing long-term forecasts.