WiSlow: A WiFi Network Performance Troubleshooting Tool
Contents
1. FHSS PHONE E MICROWAVE OVEN 0 5 10 15 20 25 EAs a The number of FCS errors per 100 KB 1 Pr aed H s eee 1 0 8 CLEAN gfe CONTENTION rd e FHSS PHONE 0 6 ur E BABY MONITOR o _ a E 0 4 e x ct 0 2 rs E 0 10 20 30 40 50 EAs b The number of estimated packet loss per 100 KB Fig 2 The CDFs of the number of FCS errors and packet loss N Packetboss IN Rees IN BO Serror 1 We found that this estimated number of packet losses more reliably represents the level of interference than the individual statistics of retries and FCS errors In other words it showed relatively constant results on multiple experiments while the others varied for each experiment Figure 2b shows the cumulative distribution function CDF of the estimated number of packet loss where it can be seen that a baby monitor video transmitter causes the most severe amount of packet loss while contention and cordless phones cause a relatively small amount of packet loss Since baby monitors send video and audio data at the same time they use more bandwidth than cordless phones that send audio only thus causing more interference Channel contention has less packet loss because of the 802 11 collision avoidance functions such as random back off and RTS CTS that force each client to occupy the medium in separate time slots In this case the divided time slo2. a commodity network card without specialized hardware Since our tool aims to provide the best estimation of the problem source if the user happens to have this specific network adapter WiSlow can adopt the same approach However to the best of our knowledge only a few chipsets currently provide this feature In addition we failed to discover references to this feature for any OS other than Linux Since there are hundreds of products that use a different chipset and or OS it is impractical to assume that a general end user has this specific setup Therefore we focus instead on analyzing the quality of a link using user controllable protocols such as UDP and 802 11 packets Because the mechanisms of these protocols are not significantly different for many WiFi devices we believe WiSlow can help a wider range of end users C Difficulties in obtaining multiple channel information Without special hardware or a particular network adapter it is still possible to measure signal strength by monitoring 802 11 packets In addition signal information from multiple frequency bands can be obtained by channel switching It may help to identify the signal signature of each interfering de vice However without the specialized functionalities of some wireless cards the AP must be reset whenever the channels are switched This is not practical for general client machines not only because it takes a while to scan all the channels but also because th
3. interfered with Channel 1 Because they do not change frequency severe interference occurs if the current WiFi channel overlaps with the frequency of the phone In addition their duty cycle is usually one which implies that no ACK cycle exists In our experiments the No Interference C Loss Type A Loss Type B Packet Analysis Contention Clean WiFi Interference l l LO Non WiFi Audio Voice Interference _ Fixed ACK cycle Loss Type Bi Frequency Fixed E cycle a D ACK Cycle hopping Hopping Frequency O Video L Audio Voice R Loss Type C I lack Cycle hopping Fig 8 The classification of problem sources by WiSlow s methods duty cycle 1 No ACK cycle UDP throughput stayed very low and no explicit ACK cycle no hopping was observed as expected Therefore WiSlow determines an analog cordless phone as the interferer if there is severe UDP throughput degradation but no explicit ACK cycle or duty cycle is detected However this could be true of other fixed frequency devices such as wireless video cameras that are not discussed in this paper Currently when WiSlow detects this type of device it informs the user that a fixed frequency device has been detected and suggests that several devices could be the cause such as analog cordless phones and wireless video cameras 5 Mixed hopping and duty cycle A Frequency Hopping Spread Spectrum FHSS p
4. non WiFi interference e g interference of a baby monitor than with channel contention or even a no interference environment Figure 2a This paradox can be explained by the low bit rates in the interference case which implies that a smaller number of bits are transmitted in the same bandwidth Consequently the number of FCS errors alone is not enough to characterize interference sources 3 Packet loss estimation As we stated above although the number of retries bit rate and FCS errors are affected by the current state of the wireless network they often show very different statistics for each experiment set We conjecture several reasons the environment is not exactly the same in every experiment the occurrence of packet loss is probabilistic rather than deterministic and the individual variables fluctuate over time affecting each other and leading to different statistics for a certain period of time Therefore it would be more reasonable to compare the combinations of these statistics together instead of investigating each variable individually and consider the distributions of the samples rather than their temporal changes First since the retries occur when the packets are lost as well as when FCS errors happen we can estimate the amount of actual packet loss by subtracting the number of FCS errors from the number of retries Equation 1 aa EH ee i aee E r ae CONTENTION BABY MONITOR
