Low-Cost Wireless Link Capacity Estimation

Transcription

1 Low-Cost Wireless Link Estimation Jonathan Guerin, Marius Portmann, Konstanty Bialkowski, Wee Lum Tan and Steve Glass School of Information Technology and Electrical Engineering, University of Queensland, St Lucia, Brisbane, Australia National ICT Australia - Queensland Laboratory {guerin, marius}@itee.uq.edu.au, {konstanty.bialkowski,weelum.tan,stephen.glass}@nicta.com.au Abstract Wireless link quality estimations are essential for the optimal operation of various network functions like routing and rate adaptation. In this paper, we present a new link quality metric named Effective Link (ELC), which predicts link capacity by utilizing information such as the packet delivery ratio (PDR) and the transmission count of data packets (). ELC requires no active probing and hence incurs zero overhead, while using only locally-available information from the transmitting node. Using our conducted testbed, we evaluate the accuracy of ELC on both single link and hidden terminal scenarios, with different configurations of packet sizes, link rates and offered loads. We also corroborate our conducted testbed findings with an evaluation of ELC for varying amounts of offered load on a live wireless data link in an office environment and find that ELC is highly accurate, with a maximum Root Mean Squared Error (RMSE) of.3%. We also compare ELC against a known bandwidth estimation tool, PathChirp, and find that ELC s RMSE of.% far outperforms PathChirp s RMSE of.%. I. INTRODUCTION The proliferation of cheap and user-friendly IEEE.11- based wireless devices has been one of the key factors that are driving the field of pervasive computing. These wireless devices are used in a variety of pervasive computing environments, ranging from static networks such as wireless sensor networks and wireless mesh networks, to mobile networks such as wireless vehicular networks. One of the most important functionality in these networks is the routing of information from data sources to their destinations. Efficient routing protocols such as the Ad hoc On-demand Distance Vector (AODV) protocol [1] are often used to manage the discovery of routes and the routing of data from a source node to a destination node in these wireless networks. In routing protocols, the routing metric is a crucial component since it provides the basis of routing decisions. The simple hop-count routing metric (i.e. the number of hops between the source and destination nodes) is not always the best [], [3], and recent works have proposed ETX (Estimated Transmission Count) [] and ETT (Expected Transmission Time) [] to better estimate the quality of a wireless link (and by extension, the quality of a routing path). Both of these metrics utilize periodic active probe messages in order to assess the wireless link quality. There have also been works [], [] that propose the use of link-layer information (e.g. the number of frame retransmissions) to provide better link quality estimations than periodic, network-layer probes. Finally, the authors of [7] present a comparison of bandwidth estimation tools. They conclude that passive estimation methods are much more accurate in predicting link quality than active probing tools. In this paper, we present a new link quality metric called Effective Link (ELC). Our metric tracks the estimated link capacity based on information such as the packet delivery ratio (PDR) and the transmission count of data packets (), measured from the device driver at the wireless interface, based on data traffic. We evaluate the accuracy of ELC on a conducted testbed in which the.11 nodes are fully connected via co-axial cables and programmable RF signal attenuators. By electronically controlling the effective link attenuation, we can easily control the link quality between nodes and perform repeatable experiments. Using our conducted testbed, we evaluate the performance of ELC against other link quality metrics under a single link configuration. We also investigate the performance of ELC under different configurations of packet sizes, link rates and offered loads, using both the single-link configuration and the well-known hidden-terminal configuration []. We further evaluate the accuracy of ELC on a wireless link in an office environment. In this paper we provide the following contributions: We present ELC, which estimates link capacity with zero overhead as it does not require active probing (unlike ETX-based metrics). ELC only uses information that is locally available at the transmitting node. Our evaluation of ELC in our conducted testbed shows that ELC is able to accurately estimate the link capacity in various configurations, with a minimum Root Mean Squared Error (RMSE) value of.7%, and a maximum RMSE value of.1%. We evaluate ELC s accuracy on a wireless link in an office environment, with our results showing that ELC can accurately estimate the link capacity with RMSE values ranging from.% to.3%. We also evaluate ELC against a known bandwidth estimation tool - PathChirp [9] - and find that ELC s estimation RMSE of.% far outperforms PathChirp s RMSE of.%. The rest of the paper is structured as follows: we present an overview of our link capacity estimation metric in Section II, present our experiment setup in Section III, present our results and discussion in Section IV and our conclusions and future work in Section V.

