A Measurement Study of Low-Power Wireless

Transcription

1 A Measurement Study of Low-Power Wireless Abstract We present observations from empirical measurements of a low power wireless technology. Our observations provide insights in to the behavior of wireless links for traffics with small and long inter-packet durations. We explore the reasons for well known phenomena such as link asymmetries and correlated packet losses. Our observations are also applicable to similar wireless technologies such as that operate in the same ISM band. Our observations have deep implications to the design of MAC, routing, retransmission and link estimation techniques. 1. Introduction This paper is a study of the data link and physical layers. It seeks to answer the following question: What are the causes of packet delivery success and failure in low power wireless networks, how can a node identify them, and what are their implications to system design? This study was motivated by the fact that low-power wireless is becoming an increasingly important area of computer networking. Higher power wireless LAN technologies, such as b, are useful in a wide variety of scenarios, but many devices and applications benefit from lower power technologies. Bluetooth, for example, has gradually become a dominant short-range wireless peering protocol, while is being deployed for smart homes, personal-area-networking, industrial monitoring, and emergency response. Furthermore, the sheer volume of these low power devices is staggering: mobile phones alone have passed the 2 billion unit mark worldwide. Bluetooth s restricted communication model - a connectionoriented data-link layer with synchronized frequency hopping - limit its utilization to mobile, peer-to-peer, and scalable applications. In contrast, s flexibility has allowed it to become the leader of low-power general networking. It serves as the basis for the ZigBee commercial networking standard [2], is at the core of an IETF effort to bring 1 IPv6 networking to personal area networks (PANs), and is bundled with the most popular wireless sensor network platforms (MicaZ, TelosB, TMote, and Intel Mote). When a new physical communication medium emerges, the natural tendency is to simply apply existing algorithms and research to the new technology. In many cases this approach works well. But the large literature on wireless TCP [4], the fact that AODV implementers still face significant implementation challenges [7], and recent work on long-distance WiFi [17] shows that wireless in particular often presents unforeseen challenges that go against expectations. In the case of , this challenge has led to number of experimental studies to quantify how the wireless technology of choice for personal computers behaves in different environments [3, 22]. Identifying how is similar to or different from existing wireless technologies would guide algorithm design and protocol development for low-power wireless devices. Compared to Bluetooth and , there are few studies of While has similarities with these more mature technologies that we can benefit from when studying it, it is intended for applications with different price points and networking requirements. Batteries dominate a device s form factor and communication dominates how it spends its energy. Rather then focusing on the traditional point-to-point IP model, deployments commonly use one of a variety of other communication models and network stacks such as ZigBee, TinyOS, or Dust. Coupled with the energyconsciousnesse of these devices, thobservations emphasize the need for a fundamental understanding of this technology to facilitate the design of efficient protocols and algorithms. Existing research on low-power networking has focused on previous generations of radios with frequency band and modulation techniques that differ significantly from We defer a discussion of these studies to Section 9. However, the successes and failures of this previous research provided guiding principles of our experimental methodology and questions to investigate. This paper starts with a series of studies showing that links in a network have a bimodal distribution. Unlike what has been observed in prior studies of other tech-

