# Time Series Analysis to Predict End-to-End Quality of Wireless Community Networks +
## 5 Use Case Analysis  
### Experimental Framework  
In this section, we present simulations that compared under several load conditions the overall network performance between: (a) the proposed EtEQ prediction strategy; and (b) a simplified version of OLSR, i.e., ETX + Dijkstra. Even though both routing strategies are pretty similar, their main difference is that the latter uses directly the real past link quality values, while the former uses predicted values based on past link quality values. Prediction provides us the benefit of making routing decisions earlier, at the expense of assuming values that may be erroneous. The routing strategies are compared using a simulator that we developed using JavaSim [54], an object-oriented simulation package based on C++SIM, that provides discrete event process-based simulation similar to SIMULA's simulation class and libraries. The simulations performed followed a setup similar to [PERSON] et al. [21]. We considered the same static scenario: a \(4\times 4\) wireless mesh backbone network, where edge routers generate packets for other edge routers, and inner routers only forward packets. The two main differences are that we dealt with time slots and we considered a heterogeneous network (both links and nodes). The network was simulated during 30,000 time slots and a random initial-time (in slots) was assumed to start the packet transmission. Our simulator was developed assuming a set of routing and link characteristics that can deliver more realistic results. In these simulations, we assumed 2000 previous samples (time slots) to train a model and any new model was trained every 300 time slots. Moreover, we also assumed a lag window size of 12. On the other side, the predictor predicted EtEQ of the next round (one time slot). The ETX of a link, recalculated in real time every single time slot, was computed based on all the messages that were received and not only with the HELLO messages of the routing algorithm. The routing algorithm calculated the routing tables of each node every 100 time slots, and was omniscient in the sense that it knew the ETX of all the links without the need of broadcasted messages. The delay of the links was proportional to the size of the pending messages queue of the node and therefore can be different in any direction. Considering that in a real network, packet pending queues of the nodes are finite, we needed to model packet dropping caused by packet overload. We considered that 1 packet every 100 slots is a very low overload, 1 packet every 10 slots is an overload close to the maximum of the network and 1 packet every 1 slot is a fully overloaded network. We simulated two different approaches in terms of packet dropping: (1) a fail every ten packets; and (2) a fail every two packets. This parameter is denoted as Mean Time Between Failures (MTBF). Notice that a two-packet MTBF can be considered as an extreme failure-rate. Moreover, the same scenario of previous sections was assumed to analyze the fault tolerance of packets in the presence of link failures (packet dropping). Therefore, we assumed three different link-failure models: (1) none; (2) uniform; and (3) Pareto. The uniform model followed a uniform distribution that has a constant probability. Unfortunately, this failure model is memoryless, which means that it does not reflect the bursty nature of failures, also known as the long-range dependency. For a more realistic model, we used a self-similar process, such as the Pareto distribution, as a long-tail failure-model. Once the system was configured in the simulator, the experiments were run under several release-times for the messages in the source nodes (network load). To this end, we tested data-packet generation rates of 1 packet sent every 100, 10 and 1 time slots. Hence, we were able to evaluate the network behavior, and the loads in the links for each path. Particularly, we measured _Packet Delivery Ratio (PDR)_, i.e., ratio of packets successfully received compared to the total sent, _End-to-End Delay (EtED)_, and _ETX of links_.