5. of the interfering device e g a microwave oven cordless phone or baby monitor and i111 point out the approximate location of the source of interference We developed and evaluated an implementation of WiSlow that diagnoses the cause of WiFi performance degradation and returns reports to users such as It appears that a baby monitor located close to your router is interfering with your WiFi network The remainder of this paper is structured as follows In Section II we describe the common sources of WiFi perfor mance degradation In Section HI we discuss the restrictions of an end user s environment and how WiSlow attempts to overcome them Section IV explains the detailed methods of WiSlow Finally Section VII summarizes our conclusions II BACKGROUND Common sources of WiFi performance degradation in clude e WiFi channel contention degradation due to a chan nel crowded by multiple WiFi devices that compete to transmit data through an AP In addition interference due to nearby APs that are using the same channel or adjacent channels cause the similar performance problems since the APs share the limited capacity of the channel We use the term contention in this paper to refer to this type of performance degradation e Non WiF i interference interference due to non WiFi devices that use the same 2 4GHz spectrum as the 802 11b g networks such as microwave ovens cord less phones baby monitors and Bluetooth devic
6. period because the frequency of the next hop is randomly chosen This randomness instead creates diverse cycles with different periods However these periods are multiples of a specific number due to the fixed hopping interval For clarity we plot a quantized time domain graph Figure 7b that is converted back from the frequency domain graph We used the 10 highest frequencies from Figure 7a In the time domain graph the number of ACKs y axis fluctuates periodically however note that the heights of the peaks vary The possible explanation is as follows the number of ACKs is large when the device hops far from the current WiFi channel and is relatively small when it hops to a nearby frequency If the device hops into the exact range of the WiFi channel the number of 802 11 ACKs drops almost to zero In other words there are multiple levels of interference which depend on how closely in frequency the device hops to the frequency used by the WiFi channel These multiple levels of interference create several pulses that have different magnitudes and frequencies Finally because the hopping interval is fixed the frequencies of the created pulses are synchronized such that the periods of the cycles are multiples of a specific value Consequently we can distinguish frequency hopping de vices by determining whether the number of 802 11 ACKs has multiple high peaks with a certain interval in the frequency domain We check this by linear regre
7. the baseline quality of the network IV WISLOW In this section we elaborate on the details of probing methods for identifying the root causes of network interfer ence First to investigate the behavior of WiFi networks in each problem scenario we artificially inject problems while transmitting UDP packets between a client laptop and an AP capturing every packet on the client Then we trace the transport layer UDP the 802 11 MAC layer and some user accessible 802 11 PHY layer information to ascertain each problematic scenario s interference levels and characteristics To capture 802 11 packets WiSlow leverages the monitor mode that provides the Radiotap 3 header a standard for 802 11 frame information The headers are used to extract the lower layer information such as FCS errors and bit rates Sniffing the wireless packets is supported by most Linux and all Mac OS X machines without additional driver or kernel modification Therefore if we can successfully characterize each performance degrading source by probing the transmitted packets the same probes will enable WiSlow to identify the problem on most platforms However it is not always possible to capture wireless packets on some types of OS e g Microsoft Windows 5 Instead Windows provides several APIs that report 802 11 packet statistics to user applications Those APIs enable WiSlow to run on Windows because they provide all the information that WiSlow must ext
