## 2 Related Work  
A key issue in WBNs is to reduce the energy consumption of the sensor nodes for extending battery lifetime. On average, processing data consumes less power than transmitting the data wirelessly [8], whose power consumption is affected by the speed and the amount of data transmitted. In this context, the use of power optimization methods at multiple layers of the communication stack is nowadays one of the most important research issues in this field; and specifically, the strategies of transmission power control (TPC) are being widely researched, because they ensure an optimal trade-off between energy consumption and reliability requirements. TPC techniques allow one to select the minimum transmission power level required to achieve good performance within a communication system. The use of these techniques, besides reducing the transmission power consumption, also allows one to reduce the interference problems and to reduce the average contention at the MAC layer. Different TPC schemes have been proposed for different communication networks, including WBSNs. The type of TPC scheme most frequently used is the link quality-based scheme. Typically, this scheme consists of a closed execution loop between the transmitter and receiver nodes. The loop starts when the transmitter node sends a data packet, and after that, the receiving node takes the measures of the received signal strength indicator (RSSI) value as a quality metric of the communication link. At this point, if the measured RSSI is out of the previously-defined target RSSI margin, the receiving node computes a new transmission power level using a particular TPC algorithm. Finally, the receiver node sends a control packet specifying this new power value to the transmitter node. Some of the most relevant literature in the field is analyzed afterwards. [PERSON] _et al_. [9] propose two practical on-line schemes that adapt the transmit power according to the RSSI value obtained from the receiver node (piggybacked in the acknowledgment packet). Both algorithms try to maintain the RSSI of the receiver between the predetermined bounds. In the conservative scheme, if the RSSI drops below the lower configured threshold, then the transmit power is raised to the maximum, and if RSSI is consistently above the configured upper threshold along the last N sample periods, then the transmission level is reduced by a small fixed constant. In the aggressive scheme, the transmitter maintains a running average of the recent RSSI values using the exponential averaging computation, which includes a pre-configured weight value. If this running average exceeds an upper threshold, the transmit level is reduced by a small constant, while if the running average is below a lower threshold, the transmit level is doubled. The algorithms were tested using MicaZ with the CC2420 radio and their Toumaz Sensium Digital Plaster platform. The results show that in a fast walking scenario, the conservative scheme preserves reliability and yet reduces energy consumption by 1.3% on average when compared to using maximum transmit power. The aggressive scheme saves 23.4% energy on average, at the expense of slightly increased loss. In the resting scenario, the energy savings under both schemes are substantial compared to using maximum power (18.6% and 25.4%) However, some drawbacks of these schemes are the power consumption cost associated with the listening of the feedback packets and, on the other hand, the change in characteristics that the wireless channel can suffer between transmission and feedback and between the feedback and next transmission. In [10], the authors propose a class of adaptive power control protocols, where the period between each feedback transmission is adaptively varied between 2 and 64 s to accommodate run-time variation in the quality of each channel. The algorithm was tested using a CrossBow MicaZ platform with the CC2420 radio under two different scenarios, a subject walking and resting.