2 Observation Section Short periods have few intermediate links. The portion of Sec. 3 links that are intermediate increases with time. The Signal-to-Interference-and-Noise Ratio (SINR) is a Sec. 4 good predictor of packet reception. Intermediate links are links that oscillate between excellent and poor. RSSI between a node pair varies across channels. Sec interferes with most channels. In shortarea PANs, shared observations of prevents the hid- Sec. 6 den terminal problem. Per-node RSSI and noise floor variations causeo asymmetric Sec. 7 links. Table 1: Summary of observations and their implications. nologies, has few intermediate links: at any time, almost all have a reception rate of 0% or a reception rate of 100%. Furthermore, the distribution of link qualities changes depending on the length of time period examined. The rest of the paper explores why this is the case and how nodes can best distinguish good links from bad ones given their energy budgets. Table 1 summarizes our findings. We conclude in Section 10 with a discussion of how and why our results differ from prior studies of other technologies. 2. Hardware This section describes the radio, platforms, and testbeds of our experimental study. 2.1 CC2420 The CC2420 is an compliant transceiver has 16 non-overlapping channels, spaced 5 MHz apart, which occupy frequencies MHz uses a direct sequence spread spectrum OQPSK modulation to send chips at 2MHz. 32 chips encode a 4-bit symbol, providing a physical layer bandwidth of 250kbps. The CC2420 uses soft chip decision: rather than convert chips to bits and match against the encodings, it decodes by choosing the symbol which maximizes chip correlation shares the same band as b and Bluetooth. The CC2420 attaches two pieces of metadata to every received packet, RSSI and CCI. It measures both over the first eight symbols (32 bits, 125µs) of a received packet. The RSSI (received signal strength indicator) is the RF signal strength, in dbm. The CCI (chip correlation indicator) ranges from 50 to 110 high is good and is a measure of chip correlation. Roughly speaking, CCI represents a bit error rate. The CC2420 only calculates CCI on received packets, but continuously calculates RSSI. Software can read the RSSI register of the radio at any time in order to measure ambient RF energy. 2.2 Platforms The Telos revb mote [27] and the MicaZ mote [28] were our two primary experimental platforms. Both of which have 2 a CC2420 radio. For the sake of this study, the principal difference of the platforms is their RF engineering. Telos motes have an integrated planar inverted F-style antenna (PIFA) printed directly on the circuit board, while the MicaZs have a detachable, quarter wave, monopole antenna connected to an MMCX jack on the MicaZ circuit board. Additionally, they have some different passive components, like the oscillator, their components are placed differently, and the Telos has an RF guard ring whereas the MicaZ does not. In addition to CC2420, we also used the EM2420, which is a variation of the CC2420 produced by Ember [8]. We used an EM2420 developer s kit, which is an EM2420 with an Atmega128 microcontroller and an ethernet backchannel. To measure the interference effects of b on , we used a Dell Optiplex SX280 (a small form-factor PC) with a USB b card attached and a Sony VAIO with integrated b. To measure the interference effects of Bluetooth, we used the laptop/pc pair with USB Bluetooth adapters. 2.3 Testbeds Most of the experiments use the Intel Mirage testbed [14] of 100 MicaZ nodes and a university testbed of 30 Telos nodes spread over approximately 2500 square feet. Both testbeds have their nodes on the ceiling. The Mirage testbed represents an ad-hoc network spread over an entire floor of a large building, while the university testbed s size more closely resembles a small home. Unlike Mirage, which is a public resource, the university testbed was under our control. This allowed us to swap nodes for experiments and otherwise alter the environment. Both of the indoor testbeds have wired backchannels for controlling and communicating with their nodes. Nodes in the Mirage testbed transmitted at full power (0 dbm) while nodes in the university testbed transmitted at -25dBm. In addition to the two wired testbeds, we used several short term setups to explore specific questions which emerged from our iterative analysis. The field testbed consisted of 11 Telos nodes arranged in line with 2 meter spacing on 6 cm high plastic boxes in a grassy field in a rural area. The lake testbed consisted of 20 Telos nodes with 4 feet spacing between each other in a dry lakebed on a university campus. The nodes had clear line of sight and were arranged in a linear topology. The interference testbed consisted of the laptops placed four feet apart with the Telos half-way between them. The office testbed had several EM2420 nodes placed in an ad-hoc fashion to analyze multipath effects. The office testbed has a relatively open floorplan, with a combination of brick walls and gypsum wallboard with wooden studs. 3. Timing Many prior studies of wireless networks have observed a a large number of intermediate links with packet reception rates of 10-90% [3, 22, 13, 35, 5, 23]. To determine whether