2 II. ESTIMATING LINK CAPACITY The Theoretical Maximum (TMT) is the maximum throughput that can be achieved on a link with perfect, error-free conditions. As shown in [7], [], this can be calculated analytically. However, it is unlikely for such perfect conditions to be seen in practice. By using TMT as a base, we propose the Effective Link (ELC) metric: TMT PDR ELC =, (1) where TMT is calculated as described above, is the average number of transmissions per frame over the last measurement period, and PDR is the ratio of frames which have been successfully delivered versus all frames transmitted in the last measurement period. We present a method of calculating link capacity from well-known metrics in Table I. The well-known application layer throughput equation of TMT PDR fails to consider.11 MAC-layer retransmissions, so the corresponding link capacity metrics of TMT ETX and TMT describe link capacity in terms of retransmissions. As ETX is simply an estimation of, the two equations are identical, save for the method of measuring retransmissions. Unfortunately, as demonstrated in our results in Section IV-A, the retransmission-based link capacity estimation no longer provides an accurate measure when the number of retransmissions approach maximum. From this, we conclude that combining PDR and to calculate link capacity covers all ranges of link quality and demonstrate this in the following sections. A. Conducted Testbed Setup III. EXPERIMENT SETUP The experiment testbed allows.11 wireless nodes (using the Atheros AR1X chipset) to be connected via co-axial cables in which each link can be electronically controlled. Control of each link is via a programmable RF signal attenuator (JFW P-17-SMA). The attenuation value can be set in 1dB steps from to 3dB for signals with a frequency range of.1 to GHz. Coupled with the cable loss and an additional db of fixed attenuation, this provides an effective attenuation range of 7 to 117dB. Thus, by varying the attenuation value on the RF signal attenuator from db to 3dB, we can effectively change the link quality from a perfect link to a completely disconnected link. Multiple links are established between each.11 nodes, by using an -way signal splitter/combiner. Each node is placed in an RF shielding box, providing db of isolation. This ensures that signal propagation between nodes occurs only along the co-axial cables. Additionally, it means that external RF signal sources do not interfere with the operation of the testbed. In its current configuration, the testbed supports up to four nodes. We present an image of the testbed in Fig. 1. B. Live Wireless Link Setup In order to evaluate the accuracy of our proposed method, we have configured a wireless link between two Linux PCs Fig. 1. Our Conducted Testbed in an office environment. Each PC is equipped with a multistandard IEEE.11abg wireless network interface and a fixed inline attenuation of approximately 1-dB is placed between the wireless network interface and the antenna. This allows us to control link quality simply by controlling the interface s transmit power which can be selected on a frameby-frame basis. We tune the cards to various.11a frequencies in order to reduce contention from other IEEE.11bg networks in the vicinity and average results over a second period. When re-tuning the network interface we observed some performance fluctuations until the driver parameters are successfully applied and so we remove the leading and trailing seconds from each period to account for this. We run each experiment multiple times, starting with a Transmit Power setting of 1dBm and increasing it each time by 1dBm in order to improve the link quality. In our experiments, we evaluated the performance of ELC with the automatic rate control (autorate) features enabled. The autorate feature is a mechanism with which the MadWifi- NG device driver [11] uses to combat frame loss which Fig..!"# $"# Single Link Scenario on Conducted Testbed TABLE I LINK CAPACITY METRIC FORMULAS Link Metric Abbreviation Formula TMT PDR Effective Link ELC Link ETX Link -LC ETX-LC TMT TMT ETX PDR Link PDR-LC TMT PDR

