# Time Series Analysis to Predict End-to-End Quality of Wireless Community Networks +
## 2 Link and End-to-End Quality Prediction in Heterogeneous Networks  
Prediction techniques have been applied in several ways such as routing traffic reduction [24; 25; 26], energy efficient routing [27; 28; 29], or LQ estimation. LQ tracking has been previously applied in several scenarios in different ways [10; 11; 12; 13] to select higher quality links that maximize delivery rate and minimize traffic congestion. Link Quality Estimators (LQE) [16; 17] are in charge of measuring the quality of the links between nodes based on physical or logical metrics. Physical metrics focus on the received signal quality [30; 31; 32], and logical metrics focus on the percentage of lost packets [10; 33; 34]. All these metrics can be used by LQE in isolation or as a combination of some of them [16; 17; 35] to select the more suitable neighbor nodes when making routing decisions. LQ prediction is used in addition to LQ tracking to determine beforehand which links are more likely to change their behavior. Although LQ prediction is not identical to EtEQ prediction, some of the above techniques can be very similar [10; 30; 31; 33; 34; 35]. This relationship is even more direct in the case of ETX EtEQ, studied in this paper, which is a linear function of the ETX LQ. As a result, the routing layer can take better decisions at the appropriate moment. Prediction of path changes can improve local node routing decisions, since it can provide the node with an estimation about the future local and remote events. End-to-End Quality has not been widely considered in the past. In fact, the main efforts have been focused on determining the cost of a single link, and then extended to the full path by assuming the cost of a path as the sum of costs of several links. For instance, MARA [36] has been proposed to make more accurate routing decisions by combining route quality evaluation and automatic rate selection. EER [37] claims that the total cost of a path cannot be expressed as a linear sum of individual link costs. Therefore, variations of this simple formulation are proposed, such as incorporating error rate in the link cost. This achieves significant energy savings compared to traditional minimum energy approaches. ETOP [38] has been proposed as a path metric to determine reliable end-to-end packet delivery. Finally, EED/WEED [39] is another approach that was designed as a link/path metric to select paths with minimum end-to-end delay and high network throughput, but considering load balancing of routing. In any case, there is no work concerning prediction of Path Quality in WMCNs. Some relevant works must be paid special attention as they are related to our study, namely those of [PERSON] et al. [40], [PERSON] and [PERSON] [41], [PERSON] et al. [42], and [PERSON] et al. [20]. [PERSON] et al. [40] introduced the MetricMap mechanism, which is fundamentally a routing protocol for WSNs that uses a learning-enabled method for LQ assessment. Based on the observation that high traffic rates make tracking LQs more difficult, this protocol uses prediction methods to estimate them in advance. Results show that MetricMap can achieve a significant improvement on the data delivery rate in high traffic-rate applications. [PERSON] and [PERSON] [41] considered the FunkFeuer network focusing on link-layer properties, topological patterns, and routing performance. They analyzed the quality of the routes,and proposed a couple of techniques to select the MPR nodes in the OLSR protocol. [PERSON] et al. [42] proposed a simple strategy for improving routing in the Internet domain, which moves in two ways: first, it detects path changes (NN4 approach) and then it remaps these paths once a change is detected (DTRACK approach). Results show that NN4 is not highly accurate, but it demonstrates the potential of prediction to improve the routing layer when making routing decisions. [PERSON] et al. [20] analyzed the behavior of LQ prediction in the routing layer of large-scale, distributed, and decentralized systems, composed of many nodes, links, content, and services.