Nowadays, this technology is used in many applications like telecare systems [2], automotive [3], domotic [4], industrial processes [5], smart cities [6], military [7], and others. For instance, the state of a dependent person can be monitored by a WSN, recognizing and preventing crisis in the home, assisting daily routines and providing awareness of daily life and long-term trends [8]. Other related projects use WSN technology to measure health variables into different places such as nursing homes, hospitals or homes [9]. Other WSN can also be used to detect filled garbage containers along an area of a city to optimize the route of the garbage trucks [10]. Another example is fire detection in forests; deploying motes with fire sensors in a WSN along a forest enables to detect small blazes before a big fire [11]. In substance, the WSNs can provide a better understanding of the environment offering a good solution to gather information that will be assessed by an intelligent system. The energy consumption of each mote in a WSN highly influence the autonomy of the WSNs applications. Usually, the motes are powered by batteries and thus can be placed anywhere independently in the electricity grid. It means that the operation time is fixed by the time that the battery can power the mote. Maximizing the operation time is critical in many applications where the costs of recharging the batteries are important. For instance, in the case of a telecare system, the dependent people often forget to recharge the motes and, therefore, the WSN will not work properly. In the same way, in the detection of forest fires, the WSN covers large area where often there is not an electrical grid, making the batteries recharge a very expensive task. In summary, energy saving is a critical aspect in WSN design. In this context, a growing demand of WSN systems requires sophisticated skills to handle the energy consumption to maximize the operation time of the WSN. The efficient usage of the communication protocol, transmission data interval and power consumption modes of the motes electronics components have great impact. We can distinguish two main components in each mote affecting the power consumption: the radio and the microcontroller. Depending on the emission power of the antenna and the communication protocol, the mote will consume more or less energy, affecting the battery life. The researches struggle to find strategies in order to maximize the battery time. These strategies are focused on the hardware, the route protocols and the data treatment. Also, all these strategies are combined with the newest WSN approach in order to join efforts. First, choosing a compatible hardware. Second, selecting the route protocol depending on the path of the information. Last, minimizing the data size to send. Once the strategy is chosen, there are three actions of each mote that influence the energy consumption: sensing, processing and transmission. Each action has its own consumption; the sensing energy consumption depends on the sensors of the mote and the associated hardware. The processing energy consumption depends on the microcontroller and the data treatment strategy. The transmission energy consumption depends on the antenna, the radio transceiver and the path. The sustainable operation of battery powered wireless embedded systems is a key challenge. Mote energy harvesting, particularly in the energy consumption of each mote, has emerged as a viable technique to improve the time between battery charges. The management of the power modes directly affects the energy usage and the storage decisions. The ability of the system to modulate its power consumption by selectively deactivating its sub-components also impacts the overall power management architecture. Taking this into account, harvesting technologies hardware depends on the batteries of each sensor motes. Particularly, the cost of the hardware depends on the batteries size.