3 3 3 1 Measured ELC PDR-LC -LC ETX-LC Fig. 3. A comparison of different estimated link capacity values with the measured capacity on a saturated Mbps link on conducted testbed. TABLE II RMSE FOR DIFFERENT LINK CAPACITY METRICS ON CONDUCTED TESTBED Link Metric ELC -LC PDR-LC ETX-LC RMSE 1.3%.97% 1.7% 9.7% automatically adjusts the wireless interface s data rate based on its own estimation of link quality. Traffic is generated on the link using the iperf performance testing tool [] in UDP mode. iperf is used to saturate the link with traffic and measure the actual link capacity under these conditions. There is only one sender and one receiver and thus the saturated throughput is the link capacity. We repeat all experiments three times, and average the results. We evaluate the accuracy of ELC against the measured link capacity by using the Root Mean Squared Error (RMSE) metric [13]. C. Implementation In order to calculate ELC, we implement a client process which passively monitors outgoing traffic and gathers linkquality information such as frame loss, frame-level throughput, and frame transmission count. A monitor-mode virtual interface is used to intercept all outgoing frames from the wireless interface and pass them to the user-space client. This process extracts link quality data which the MadWifi-NG wireless device driver attaches to the frame as part of the frame s Radiotap header []. We maintain a 1 second average of the following values: the data rate, transmission count and transmission failure of each frame. We use these averaged values to calculate TMT and from this, ELC. The IEEE.11 RTS-CTS exchange is disabled by default in many commodity wireless interface drivers, and hence we also disable it for all the following experiments. IV. RESULTS AND DISCUSSION A. Conducted Testbed - Link Estimation Comparison For this experiment, we configure a scenario on our conducted testbed where two nodes are connected by a single link with an in-line variable RF attenuator, as shown in Fig.. We gradually increase the attenuation between the sender and receiver, thereby emulating the effect of increased distance between the two nodes, with the corresponding drop in link quality. We investigate the effect of this attenuation on a saturated link with a fixed physical data rate of Mbps, and a packet size of bytes. We use the link capacity metrics described in Table I to compare their estimation accuracy. Fig. 3 shows a comparison of the measured (actual) and estimated link capacity of each link capacity metric. We observe that ETX is a poor estimator of the link capacity, while PDR is only able to accurately predict the link capacity when the link is good (no packet loss) and when the link is disconnected (% packet loss). On the other hand, is able to closely track the measured capacity until approximately 9dB of attenuation, after which the estimated link capacity remains constant at.mbps. This is due to the fact that doesn t differentiate between a failed frame which is dropped after it has been retransmitted a maximum number of times, versus a successful frame which is delivered after the maximum number of retransmissions. We conclude that ELC estimates the link capacity with an RMSE of 1.% - a high degree of accuracy Measured (1Mbps) ELC (1Mbps) Measured (11Mbps) ELC (11Mbps) Measured (Mbps) ELC (Mbps) Fig.. ELC vs. on a saturated data link at different link rates on Conducted Testbed TABLE III SINGLE LINK, SATURATED ON CONDUCTED TESTBED -ELC RMSE Packet Size Link Rate B B B Mbps 7.91%.% 1.3% 11Mbps.3% 1.1%.79% 1Mbps 1.37%.7% 1.%

4 !"#!%# 3 Measured (1Mbps) ELC (1Mbps) Measured (11Mbps) ELC (11Mbps) Measured (Mbps) ELC (Mbps) $"# $%# 1 Fig.. Hidden Terminal Scenario on Conducted Testbed In order to assess the estimation accuracy of each link capacity metric, we use the RMSE as it is a standard measure of model fitting, with each value normalized to the link capacity [13], and these are presented in Table II. Table II shows that the link capacity as estimated by ETX is extremely poor (9.7% RMSE). This is due to the small, unacknowledged probe frames used to estimate the metric value, which do not reflect the behavior of larger, acknowledged data frames. Similarly, the link capacity estimated by PDR is also poor (1.7% RMSE) while is able to better estimate the link capacity (.97% RMSE). We see that ELC performs the best in estimating the link capacity, with an RMSE value of 1.3% B. Conducted Testbed - Single Link Using the same scenario as in Section IV-A, we evaluate the ability of ELC to estimate link capacity over three different physical link rates: 1Mbps, 11Mbps, and Mbps. We also vary the packet sizes with the following values: Bytes, Bytes, and Bytes. Finally, we evaluate the performance of ELC when the offered load on the single link is changed from fully saturated (33Mbps) to partially loaded (1Mbps). 