8. WiSlow A WiFi Network Performance Troubleshooting Tool for End Users Kyung Hwa Kim Columbia University New York NY USA Email khkim cs columbia edu Abstract The increasing number of 802 11 APs and wireless devices results in more contention which causes unsatisfactory WiFi network performance In addition non WiFi devices shar ing the same spectrum with 802 11 networks such as microwave ovens cordless phones and baby monitors severely interfere with WiFi networks Although the problem sources can be easily removed in many cases it is difficult for end users to identify the root cause We introduce WiSlow a software tool that diagnoses the root causes of poor WiFi performance with user level network probes and leverages peer collaboration to identify the location of the causes We elaborate on two main methods packet loss analysis and 802 11 ACK pattern analysis I INTRODUCTION Today it is common for households to put together home wireless networks with a private wireless router access point that supports multiple wireless devices However the increas ing usage of wireless networks inevitably results in more contention and interference which causes unsatisfactory WiFi performance There are two main sources of performance degradation First WiFi devices connected to the same AP or nearby APs that use the same channel can cause packet collisions 1 e channel contention Second non WiFi devices such as microwave ove
9. c i Aae c Time domain Microwave oven top 10 a Time domain Microwave oven b Frequency domain Microwave oven frequencies Fig 6 The number of 802 11 ACKs per 100 KB of UDP packets with interference of a microwave oven particular type of device e g 60 Hz for microwave ovens 3 Frequency hopping baby monitors The duty cycle of typical video transmitters such as wireless camera is known as one 10 It means that they send and receive data constantly implying that they continuously interfere with WiFi networks without any off period Baby monitors which transmit video and audio data constantly also have a similar characteristic Therefore intuitively we do not expect to observe similar ACK patterns as those observed in the microwave oven experiment However when converting the plot from the time domain to the frequency domain we observe another notable pattern Figure 7a shows that there are multiple high peaks set apart by a specific interval 1 e 43 Hz occurring at 43 86 129 and 172 Hz This is in contrast to the microwave ovens that showed only one significantly high peak at 60 Hz Figure 6b We conjecture that these peaks are caused by frequency hopping a frequency hopper switches its frequency periodically and interference occurs when it hops to a nearby frequency of the current WiFi channel However the frequency hopping device does not necessarily return to the same frequency at a regular
10. e frequencies given for the signal samples are not at a sufficiently fine grained resolution Therefore we assume that we can only observe a fixed channel of the network The disadvantage of this approach is that it may fail to detect some interferers that operate within another range of frequencies However it is reasonable for WiSlow to ignore this case because there is no motivation from an end user s perspective to detect these interferers if the interference does not overlap with his her WiFi network D Lack of monitoring data Another restriction in the end user environment is the lack of a monitoring history If we assume that we have been monitoring the machine up to the moment when a performance problem happens the diagnosis will be easier because we can obtain several important clues such as the average quality of the network s usual performance the time when the problem started and whether it has happened in the recent past However although the overhead of network monitoring is not heavy on modern machines it is difficult to expect that end users will continuously run such a tool The more common scenario is that a user launches a troubleshooting tool like WiSlow and requests a diagnostic only after he she has noticed a severe performance problem Therefore we need to design the tool assuming little or no previous monitoring data In the next section it is explained how WiSlow estimates the problem source without knowing
11. es In this paper we use the term interference to refer to this type of degradation e Weak signal when the signal is not strong enough due to distance or obstacles packets can be lost or corrupted Although the extent varies all the above sources result in severe performance degradation some of them even drop the TCP UDP throughput to almost zero 10 In this study we focus on WiFi channel contention and common non WiFi interference sources II CHALLENGES In this section we describe several difficulties analyzing wireless networks due to end users restricted conditions such as limited hardware capabilities and lack of monitoring data A Inaccurate RSSI and SINR measurement Received Signal Strength Indication RSSI and Signal to Interference plus Noise Ratio SINR are generally considered to be the key factors that indicate the quality of a wireless link However according to Vlavianos et al 12 RSSI inaccurately captures the link quality and it is difficult to accurately compute SINR with commodity wireless cards thus they are not appropriate when estimating the quality of the link We also observed a similar result when monitoring RSSI and SINR values We placed various types of interference sources close to the AP and measured the values on a general client machine In Figure 1a RSSI values with a baby monitor were consistently higher than a cordless phone which should be We used a MacBook Pro 2013 networ