3 (a) University, IPI=14s (b) Mirage, Channel 11. (c) Lake, IPI=50ms, Ch. 11. (d) University, Channel 26. (e) Mirage, Channel 26. (f) Lake, IPI=50ms, Ch. 26. Figure 1: Distribution of packet reception rates across all possible node pairs. Reception rates are generally bimodal, and the portion of intermediate links increases with larger (IPIs). (a) IPI=10ms an IPI of 10ms, approximately 25 (5%)of the non-zero links have intermediate PRRs. Figures 1(b) and 1(e) show the PRR distributions for the mirage testbed on channels 11 and 26. Channel 11 in Mirage has more intermediate links than channel 26. Furthermore, with the same IPI (10ms), Mirage has more intermediate links on channel 11 than the university testbed does. In contrast, the lake testbed, shown in Figure 1(c) and Figure 1(f), has equally sharp curves for channels 11 and 26. As we discuss in Section 6, these slight differences between the tesbteds is due to b interference. Despite subtle differences, the plots show that for low IPI transmissions have bimodal link quality distributions across different environments and frequencies. Most links have PRR of either >99% or 0%. The bimodality of the links for low IPI traffic means that sending a short burst of packets may an efficient method for estimating link quality. Figure 2 shows the conditional packet delivery function (CPDF) for two IPIs. A CPDF shows the probability that a packet will arrive successfully given n previous successes or failures. The conditional probability for the small IPI shows the bimodal nature of its links. In contrast, the plot for the large IPI shows significant variability. Links are bimodal for short periods of time. Intermediate links are caused by longer-term oscillations between a very high and a very low PRR. (b) IPI=1s. Figure 2: For an indoor testbed, probability that next packet will succeed given previous (n-1) packets succeeded is higher the lower IPI. The negative x-axis values correspond to consecutive packet losses has similar properites, we measured packet reception rates in the university, Mirage, lake, and field testbeds. Experimental Methodology: Nodes sent packets at varying inter-packet intervals (IPIs). Nodes transmitted small IPIs as bursts and took turns when transmitting with large IPIs. In the wired testbeds a PC controlled all transmissions. In the outdoor testbeds nodes self-scheduled and logged all data to flash. Logging prevented outdoor nodes from having an IPI below 50ms. For small IPIs we sent 2000 packets and for larger IPIs we sent 100. Figures 1(a) and 1(d) show the distribution of PRRs in the university testbed. The plots show the distribution for IPIs of 1s and 10ms. With an IPI of 1s, approximately 100 (20%) of the non-zero links have intermediate PRRs. With 4. 3 Signal, Noise and Capture

4 (a) RSSI vs PRR, Traffic with (b) CCI vs PRR, Traffic with IPI = 50 msecs, Attenuator IPI = 50 msecs, Attenuator Experiment, Channel 26. Experiment, Channel 26. (a) Mirage, IPI = 10 msecs, (b) Lake, IPI = 50 msecs, Channel 11. Channel 11. Figure 3: PRR versus RSSI and CCI versus PRR plots for a chosen pair of nodes connected through a variable attenuator. Each data point is for an attenuation level. The error bars show one standard deviation of the measured (RSSI/CCI) values. (c) University, IPI = 10 (d) University, IPI = 14 msecs, Channel 11. secs,channel 11. In section 3, we saw that over short periods link qualities follow a bimodal distribution and that intermediate links are the cause of oscillations between the two modes. This section examines signal and noise, the two principal factors that affect packet reception, finding that these oscillations are due to slight variations in RSSI. We apply these findings to measure the capture effect on the CC2420. Figure 4: PRR versus RSSI in Mirage, university and lake testbeds. Each data point is a directional node pair. The error bars show one standard deviation of the measured RSSI values. time of a channel in wireless communications [20], which is the time over which the channel state remains highly correlated with itself. The RSSI of a link changes over time. The RSSI distributions of links in the university testbed for traffic with IPI of 14 s have greater variance than those in the lake testbed, and Figure 1 showed that the university testbed also a greater portion of intermediate links. Experimental Methodology: We connected two shielded micaz motes through a variable attenuator via shielded SMA cables. At each attenuation level (1 to 64 db) one node transmitted 100 packets with an IPI of 50 msecs. We sampled the RSSI register of CC2420 of both node there were no to measure background (hardware/awgn) noise. We calculated a node s noise floor as the mode of these samples. Figure 3(a) shows a plot of RSSI vs. PRR for the different attenuation levels. The solid vertical line is at -96dBm, the noise floor of the receiving node. The receiver