Tracking and Predicting Link Quality in Wireless Community Networks (WCN) 3 rd Int. Workshop on Community Networks and Bottom-up-Broadband, CNBuB 2014 October 8 th, Larnaca, Cyprus P. Millán 1, C. Molina 1, E. Molina 2, Davide Vega 2, R. Meseguer 2, B. Braem 3, C. Blondia 3 1 Universitat Rovira i Virgili, Tarragona, Spain 2 Universitat Politècnica de Catalunya, Barcelona, Spain 3 University of Antwerp - iMinds, Antwerpen, België

Motivation [Link Quality] Prediction in [Wireless] Networks Experimental Methodology & Results Conclusions & Future Work OLSR Outline 2

Motivation 3

Community networks create measurable social impact: provide the right and opportunity of communication 4

These large, decentralized, dynamic and heterogeneous structures raise challenges – What is the effect of the unreliability and asymmetrical characteristics of wireless communications on routing protocols and network performance? – Link quality tracking is a key method to apply when routing packets through an unreliable network. – Routing algorithms should avoid weak links whenever possible and as soon as possible. Motivation 5

Link quality estimation/prediction approach increases the improvements in routing performance achieved through link quality tracking. – RT metrics do not provide enough information to detect degradation/activation of a link at the right moment. – Prediction techniques are needed to foresee link quality changes in advance and take the appropriate measures. Motivation 6

– Main contributions: Use of time series analysis to estimate link quality in the routing layer for real-world wireless mesh community networks. A detailed evaluation of the results obtained from several learning algorithms, showing the potential of time series to estimate link quality. Clear evidence that link quality values computed through time series algorithms can make accurate predictions in those WCN. In this work we present a link quality analysis and prediction of Funkfeuer wireless mesh community network 7

Prediction in WCN 8

Energy Efficient Routing: – Lifetime Prediction Routing (LPR), Minimum Drain Rate (MDR), E-DSR routing protocol. Routing Traffic Reduction: – OLSRp, Kinetic Multipoint Relaying (KMPR). Network Reliability: – Mobile Gambler’s Ruin (MGR). Link Quality prediction. Goals of Network Prediction 9

Link quality tracking: – To select higher quality links that maximize delivery rate and minimize traffic congestion. Link quality prediction: – To determine beforehand which links are more likely to change their behavior. Result: – The routing layer can make better decisions at the appropriate moment. Link Quality Prediction in WCN 10

Measure the quality of the links between nodes based on physical or logical metrics. Physical metrics focus on the received signal quality: – LQI (Link Quality Indication), SNR (Signal-to-Noise Ratio), RSSI (Received Signal Strength Indication). Logical metrics focus on % of lost packets: – RNP (Required Number of Packets), ETX (Expected Transmission Count), PSR (Packet Success Rate) To select the more suitable neighbor nodes when making routing decisions. Link Quality Estimators (LQE) metrics 11

Routing protocol for wireless sensor networks that uses a learning-enabled method for link quality assessment. – Also uses time series analysis to improve the routing protocol. MetricMap 12 MetricMap: Evaluates a small wireless sensor network. Gives only a flavor of the potential of time series analysis to predict link quality. Applies a time series analysis to predict current link quality values. Uses a cross-validation method, which uses a subset of the sample data to validate LQE. Our work: We evaluate a large wireless mesh community network. We perform a detailed and deep analysis of this potential. We use a time series to predict future link quality values. We use new data to validate the link quality estimation (LQE).