12. es are categorized into each group we need another method that further narrows down the root causes In this section we explain the second method designed to distinguish several detailed characteristics of non WiFi devices such as frequency hopping and duty cycle 1 Probing method WiSlow sends bulk UDP packets to the AP and counts the received 802 11 ACKs to check the quality of a wireless link within a given period In order to detect patterns on the scale of milliseconds we use a very small size of UDP packets 12 bytes that reduces potential delays such as propagation and processing delays and we transmit as many UDP packets as possible to reduce the intervals between samples As a result we received 0 7 ACKs per millisecond with an average number of 2 7 in a clean environment In the following sections we describe the results of the above method when performed with various non WiFi inter ferers and we explain how WiSlow identifies the devices based on the results 2 Duty cycle microwave ovens Microwave ovens gener ate severe interference in almost every channel of the 2 4 GHz band We identify this heavy interferer using its duty cycle which is the ratio of the active duration to the pulse period It is known that the duty cycle of microwave ovens is fixed at 0 5 and the dwell time is 16 6 ms 60 Hz 7 This implies that it stays in the ON mode producing microwaves for the first 8 3 ms and the OFF mode for the ne
13. generates a CDF of the distances and compares it to the pre obtained CDFs of each problem source For the convenience of identification we group the problem sources into three groups by the shape of the CDFs no interferers group 1 light interferers group 2 and heavy interferers group 3 Each group has its representative CDFs that are determined by multiple experiments Figure 5 In our data sets group 1 indicates a clean environment group 2 includes channel contention and FHSS cordless phones and group 3 contains microwave ovens and baby monitors WiSlow examines which representative CDF is the most similar one to the user s CDF To compare the CDFs WiSlow uses the Two Sample KolmogorovSmirnov test K S test a widely used statistical method that tests whether the two empirical CDFs obtained from separate experiments have the same distribution 9 If the p value of this test is close to 1 the two CDFs are likely to come from the same distribution however if the p value is close to 0 they are likely to come from different distributions Since the K S test not only considers the average and variance of the samples but also takes into account the shape of the CDFs it best fits the purpose of WiSlow where it is used to pick the most similar distribution from multiple data sets B ACK Pattern Analysis The first method is able to determine which type of loss pattern a problem source has However because multiple problem sourc
14. h We assume that the clients already have WiSlow installed and have contact information of the others 1 e IP address and port number Each probing process takes about 30 40 s thus it took a few minutes to collect the results from the three clients in our experiment Then WiSlow checks whether the other clients have also detected the same type of interference VI RELATED WORK Airshark 10 uses a commodity WiFi network adapter to identify interference sources It leverages a spectral scan to obtain signal information from multiple frequency ranges It identifies the interference sources very accurately over 95 by analyzing the spectrum data using various methods However it is not easy to apply this approach for typical end users because collecting high resolution signal samples across the spectrum is impossible if the network card does not support this functionality WiFiNet 11 identifies the impact of non WiFi interference and finds its location using observations from multiple APs that are running Airshark Although the authors briefly mention that WiFiNet could be used by end users they focus more on building infrastructure using APs while WiSlow focuses on end users and their cooperation to identify the location of the interference source Kanuparthy et al 8 proposed an approach similar to WiSlow in terms of using user level information to identify interference sources They distinguished congestion channel contentio