node was not able to receive packets below an SNR threshold of 4dBm. SNR between 4dBm and 6dBm are intermediate links. The tiny bars show that RSSI at each attenuation level was very stable. In the absence of interference, SNR is an excellent determinant of PRR. Figure 3(b) shows that CCI has large variations at intermediate PRRs even on a static channel. This supports Srinivasan et al. s claim that CCI is a probabilistic quantity and so single CCI values should not be used as link quality indicators for intermediate links [25]. As RSSI is the physical phenomenon that determines CCI, we focus the rest of our study on the former Table 2: Distribution of estimated noise floor across 25 motes on University testbed. SSI (dbm) # Nodes Signal We examine the correlation between the RSSI and PRR between two nodes. The data this section presents is from the same experiments as in Section 3. Figure 4 plots RSSI against PRR for different testbeds and IPI values. RSSI is stable for low IPI traffic in indoor testbeds [25]. This is consistent with the notion of coherence Noise The plots in Figure 4 show that high PRR (above 80%) links have high average RSSI (above -87dBm). Below this threshold, however, there is a broad grey region of many different PRRs, with no clear correlation to RSSI. The attenuator experiment showed that SNR forms a very precise and smooth curve. Table 2 shows the distribution of noise floors of 25 nodes in the university testbed. The noise floor varies across nodes and one outlier node has a high noise floor of -94dBm. Given the same RSSI, this difference in noise floor causes different signal-to-noise ratios and thus different PRRs. Figure 4 has several outliers from the general curves. Figure 5 shows a detailed look at the RSSI for a sequence of received packets for a single node. While most of the received packets have an RSSI above -91dBm, a few are as slow as

5 (a) Reception during preamble. (b) Reception after preamble. Figure 5: RSSI variation over time on a single link. The noise floor at the receiver node was -94dBm. The receptions at -90dBm form dense clumps, while the receptions at -91dBm are sparsely scattered. Figure 6: Signal power (marked by circles) and noise power (marked by x) plotted against sequence number for a single link from Roofnet data. Signal and noise both vary significantly over time. -93dBm. If a link is near the cusp of the SNR threshold, then slight variations can cause an intermediate link. Packet-based measurements inherently introduce a measurement bias. Since SNR can only be calculated on received packets, fluctuations in channel conditions can lead to incorrect measurements. For example, a link that oscillates between two different RSSI values one far above the SNR threshold and one far below will appear to have an intermediate PRR but a very high SNR. The observation that SNR is a good predictor of PRR contrasts the findings of Aguayo et al. for Roofnet [3]. Since is in the same frequency band and uses similar modulation, then RF theory would hold that the two may behave similarly. We examined the publically available Roofnet data to examine why it differs from our observations. Figure 6 shows the signal and noise power plotted against sequence 5 Figure 7: Capture effect in the EM2420. If the second packet (B) arrives after the preamble, the radio does not receive it regardless of its strength. number of packets on an intermediate Roofnet link. As noise values are measured at the beginning of a packet transmission after a node has detected a clear channel, they represent a biased sample. Both the signal strength and noise power vary significantly over time. As Roofnet study averaged SNR over one second periods, these variations are lost. Given the mentioned variation, and the fact that average SNR over this second-long period will lead to different averages for the same PRR, it is logical that plotting this average SNR versus PRR will show little, if any, correlation. As packets are discrete events, averaged SNR is a poor predictor of link quality. 4.3 Capture Signal capture is when a node hears two transmissions and is able to recover the stronger one. Whitehouse et al. [32] demonstrated the capture effect for the CC1000, a low power radio with frequency shift keying (FSK) modulation. For the same radio, Son et al. [23] took a step further and measured capture threshold to be 3dBm. Because the CC1000 has a purely software stack, Whitehouse was able to recover stronger packets that arrive in the middle of weaker ones. Other studies have observed the capture effect in radios [31], but noted that after the preamble sequence the chipset would not recover stronger packets. Experimental Methodology: We connected three EM2420 motes using a network of coaxial cables and -50dB attenuators. We varied the power settings in 2dB increments across a range from -2 to -32 dbm for both the transmitters, creating 256 different power setting combinations. For each power setting, the two transmitters sent 100 synchronized transmissions, and we varied the response time to control which transmission arrived first and how long the delay was. Figure 7(a) shows the result of node A transmitting at a range of power levels and node B transmitting at -18dBm. Node B transmits after node A but before A s preamble completes. If A is -12dBm or stronger, its packet is received. If A is -16 dbm or weaker, B wins. Figure 7(b) shows the same