1) Saturated Link: For these results, the link is saturated with an offered load of 33Mbps from the sender, while the 3 1 Measured (1Mbps) ELC (1Mbps) Measured (11Mbps) ELC (11Mbps) Measured (Mbps) ELC (Mbps) 1 3 Fig. 7. ELC vs. on a saturated data link in the Hidden Terminal scenario on Conducted Testbed attenuation is increased from db to db. Fig. shows the performance of ELC on a saturated data link, at different link rates and with the packet size set to Bytes. We observe that ELC is able to accurately estimate the measured link capacity, as evidenced by the RMSE values of less than 1.3% for three different packet sizes and link rates in Table III. However, ELC demonstrates slightly-reduced accuracy as the packet size gets smaller. This is due to the higher MAC/PHY header overheads as the packet payload size gets smaller. ) Partially Loaded Link: For these results, the link is partially loaded with an offered load of 1Mbps from the sender, while the attenuation is increased from db to db. Fig. shows the performance of ELC on the partially loaded data link, at different link rates and with the packet size set to Bytes. We observe that the estimated capacity is slightly larger than the measured capacity for the Mbps rate, while it is slightly lower for the 11Mbps rate. As can be observed in Table IV, the ELC metric loses some accuracy due to the smaller sample size, as less frames are sent out when compared to the saturated case. However, the ELC estimated capacity is still quite accurate, with an RMSE value of less than.%. C. Conducted Testbed - Hidden Terminal Here, we create the well-known hidden terminal scenario [] on our conducted testbed, as shown in Fig.. It is worth noting that carrier sense between the sender nodes S1 and S, and between the receiver nodes R1 and R, is removed by 1 3 Fig.. ELC vs. on a partially-loaded data link at different link rates on Conducted Testbed TABLE IV SINGLE LINK, PARTIAL LOAD ON CONDUCTED TESTBED -ELCRMSE Packet Size Link Rate B B B Mbps.7%.9%.97% 11Mbps 3.%.7%.1% 1Mbps 1.% 1.% 1.%

5 TABLE V HIDDEN TERMINAL ON CONDUCTED TESTBED -ELCRMSE Fig.. Interferer Link Packet Size Interferer Link Rate B B B Mbps 3.% 3.% 3.% 11Mbps.1% 3.1% 3.% 1Mbps.9%.%.19% ELC Transmit Power (dbm) Link Estimation on Saturated Autorate Wireless Link TABLE VI SATURATED AUTORATE WIRELESS LINK -RMSE FOR ELC Experiment RMSE Saturated Link.9% disconnecting the links. In this experiment, the S1-R1 link is the data link, while the S-R link is the interfering link. Both links are saturated with an offered load of 33Mbps. The data link rate is fixed at Mbps, while the interfering link rate is set to three different physical link rates: 1Mbps, 11Mbps, and Mbps. The packet size of the data flow in the S1-R1 link is fixed at Bytes, while the packet size of the interfering flow in the S-R link is set to three different sizes: Bytes, Bytes, and Bytes. The attenuation value between the interfering sender S and the data link receiver R1 is increased from db to db, thereby decreasing the effect of the hiddenterminal interference. Fig. 7 shows the performance of ELC on the saturated data link, with the data flow s packet size set to Bytes. We observe that ELC is still able to closely track the measured capacity. The RMSE values are presented in Table V, and indicate that ELC maintains a high accuracy in estimating link capacity in the face of hidden-terminal interference. From this, we conclude that ELC is the most promising of these link capacity metrics in a fixed-rate environment. However, as we aim to utilise the ELC metric in a deployed environment, we explore its behavior on wireless links with autorate enabled. D. Live Wireless Link - Auto Rate Environment Here, we evaluate the accuracy of ELC in estimating Link in an autorate environment. In the results presented in Fig., the link is saturated with an iperf flow, using byte packets, and an offered load of 33Mbps. The throughput increases, as expected, as the sender s transmission power increases - thereby increasing the link quality. The Link, as estimated by ELC, is highly accurate - it closely matches the saturated link throughput. We also present the estimation error in Table VI and note that at.9%, this error is extremely low. We repeat this experiment 3 times, with differing offered loads: Mbps, Mbps, and Mbps - all with packet sizes of bytes. The results of these tests are presented in Fig. 9. Once again, we observe that ELC is able to accurately estimate the wireless link capacity. We summarize the RMSE values of each experiment in Table VII Fig. 9. ELC (Mbps Offered Load) ELC (Mbps Offered Load) ELC (Mbps Offered Load) Transmit Power (dbm) Link Estimation on Non-Saturated Autorate Wireless Link TABLE VII LOW OFFERED LOAD, AUTORATE WIRELESS LINK -RMSE FOR ELC Experiment RMSE Mbps Offered Load.3% Mbps Offered Load.7% Mbps Offered Load.7%