15. he source of the problem Based on our experimental results and heuristic methods we have developed an algorithm that successfully distinguishes channel contention from non WiFi interference and infers the product type of the offending device We believe that this tech nology will be useful to end users since it can inform them of what needs to be done in order to improve the performance of their networks whether to upgrade their Internet bandwidth or remove a device that is emitting the interference In non WiFi interference scenarios another goal is to identify the location of the source of interference Although it is difficult to pinpoint the exact physical location of the source without the support of hardware or APs we can infer the relative location of the problem source by collaborating with other end users connected to the same wireless network WiSlow collects patterns of variables from peers and deter mines whether others observe the interference at the same time If all the machines observe it it is highly likely that the problematic source is close to the wireless AP However if only one of the peers observes the interference the source is likely to be located close to that peer Our experimental results clearly show that this approach is feasible In summary WiSlow uses information obtained from captured packets and other users to i distinguish channel contention from non WiFi interference 11 infer the product type
16. hone is another example of a device that explicitly shows the hopping patterns that we described above In addition it is known that some FHSS phones have a specific pulse interval which was verified by Rayanchu et al 10 using signal measurement We also confirmed this feature with our user level probes Figure 7c shows the frequency domain of 802 11 ACKs It shows similar patterns as the microwave ovens low duty cycle devices rather than the baby monitors frequency hopping devices even though it also uses frequency hopping This is because the duty cycle influences the shape of the waveform more than the hopping effect Therefore it 1s possible to use this duty cycle to distinguish the FHSS cordless phones as we did for the microwave ovens In this case we use the frequencies 100 and 200 Hz to determine the FHSS cordless phone interference However to the best of our knowledge there is no standard regarding the period of the duty cycle for FHSS cordless phones This means it can vary depending on the product Therefore if a duty cycle is detected but the period is an unknown value WiSlow fails to identify the exact product type In this case we provide our best estimate of the problem source by listing a possible set of candidates V LOCATING INTERFERING DEVICES Once WiSlow detects non WiFi interference and identifies the type of device causing it the next step is to locate its physical location Device localization has been i
17. k card AirPort Extreme chipset Broadcom BCM43 series in this measurement rrene i l BABY MONITOR TET aaa i m ook CLEAN 0 8 e psss PHONE E FHSS PHONE B MICROWAVE OVEN BABY MONITOR 1 ote CLEAN e DSSS PHONE a 0 4 m FHSS PHONE MICROWAVE OVEN a oa fi i i i D a a a ast i i i Go 55 50 45 40 35 30 25 0 25 30 35 40 45 50 55 Values Values b SINR measurement a RSSI measurement Fig 1 Cumulative distribution functions of RSSI and SINR values reversed when the measured UDP throughput is considered In Figure 1b the SINR values with a cordless phone were higher than even a no interference case Furthermore these results varied for each experiment Based on this observation we conclude that RSSI and SINR values captured by a general wireless cards do not correctly represent the level of interfer ence Therefore we do not use these metrics for other purposes besides as a hint in the case of an extremely weak signal B No specific network adapter or driver We do not make any assumptions about the specific net work adapters or drivers that end users may have Some Atheros chipsets which are widely used in research studies support a spectral scan that provides a spectrum analysis of multiple frequency ranges Rayanchu et al developed Airshark 10 and WiFiNet 11 leveraging this feature to distinguish non WiFi interferers using
18. ment of a wireless network is highly affected by the client s own environment such as distance from the AP signal power or fading multi path and shadowing In other words even though they have the same type of problem the statistics of the measured metrics can vary depending on each end user s own situation Note that this is the reason why simple measurements such as the higher layer throughput e g TCP or UDP or number of 802 11 retries are not enough to identify the level of interference and the type of interferers Therefore to apply our approach to end users it is important that the measured statistics are independent of the underlying environment We found that even if the underlying environment changes the extent of the area where a set of samples correlated packet loss and bit rate are dispersed remains similar if the problem source is the same Figure 3b shows that even though the two groups of samples from discrete environments are distributed on different spots on the plane their extent is similar Thus we first quantify how widely the samples are dispersed by calculating the Euclidean distances between each sample and the mean Figure 4 compares the CDFs obtained from two experiments that were conducted with the same baby monitor in two discrete environments The CDFs of packet loss estimation Figure 4a and bit rates Figure 4b show different distributions while the CDFs of the Euclidean distances of the samples to