6 experiment where B s packet arrives after A s preamble. In this case, B s packets will not be received even if they are stronger than A s. Like most chipsets, the EM2420 (and the CC2420) do not recover stronger-last capture effects after the packet preamble.. 5. Channel Selection This section explores the relationship between radio channel and link quality. Experimental Methodology: Each node on the Mirage testbed broadcast a burst of 200 packets with a 10ms IPI. The experiment was repeated for each of the channels. We computed the average RSSI at each node resulting from these transmission as a proxy for link quality. 1 Figure 8(a) shows the results of the experiment in which packets are sent in a burst from node 2. The receivers are shown on the x-axis and the channels are shown on the y- axis. The figure shows that different links have different optimal operating channels. For instance, channel 20 provides the highest average RSSI for the directional link between nodes 2 and 4, while channel 12 is the best for the link between nodes 2 and 9. Some links only appear on some channels, but not others. For example, the link from node 2 to 15 only exists on channel 14. These data suggest that no single channel is optimal across all nodes in the network. We next explore whether optimality is symmetric that is, if the same channel is optimal for both directions of communication for a node pair. Figure 8(b) shows the results of a trial with node 3 as the transmitter. Channel 19 is optimal for the link from node 3 to 2, the same as the link from node 2 to 3 in Figure 8(a). This pattern of symmetry appeared on approximately 80% of the links, suggesting that a single channel is optimal for both directions of a link. Finally, we explore whether the optimal channel for a link is constant over time. We ran a second trial of the channel comparison experiment with node 2 as the transmitter, on a different day, as Figure 8(c) demonstrates. Comparing the earlier and later trials reveals that the optimal channel for many of the links changed. For example, the best channel for the link from 2 to 4 was channel 20 in the first trial, but channel 24 in the second trial. Similarly, for the link from 2 to 9, the optimal channel changed from 12 to 11. This variation with time may complicate the process of estimating an optimal channel for a link. Although the optimal channel for a particular link changes with time, fortunately, our results suggest that both directions of a node pair contemporaneously observe the same channel as optimal. 6. Interference 1 To the best of our knowledge, no such experimental study has been performed for either or low-power wireless radios. The results of this study may provide motivation and guidance for designing adaptive channel-hopping techniques operates in the same 2.4GHz ISM band as Bluetooth and far more powerful b. Since all of these systems co-exist in the same wireless spectrum, it is important to understand how b and Bluetooth affect packet delivery. Experimental Methodology: Nodes read raw RSSI samples from the CCC2420 radio on channel 11 at 4Hz for 11 hours. No nodes transmitted packets during this time. Figure 9: Sampled signal strength trace and histogram. (a) Sampled signal strength (dbm) measured over a two minute period at a single node; (b) A histogram of the sampled signal strength over 11 hours measured at the same node. Figure 9 shows a two-minute subset of the values measured at a single node as well as a histogram of the values over an 11 hour period. 59.4% of the samples have a value of -99 dbm, 10.3% have a value of -100 dbm, and 0.002% have a value less than or equal to -101 dbm. The distribution of samples is right-tailed, with more than 8.6% of the samples having a value greater than -85 dbm. Experimental Methodology: Nodes on the university testbed sampled the RSSI register on channel 11 at 128Hz for three minutes. A reference broadcast synchronized when they started sampling. Figure 10 shows the noise samples from six nodes. The minimum calculated correlation coefficient between the traces is This indicates that the noise spikes are highly correlated and likely external to the nodes. We confirmed that the source of the spikes were due to co-located b network access points by observing the difference in the noise spikes measured with and without shielding the near-by b access point (AP). Shielding the AP reduced the peak noise by approximately 15dBm b Although b and share the same spectrum, their channels occupy different bandwidths. Figure 11(a) il-