Funkfeuer WCN (Austria): – links, OLSR-NG routing protocol. Open data set (Confine Project): – OLSR info, 404 nodes, 7 days, degree: 3.5, diameter: 16. – links with variations in LQ (if all nodes: higher prediction accuracy). Link Quality: – ETX = 1 / (LQ × NLQ), LQ = %HELLO received. Time Series Analysis & Forecasting: – Training and test sets validation approach. – Weka: machine learning/data mining approach to model time series, encodes time dependency via additional input fields (“lagged” variables). Metrics and Plots: Mean Absolute Error (MAE). MAE = sum(abs(predicted - actual)) / N Boxplots: classic representations of a statistical distribution of values. Experimental Methodology 13

A sample of variation of LQ values of a link over a day Variation of LQ values 14

Results 15

Comparison of learning algorithms Time series analysis and prediction can be used to predict the next link quality value? 4 classification algorithms : Support Vector Machines (SVM) k-Nearest Neighbors (KNN) Regression Trees (RT) Gaussian Processes for Regression (GPR) 4 classification algorithms : Support Vector Machines (SVM) k-Nearest Neighbors (KNN) Regression Trees (RT) Gaussian Processes for Regression (GPR) 16 Data sets: Training: 1728 instances (6 days) Test: 288 instances (1 day) Lag window: last 12 instances Data sets: Training: 1728 instances (6 days) Test: 288 instances (1 day) Lag window: last 12 instances BEST WORSE Very high success rate: >95%

Learning algorithms: error variability 17 The four algorithms achieved a similar performance for most of the links (median, 1st & 3rd quartile) The four algorithms achieved a similar performance for most of the links (median, 1st & 3rd quartile) Some outliers have high errors … that increase the average values T-test result: RT is a good candidate to predict LQ.

Impact of lag window size What is the impact of lag window in the prediction of next LQ value? 18 Same experimental setup BEST WORSE These results are similar or even better than results obtained by other algorithms: RT is the best candidate These results are similar or even better than results obtained by other algorithms: RT is the best candidate T-test result: our results do not provide clear evidence of the best window size. >97%

Prediction of some steps ahead Time series analysis and prediction can be used to predict the value of LQ some time steps ahead into the future? 19 Same experimental setup The values of third quartile and outliers grow with steps ahead values. These differences in the variability of errors lead to the differences in the average MAE. Good results for all values of steps ahead Average MAE grows slower than linear >97%

Degradation of RT model over time What is the accuracy of the prediction models over time? 20 Average MAE of the overall network and its approximation to a linear function Linear function: slope = b = Linear function: slope = b = A linear function can be used to model the degradation of the RT over time ½ day 6 days Variability of errors increases linearly with the number of instances of the test data set It is important to train the model again after a period of time Linear function: we could easily determine a trade-off between error & frequency of model updates. 97%  84%

Evolution of prediction error over time 21 The larger the size of the training data set, the smaller the error RT model was trained at time values predicted ( 1728 instances for training) 288 values predicted ( 288 instances for training) Impact of the size of the training data set in the prediction error Further analysis would be necessary to determine an ideal size for the training data.

22 Conclusions Future Work

Time series analysis is a promising approach to accurately predict LQs in WCN Routing protocol performance can be improved by providing information to make, at the right time, appropriate decisions to maximize delivery rate and minimize traffic congestion All algorithms achieved percentages of success between 95% and 98% when predicting the next value of LQ, being the Regression Tree the best one. Prediction accuracy could have been even better including all the WCN links (not only those with variations). Prediction of values that are more than one step ahead also achieves high success ratios, between 97% and 98%. The size of the training data set is a key factor to achieve high accuracy of predictions. – The bigger the data set size, the smaller the degradation of the error over time. OLSR Link-Quality Prediction: Conclusions 23

OLSR Future Work 1)Identify which links contribute the most to the error of the link quality prediction 2)Understand what factors make difficult to predict the behavior of these links 3)Extend the analysis presented in this research work to other community networks, such as Guifi.net, to see if the observed behavior can be generalized. 24

Questions? Thanks for Your Attention 3rd Int. Workshop on Community Networks and Bottom-up-Broadband, (CNBuB 2014)October 8th, Larnaca, Cyprus

Questions? 3rd Int. Workshop on Community Networks and Bottom-up-Broadband, (CNBuB 2014)October 8th, Larnaca, Cyprus

ANEXOS 27

28