# Improving Route Selections in ZigBee Wireless Sensor Networks
## 3 Estimating Link and Route Costs
### Literature Review  
The first procedure suggested in the ZigBee specifications, estimating link costs from the reception of broadcast LS messages and exchanging such information in LS messages, was proposed in [28]; however, the authors in [28] used such an estimate to derive a different cost metric: the expected transmission count metric (ETX) of a link. The second procedure suggested in the ZigBee specifications, estimating link costs from the average LQI of received packets, is similar to the MultihopLQI procedure used in the TinyOS platform. A formal reference for the MultihopLQI algorithm is no longer available; however, as reported in [44], the MultihopLQI estimates the overall cost of the route by combining the average LQI of received beacons at each node of the route. The authors in [33] have reported that link cost estimation, through LQI, performs better than procedures based on only radio signal strength, and several authors [44; 45; 46] have considered LQI to estimate link costs. In contrast to beacon or LS-based procedures, and related to the method that we propose in Section 6, several authors considered MAC feedback to estimate the link costs:  
* The authors in [22; 24; 25] relied on unicast transmissions to infer link costs; however, they focused on different link metrics based on the expected transmission time or MAC latency. * The authors in [26] suggested the use of MAC feedback from unicast transmissions to infer link cost as opposed to relying on broadcast packets; however, as in [28], the authors in [26] focused on the estimation of the ETX metric. * The authors in [27] proposed the EAR (efficient and accurate link-quality monitor) procedure in which nodes constantly switch between passive, cooperative, and active modes of estimation in order to estimate a metric similar to ETX. * Focusing on the IPv6 routing protocol for low Power and lossy networks (RPL), the authors in [18] proposed that link qualities be estimated by counting the number of first time transmissions that are unsuccessful and by using an active probing mechanism where nodes send unicast messages to neighbors to estimate the link quality. Although the procedures above explore unicast transmissions and MAC feedback to estimate links costs and select routes, they were not aimed at estimating \(p_{z_{1},z_{2}}\) required by the ZigBee specification; the proposed link metrics were not evaluated considering ZigBee's 3-bit link cost quantization; and, therefore, their route selection procedure does not consider how to select among routes with the same cumulative cost. More recently, several authors proposed machine learning inspired procedures to estimate link costs and select routes. The authors in [14] proposed a fuzzy C-means clustering algorithm to estimate link qualities based on the packet reception rate. The authors in [15] proposed an algorithm based on reinforcement learning to control monitoring and probing mechanisms to estimate link qualities. The authors in [16] proposed an unsupervised learning technique to select network features to better classify the quality of links. The authors in [17] evaluated various machine learning algorithms that use packet reception rates, LQI, and SINR metrics as input and output the probability of successful delivery in a link. The authors in [19] proposed that nodes monitor RSSI, SINR, and packet reception rates; exchange information with neighbors; and use this information as input to a supervised learning algorithm that uses labeled training samples to estimate the quality of links. The authors in [47] proposed estimating the probability of successful packet transmission in a link by using wavelet and neural network techniques. Their approach would require decomposing measurements of the SINR into a time-varying component and a non-stationary random part. The authors in [48] proposed a hybrid online machine learning algorithm to estimate the quality of candidate links. Their approach combines current samples of link quality with baseline samples previously learned from past samples.