7 (a) Traffic with IPI = 10 msecs, Mirage, First (b) Traffic with IPI = 10 msecs, Mirage, First (c) Traffic with IPI = 10 msecs, Mirage, SecTrial. Transmitter is node 2. Trial. Transmitter is node 3. ond Trial. Transmitter is node 2. Figure 8: Average RSSI observed as a function of receiver (x-axis) and channel (y-axis) for a single transmitter. Darker color indicates higher average RSSI. The absence of a data point indicates no packets were received on that link and channel. A burst of 200 packets were transmitted with a 10ms inter-packet interval. (a) Node 2 was the transmitter. (b) Node 3 was the transmitter. (c) Node 2 was the transmitter again a repeat of (a) carried out at a different time. These figures demonstrate link quality and radio channel are related and the optimal channel for a link changes. (a) (Bluetooth), b and (b) Increase in average noise for all (c) Increase in average noise for all spectrum usage. channel pairs observed by an channels for an rereceiver during b transmissions. ceiver during a transmissions. Figure 11: Interactions between Bluetooth, b and Bluetooth adds approximately 20dBm to the noise floor while adds between 0 and 45dBm to the noise floor. lustrates how the channels nominally overlap. We note that most wireless b access points use channels 1, 6, and 11 because these three channels are mutually non-interfering. Figure 10: Sampled signal strength over 10 seconds and across six nodes. 7 Experimental Methodology: A Telos node placed between two b devices sampled the CC2420 RSSI register at 4Hz. The b devices were in ad-hoc mode and transfered a large file using FTP. We measured the noise observed at the node for all combinations of and b channels. Figure 11(b) shows the difference in received signal strength cause by the presence of b traffic. An inopportune choice of channel can result in up to 45 dbm of interference from b traffic under our test conditions. The data also indicate that only channel 26 is largely immune to b interference. This data suggests that when selecting an operating channel, one should avoid channels of coexisting b networks, to minimize interference and loss [10, 33]. These packet losses are because b nodes usually do not defer transmission when an packet transmission is in

8 CCA Range Interference Range Node 15.4 Transmitter 15.4 Range 15.4 Receiver Figure 12: hidden terminals are uncommon occurances to 15.4 nodes because of power disparities. For an to be a hidden terminal, the 15.4 transmitter must hear a signal weak enough for clear channel assessment, the 15.4 receiver must be within interference range, and the receiver be at the edge of the transmitters range. progress. This is because of the difference in the transmission power between the two technologies b transmission power is larger than that of by a factor of 100. In contrast, transmissions can prevent clear channel assessment at nodes and increasing latencies. 6.2 Bluetooth Unlike b and , Bluetooth is based on frequency hopping spread spectrum (FHSS) technology. Bluetooth uses 79 different 1MHz channels. Figure 11(a) shows the overlap between Bluetooth and channels. We investigated the effect of coexisting and Bluetooth networks using an approach similar that used for Experimental Methodology: A Telos node placed between two Bluetooth devices sampled the RSSI register of the CC2420 at 4Hz. The Bluetooth devices transfered a large file using the Bluetooth file transfer protocol. We measured the noise observed at the node for every channel. Figure 11(c) shows the difference in received signal strength between the presence and absence of Bluetooth traffic measured in different channels. Interference was as high as 25 dbm. We suspect that since the interference magnitude is on the same order as our observations for the networks, similar implications hold. 6.3 Relation to SNR Unlike 15.4, which is assumed to have low utilization, a copresent network might be very busy. While might experience interference from 15.4, there is a 100-fold difference in output power: chipsets have an output power of 15-23dBm, while 15.4 chipsets are typically -3-0dBm. Figure 12 shows how in practice, the disparity lowers the chances of an node being a hidden terminal, as it will only occur if the the signal strength at the 15.4 transmitter is below the clear channel threshold for CSMA, yet the signal strength at the 15.4 receiver is strong enough that it