19. n by measuring the one way delay of different sizes of packets Then they investigated the delay patterns to distin guish a hidden terminal from a weak signal While this study focused on congestion weak signals and hidden terminals WiSlow focuses on not only congestion and signals but also the detailed identification of non WiFi interference sources VII CONCLUSION We designed WiSlow a WiFi performance trouble shooting application specialized to detect non WiFi interferences WiS low distinguishes 802 11 channel contention from non WiFi interference and identifies the type of interfering devices We introduced two main methods packet loss analysis and 802 11 ACK pattern analysis These methods uses user accessible packet trace information such as UDP and 802 11 ACKs In addition WiSlow leverages peer collaboration to identify the physical location of the sources of WiFi performance degradation REFERENCES 1 AirMaestro http www bandspeed com products products php On line accessed May 2013 2 AirSleuth http nutsaboutnets com airsleuth spectrum analyzer On line accessed May 2013 3 Radiotap http www radiotap org Online accessed May 2013 4 Wi Spy http www metageek net Online accessed May 2013 5 WLAN packet capture http wiki wireshark org CaptureSetup WLAN Online accessed May 2013 6 S Gollakota F Adib D Katabi and S Seshan Clearing the RF smog making 802 11n robu
20. ns cordless phones and baby monitors emit severe interference because these devices operate on the same 2 4GHz spectrum as 802 11b g 6 Although these problem sources can be easily removed in many cases e g by relocating the baby monitor choosing a different channel or moving to the 5 GHz band it is difficult for technically non Savvy users to even notice the existence of channel contention or non WiFi interference Instead properly working routers or service providers are frequently misidentified as the culprit while the actual root cause remains unidentified However iso lating the root causes of poor WiFi performance is nontrivial even for a network expert because they show very similar symptoms at the user level and special devices are required in order to investigate the lower layers of the protocol stack We introduce WiSlow Why is my WiFi slow a software tool that diagnoses the root causes of poor WiFi performance with user level network probes and leverages peer collaboration to identify their location We focus on building software that does not require any additional spectrum analysis hardware unlike e g WiSpy 4 AirSleuth 2 or AirMaestro 1 In addition WiSlow does not depend on a specific network adapter such as the Atheros chipset which were used to achieve similar goals in other studies 10 11 WiSlow runs on a typical end user s machine Currently it Hyunwoo Nam Columbia University Ne
21. nvestigated intensively in many research fields A number of research studies on indoor location tracking have tried to pinpoint the location of client laptops or smartphones using various meth Interference Source Ww Probe 1 G Probe 2 Fig 9 Probing for localizing an interference source ods cite WiFiNet 11 pinpoints the location of the interference source using multiple APs that use Airshark 10 Although we leverage a similar cooperative diagnosis approach we focus on the collaboration of multiple end users instead of APs However it is not easy to pinpoint the exact physical location of the source by end users because they cannot obtain precise signal information Instead we attempt to infer the relative location of the problem source The basic mechanism is that an end user probing client first requests multiple cooperative clients to perform WiSlow diagnostics as described in previous sections Then it checks whether the other client machines observe the same interference as itself If all the cooperative client machines observe a particular type of interference at the same time it is likely that the problematic source is close to the AP because this would affect the entire wireless network However if only one of the clients observes the interference the source is highly likely to be located close to that client A Cooperative Probing Figure 9 illustrates the details of the cooperative probing approac
