## 3 Related Works
### Transmission Power Control and Routing  
A significant body of research investigates transmission power control. [PERSON] [19] provides a survey on a wealth of research on transmission power control. The majority of these research works focus on theoretical aspects, including maintaining graph properties such as k-connectivity. A major gap that authors notice in the literature is that the theoretical transmission power control algorithms have not been efficiently integrated with the standard protocols (including RPL). [PERSON] et al. [20] classify the literature on transmission power control into approaches that select the transmission power for (i) the whole network, (ii) each node, and (iii) each pair of nodes. The authors also introduce _Adaptive Transmission Power Control_ (ATPC), and at run time, the transmission power is set for each pair adaptively. Pairs build a linear model to map the transmission power to the RSSI at the destination. To this end, nodes gather samples by sending a set of beacons at different transmission power settings and adopt a least-squares approximation to find the two coefficients for the linear model (slope and bias). Based on the empirical results, the authors analyzed how the linear model changes over time. The slope is shown to be almost constant for the same physical environment, but the bias changes more significantly over time. So, after initializing the model according to the approximation, pairs update the bias to cope with the link dynamics over time. The fact that ATPC nodes piggyback RSSI readings (rather than ETX or PDR) is an important design choice, as it may be impossible to retrieve all the RSSI readings due to interference. On the other hand, ACTOR aims at reducing the size of the neighbor table by only evaluating the links for possible parents and reasonable transmission power settings. While ATPC and many of its counterparts lack integration with RPL, the most prominent works that extend RPL to adapt transmission power control are PC-RPL [10] and XRPL [21]. We studied these two proposals in more detail as the selected benchmarks. The remaining works in the literature that focus on this research problem are as follows. [PERSON] et al. [22] extend RPL to jointly perform routing, channel selection, and transmission power control. The proposed method consists of two strategies, namely the _Maximum Probability Delivery Ratio_ (MaxPDR) and the _Minimum Aggregated Power_ (MinAP). For both strategies, DIO packets are sent in batches (rather than a single packet) using all the available power levels. Based on the reception ratio of the DIO batch, nodes select their transmission power before making any routing decision. MinAP prioritizes energy consumption over the reliability of the network. Under MaxPDR, however, first, the node with the highest end-to-end PDR gets selected as the parent. Then, the transmission power is configured to the lowest level that yields sufficient PDR (for example, 80% of the maxPDR). The drawback of both MaxPDR and MinAP is the increasing number of DIO packets that introduce a tangible communication overhead. TCOR [23] combines an opportunistic routing approach with transmission power control to reduce energy expenditure while maintaining link reliability. In this theme, the transmitter keeps retransmitting its packets until one of the forwarders acknowledges the packet. To select the appropriate forwarders set and their associated transmission power, nodes estimate the probability of packet delivery based on a shadowing propagation model. Adaptive Robust Topology control (ART) [8] defines a sliding window and two thresholds. If the ratio of correctly acknowledged packets during the sliding window is high, it tries to reduce the transmission power in a \"trial\" state. If the reception ratio falls, ART may increase or decrease the transmission power based on history. [PERSON] et al. [24] take a similar approach by actively probing the transmission power settings.