will corrupt the 15.4 packet. However, as has such a lower output power and is narrowband interference, networks are not likely to respond to their transmissions when performing CSMA packets can be much briefer than 15.4 packets: a 300 byte packet at 11Mbps is approximately 200µs, while a 30 byte packet at 256kbps is 1ms. While most data packets are 1500 bytes, acknowledgements and other control traffic often has smaller payloads. Therefore, a 15.4 node can detect a clear channel, start sending a packet, and receive a corrupting burst of mid-packet interference. These properties help explain the link PRR distribution curves of Section will generally overwhelm traffic, making perfect PRRs rare. This is why channel 11 in Figure 1(a) has a long tail of links with PRRs of 90-99%. In contrast, channel 26, which overlaps least with , has many 100% links. The spatial properties of and the university testbed also explain why its PRR curves were steeper at low IPI values than those of Mirage. Because the university testbed is at a lower power level and smaller, nodes are more likely to share observations of transmissions. Therefore, is less likely to be a hidden terminal. Finally, the lake testbed, being far from any access points, looks the same on channel 11 as it does on Bi-Directional Link Behavior In this section, we study asymmetry, a common phenomena in wireless networks. We examine the commonality of asymmetric links in networks and explore the causes of these asymmetries. Experimental Methodology: 30 nodes on the Mirage testbed sent 200 unicast packets to each other node with an IPI of 10ms. Receivers sent the RSSI, CCI and sequence number of every packet to a logging PC over a wired backchannel. This approach means the two directions of a node pair may have been measured several minutes apart, while the PRR is calculated over a very brief interval of only 1 sec (100*10 msecs). We ran this experiment once on a Wednesday evening and once on a Saturday morning. We define a bidirectional link between a node m and a node n to be asymmetric if P RR m P RR n > 0.4 Figure 13 shows the results from our experiment. While has asymmetric links, only two of the 16 asymmetric links are persistent across the two experiments. This implies temporal variations in link asymmetry. To determine the time scale of variations in PRR asymmetry, we examined the data from an experiment with 15 sec IPI traffic and calculated link asymmetry over four separate one hour periods. Figure 14 shows the results. A few links such as N17 N4 are consistently asymmetric while others such as N18 N10 are not. Furthermore, the number of asymmetric links in each period is significantly less than what was observed in the experiments with an IPI of 10ms. These results reemphasize that there are significant differences between long-term and short-term link behavior. In Section 6, we saw that a cohabiting b or Blue- Tooth network can create significant interference This lowers the SNR and leading to significant packet losses. External

9 (a) Traffic with IPI = 10 (b) Traffic with IPI = 10 msecs, Mirage, Channel 11, msecs, Mirage, Channel 11, Wednesday Saturday Figure 13: The nodes are shown in a circle solely for visualization purposes, nodes close to each other on the circle were close to each other in the testbed. Nodes having asymmetry are connected using a colored line, where the red end of the line is the node that had trouble receiving packets. A larger gradient on the line indicates higher asymmetry. While each trial had a significant number of asymmetric links, there are only two (N14-N26 and N17- N4) present in both. (a) Traffic with IPI = 15 secs, (b) Traffic with IPI = 15 secs, Mirage, Channel 11, First Mirage, Channel 11, Second Hour Hour (c) Traffic with IPI = 15 secs, (d) Traffic with IPI = 15 secs, Mirage, Channel 11, Third Mirage, Channel 11, Forth Hour Hour Figure 14: Hour-by-hour asymmetry plots for a four hour IPI experiment on the Mirage testbed with IPI = 15 secs. The visualization methodology is the same as in Figure 13. A small number of links such as N17 N4 are consistently asymmetric and there are also transiently asymmetric links such as N18 N10. Node 4 also seems to be a bad node, in that many of the stable asymmetric links have it as a bad receiver. 