22. obing on an end user s machine WiSlow only needs to transmit 10 MBytes of packets to identify the root cause which takes reasonable amount of time 20 50 s for a problem diagnosis application 1 Retry and bit rate Since an 802 11 retry and bit rate reduction are both initiated by a packet loss their temporal changes are closely correlated when a packet loss occurs the bit rate decreases by the 802 11 rate adaption algorithm The probability of packet loss then decreases due to the reduced bit rate which lowers the number of retries After that the bit rate gradually increases again due to the reduced packet loss which leads to a higher probability of packet loss and retries In other words if contention or interference exists it causes packet losses and then the bit rate and number of retried packets repeatedly fluctuate during the subsequent data transmission The more interference the more the fluctuations are observed 2 FCS errors Another variable that we trace is the number of FCS errors Intuitively it can be predicted that non WiFi interference introduces more FCS errors than channel contention or a no interference environment because the packet corruptions are likely to occur more frequently when a medium is noisy However in our experiment it turned out that a large number of FCS errors are not necessarily correlated with severe interference On the contrary we often observed that fewer FCS errors occur with severe
23. ract from the 802 11 packets In summary WiSlow can operate properly if the client s machine supports wireless packet sniffing or provides a set of appropriate APIs In the following sections we explain WiSlow s two main diagnostic methods packet loss analysis and ACK pattern analysis A Packet loss analysis First we found that each problem source varies in their packet loss characteristics represented by three statistics 1 the number of 802 11 retries 2 the number of FCS errors and 3 the bit rates In each experiment we measured these values on a client laptop while downloading 100 MB of packets from an AP The values were recorded for each 100 KB of UDP packets received Thus we collected a total of 1 000 samples for each experiment We repeated this experiment for different scenarios including channel contention and non WiFi interferences To simulate channel contention we set up sev eral laptops sending bulk UDP packets to the AP To generate non WiFi interference we placed each interfering device baby monitors microwave ovens and cordless phones close to the AP about 20 cm and measured the effect on the client placed at various distances from the AP e g 5m 10m and 20 m In this study we did not consider the simultaneous interference of multiple devices Note that the client downloaded 100 MB of UDP packets for each experiment to collect a statistically meaningful amount of samples but when actually pr
24. ssion of the peak frequencies if the correlation coefficient is greater than 0 9 Hopping Interval Magnitude Magnitude 8 16 24 32 Time ms 0 100 400 500 sag a a A baby monitor frequency b A baby monitor time domain domain top 10 frequencies 4 x 10 J ol A Magnitude ao ine 0 100 400 500 Time ms 200 300 Frequency c A cordless phone frequency d A cordless phone time do domain main top 10 frequencies Fig 7 The number of 802 11 ACKs per 100 KB of UDP packets with a baby monitor and a cordless phone we consider it to be a frequency hopping device However it is obvious that we cannot conclude that every frequency hopping device is a specific type of device such as a baby monitor Therefore WiSlow needs to take into account the results of both the first method and this second method to identify the problem source precisely For example if a problem source is classified as group 3 by the first method and a frequency hopper by the second method we consider it to be a baby monitor Of course it is still possible that another type of device not discussed in this study has the same characteristics as a baby monitor We discuss this case in Section 4 Fixed frequency analog cordless phones Typical analog cordless phones use a fixed frequency so they usually interfere only with a small number of channels The analog phones we tested only
25. st to cross technology interference In Proc of ACM SIGCOMM 11 Toronto Ontario Canada Aug 2011 7 A Kamerman and N Erkocevic Microwave oven interference on wireless lans operating in the 2 4 GHz ISM band In Proc of PIMRC 97 Helsinki Finland Sep 1997 8 P Kanuparthy C Dovrolis K Papagiannaki S Seshan and P Steenkiste Can user level probing detect and diagnose common home WLAN pathologies Computer Communication Review 42 1 7 15 2012 9 F J Massey Jr The Kolmogorov Smirnov test for goodness of fit Journal of the American statistical Association 46 253 68 78 1951 10 S Rayanchu A Patro and S Banerjee Airshark Detecting non WiFi RF devices using commodity WiFi hardware In Proc of ACM IMC 11 Berlin Germany Nov 2011 11 S Rayanchu A Patro and S Banerjee Catching whales and min nows using WiFiNet Deconstructing non WiFi interference using WiFi hardware In Proc of USENIX NSDI 12 San Jose CA USA Apr 2012 12 A Vlavianos L K Law I Broustis S V Krishnamurthy and M Faloutsos Assessing link quality in IEEE 802 11 wireless networks Which is the right metric In Proc of PIMRC 08 Cannes France Sep 2008