6 Fig.. ELC PathChirp Transmit Power (dbm) Comparison of Link Estimation on Autorate Wireless Link TABLE VIII COMPARISON OF LINK CAPACITY ESTIMATION ON AUTORATE WIRELESS LINK -RMSEVALUES Estimation Technique RMSE ELC (Saturated Link).% PathChirp.% configurations of packet sizes, link rates and offered loads. Our results show that ELC is able to accurately estimate the link capacity in various configurations, with an RMSE value ranging between.7% and.1%. We also evaluate ELC on a live wireless link in an office environment with varying levels of offered traffic loads and find that it consistently provides an accurate estimation of Link. Finally, we evaluate ELC against an active-probing bandwidth-estimation tool (PathChirp), and find that ELC provides greater estimation accuracy, without requiring the additional overheads of this - and similar - tools. As ELC estimates the link capacity for a single transmitter, we aim to extend ELC to include support for the sharing of this link capacity between contending neighbors in future works. Additionally, we aim to evaluate ELC in a routing protocol, so that it can be compared against other state-of-the-art wireless routing metrics. ACKNOWLEDGMENT NICTA is funded by the Australian Government as represented by the Department of Broadband, Communications and the Digital Economy and the Australian Research Council through the ICT Centre of Excellence program. REFERENCES E. Live Wireless Link - Comparison with Bandwidth- Estimation Tools We evaluate the accuracy of ELC in a final experiment. We use a known bandwidth estimation tool, PathChirp [9], and estimate the available link throughput. As the available link throughput in the absence of contention is simply link capacity, we compare the accuracy of ELC with this tool. PathChirp estimates the link capacity with active probing. We once again utilise the RMSE values to compare ELC s accuracy to PathChirp s. As PathChirp s performance suffers when experiencing severe packet loss, we only evaluate the range from 9dB to 17dB. This is due to the fact that PathChirp over-estimates the link capacity when experiencing severe packet loss. We use this range to provide a fair comparison with ELC, as calculating the RMSE over the entire 1dB to 17dB range would unfairly inflate PathChirp s error values. The results are presented in Fig., and the RMSE values in Table VIII. The is reported from a saturated iperf flow - with packet sizes of bytes, and an offered load of Mbps - and ELC is also calculated on a saturated link. It can be clearly observed that ELC has superior accuracy, with.% error compared to PathChirp s error of.%. This confirms ELC s great accuracy, and most importantly, that it requires none of the active probing required for PathChirp s estimation. [1] C. Perkins, E. Belding-Royer, and S. Das, RFC1: ad hoc on-demand distance vector (AODV) routing, 3. [] D. Couto, D. Aguayo, J. Bicket, and R. Morris, A high-throughput path metric for multi-hop wireless routing, Wireless Networks,. [3] R. Draves, J. Padhye, and B. Zill, Comparison of routing metrics for static multi-hop wireless networks, in ACM SIGCOMM,. [], Routing in multi-radio, multi-hop wireless mesh networks, in ACM MobiCom,. [] H. Zhang, A. Arora, and P. Sinha, Learn on the fly: Data-driven link estimation and routing in sensor network backbones, IEEE Infocom,. [] K. Kim and K. Shin, On accurate measurement of link quality in multihop wireless mesh networks, ACM MobiCom,. [7] D. Gupta, D. Wu, P. Mohapatra, and C. Chuah, Experimental Comparison of Bandwidth Estimation Tools for Wireless Mesh Networks, IEEE Infocom, 9. [] F. Tobagi and L. Kleinrock, Packet switching in radio channels: part II the hidden terminal problem in carrier sense multiple-access and the busy-tone solution, IEEE Transactions on Communications, 197. [9] V. Ribeiro, R. Riedi, R. Baraniuk, J. Navratil, and L. Cottrell, pathchirp: Efficient available bandwidth estimation for network paths, in Passive and Active Measurement Workshop, 3. [] J. Jun, P. Peddabachagari, and M. Sichitiu, Theoretical maximum throughput of IEEE.11 and its applications, in IEEE NCA, 3. [11] Multiband Atheros Driver for Wi-Fi (MADWIFI), 9, available from: [] A. Tirumala, J. Ferguson, J. Dugan, F. Qin, and K. Gibbs, Iperf, 9, available from: [13] C. Reis, R. Mahajan, M. Rodrig, D. Wetherall, and J. Zahorjan, Measurement-based models of delivery and interference in static wireless networks, in ACM SIGCOMM,. [] D. Young, Radiotap, 9, available from: V. CONCLUSION &FUTURE WORK In this paper, we presented a new Link estimation metric named Effective Link and evaluate it using our conducted testbed. We have evaluated the accuracy of ELC on both single link and hidden terminal scenarios, with different