26. the mean show similar distribution Figure 4c Therefore WiSlow can use the above CDFs of the distances to identify the root causes of network interference We prepare these CDFs of each problem source in advance which are obtained from our experiments Then WiSlow traces the Pe tetas 1 c e pa Y 0 8 j BABY MONITOR Ex1 0 8 BABY MONITOR Ex1 0 8 p BABY MONITOR Ex1 i Pai eee BABYMONITOR EX2 BABY MONITOR Ex2 S fff fF E BAB Y MONITOR Exa z r 0 6 a Pat 4 0 67 0 6F z E i Pa O z 0 4 a 0 4 0 45 fi if 0 2 Foe f 0 27 0 2 ad M a hill is L i i i t yY i L i L i mAsa HE L L 1 L i Ll Ll OT 10 15 20 25 30 35 os 20 25 ya 35 40 4 amp W 5 5 10 15 20 2 30 35 40 EAs ps Distance a The estimated number of packet loss b Bit rates c Distance Fig 4 The CDFs obtained from two experiments with the same baby monitor in different environment 100 MB of UDP packets were transmitted from the AP and the values were sampled every 100 KB oe Group3 eGroup1 Group2 4 0 6 0 4 0 2 0 2 4 6 8 10 12 14 16 18 20 Distance Fig 5 The packet loss analysis groups the problem sources into three groups 1 a clean environment 2 contention and FHSS cordless phones and 3 microwave ovens and baby monitors wireless packets on an end user s machine
27. ts caused the degradation of throughput rather than noise from other sources The impact of a hidden terminal is not considered in this section Furthermore we found that the correlation between bit rate and the estimated number of packet losses shows clearer differences among various problem sources In Figure 3a the majority of the samples from clean environment are distributed in a healthy zone higher bit rate and low packet loss while the samples of baby monitors and microwave ovens are widely dispersed on the coordinate plane WiSlow uses the correlation of these two variables to distinguish the level of interference Although the problem sources each have their own dis tribution patterns on the above scatter plot an end user cannot infer a root cause by simply matching the measured Statistics with the results of our experiments This is because 80 70 L J 60 Ha BABY MONITOR e CLEAN 50 A FHSS PHONE Y 40 Tae t LOSS 30 20 10 10 20 30 40 AVAILABLE BITRATE a Bit rates and the estimated packet loss with differ ent interference sources 50 BABY MONITOR Ex1 BABY MONITOR Ex2 40 30 LOSS 20 gt 10 b Bit rates and the estimated packet loss with the same device a baby monitor in different environment Fig 3 The distribution of the correlation of bit rates and the estimated packet loss the measure
28. w York NY USA Email hn2203 columbia edu Henning Schulzrinne Columbia University New York NY USA Email hgs cs columbia edu runs on any machine that supports wireless packet sniffing enabled by the 802 11 monitor mode We trace behaviors of 802 11 networks such as retries Frame Checksum Sequence FCS errors packet loss and bit rate adaption which can be observed on ordinary operating systems Our experimental results show that the statistical patterns of the above variables vary depending on the problem sources For example in the case of non WiFi interference we observed a greater number of retried packets fewer FCS errors and larger variations in the bit rates compared to channel contention Correlating these variables we can categorize the sources of performance problems into several distinct groups In addition the non WiFi devices such as baby monitors cordless phones and microwave ovens have different patterns when the number of UDP packets and 802 11 ACKs are plotted over time In this study we elaborate on two main methods packet loss analysis and 802 11 ACK pattern analysis To improve the accuracy of the algorithm WiSlow also uses a heuristic method that considers the history of interference episodes and matches it to the common usage characteristics of various devices e g microwave ovens are often used intermittently for periods of 5 30 minutes whereas baby monitors are used continuously to ascertain t
29. xt 8 3 ms This feature can be observed by various means such as using a spectrum analyzer 4 or by signal measurement 10 Our hypothesis was that a user level probe could also detect this on off pattern if the network packets were monitored on a millisecond timescale because the packets would be lost only when the interferer was active on mode To validate this assumption we implemented the above method and plotted the number of successfully received 802 11 ACKs per millisecond As a result a clearly perceptible waveform with a 0 5 duty cycle is observed in Figure 6a the number of ACKs is over five for the first 8 ms and zero during the next 8 ms This pattern repeats while the microwave oven is running This result becomes clearer when it is converted to the frequency domain Figure 6b The highest peak 1s at 60 Hz which means the exact cycle is 16 6 ms This number is exactly the same as the known duty cycle of microwave ovens Consequently if a perceptible cycle is detected from this probing method and the period matches a well known value WiSlow determines that the current interference is due to a This frequency could be 50 Hz in other countries e g Europe and most of Asia where 50 Hz AC power is used Duty Cycle 10 i cb 8f S g 2 5 D ia 6 4 f wo 4 J x it WY 50 Time ms Pas 1 455 1 46 aa 1 47 1 475 1 48 05 100 200 300 400 500 ime 4 10 Frequen
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