9 Figure 15: Average noise at node 4 and RSSI of packets received from all nodes for traffic with IPI = 15 secs on Mirage. The circle on each vertical line marks the average RSSI while the ends of each line correspond to the minimum and maximum RSSI of packets received from that node. It receives no packets below its noise floor (-93dBm), and very few below one standard deviation above that (-90dBm). noise can cause asymmetry if it affects only one side of reception. With low IPI traffic (10 msecs), this is a plausible hypothesis that an abnormal interference spike could have a significant affect on PRR for a single direction only. However, with larger IPI traffic, the external noise would have to consistently affect only one of the nodes over a relatively extended period of time. Figure 15 shows node 4 s view of incoming traffic. Node 4 receives no packets below its noise floor (-93dBm), and very few below one standard deviation above that (-90dBm), consistent with the notion of an SNR threshold (Section 4). Its noise floor is also one of the highest in the network. Looking at Figure 14, node 4 had four asymmetric links, with nodes 17, 19, 22, and 30, appearing in at least 3 of the 4 one hour periods. In each of these asymmetric links, node 4 was the bad receiver. n Figure 15, shows that all of them are on the edge of receivable RSSI. One, in particular, 19, has no successfully delivered packets. Examining the reverse direction, node 19 s noise floor was -98dBm, and the average RSSI of received packets from node 4 was -93dBm; without significant RSSI asymmetry in its favor, node 4 is unlikely to receive any packets. Another factor that can contribute to PRR asymmetry is RSSI asymmetry. Figure 16 shows a distribution of the RSSI asymmetries in the low rate experiment. The largest asymmetry is 6dBm. If noise floor differences and RSSI asymmetries are correlated, this would mean that there is some miscalibration issues in the analog to digital converter (ADC). If noise floor differences were due to ADC miscalibration then this would invalidate our theories on the grey region in the RSSI versus PRR plot and on the causes of PRR asymmetries. Figure 17 shows the difference between RSSI and noise floor plotted for one link against that of the opposite link. If the RSSI and noise floor were correlated we would have seen all the points to be on a straight line. However, Figure 17 shows no such pattern suggesting that the asym-

10 Figure 16: Disribution of RSSI asymmetries on Mirage with traffic with IPI = 15 secs. 50% of all communicating node pairs have pairwise RSSI differences of 2dBm or below. (a) Traffic with IPI = 1.2 (b) Traffic with IPI = 1.2 msecs, Office testbed, 12:00 msecs, Office testbed, 02:00 AM. AM. (c) Traffic with IPI = 1.2 (d) Traffic with IPI = 1.2 msecs, Office testbed, 04:00 msecs, Office testbed, 06:00 PM. PM. Figure 17: SNR asymmetry for traffic with IPI = 15 secs on Mirage. Figure 18: PRR over space for a short packet burst with inter-packet interval (IPI) of 1.2 msecs. X and Y axes show the position of the transmitter on a 10x10 grid with each grid side measuring one tenths of the wavelength (1.25 cms). PRR is correlated over small distance when variation in the channel is minimal. metries in RSSI and noise floor are not due to ADC miscalibration issues. Consequently, the cause of RSSI asymmetry still remains unanswered. Son et al. [23] present on theory, suggesting that RSSI asymmetries are due to oscillator miscalibration issues. Unfortunately, as Mirage is a public testbed, we were not able to take the nodes in question and examine them with a spectrum analyzer. 8. Location Effects Having looked at a host of other factors affecting networks, in this section we explore the effect of multipath fading in an indoor office environment. Experimental Methodolgy: We used two EM2420 motes, one as a transmitter and the other as a receiver. Both the motes also featured an Ethernet back channel which allowed us re-program the microcontrollers, initiate tests and gather results from each board. The receiver was mounted 7ft above floor level on an interior wall of the office testbed. The transmitter was sitting near the middle of a large wooden conference room table, 14.1 meters from the receiver. Both the transmitter and receiver had a quarter-wave dipole antenna. The transmitter was placed on a square printed paper grid, 12.4 cms on a side (one lambda at GHz). The grid was subdivided into 10 x 10 squares, each 1.24 cms on a side. For each sub-test, the antenna of the transmitter 10 was placed directly over one of the 100 squares. Each subtest was a straightforward packet reception rate test: the transmitter broadcast 100 packets with an IPI of 1.2 msec. Each packet had a 24 byte payload and was transmitted at -1 dbm. If the receiver received the packet with no CRC errors, it was recorded as a success, otherwise as a failure. The result of each sub-test was the percentage of correctly received packets. No RSSI data was gathered. We conducted multiple Multipath experiments at different times of the day to observe multipath effects with and without variations in the channel. We ran five tests: two of them were ran at night when there was minimal human activity and three during working hours. Figures 18(a), 18(b), 18(c) and 18(d) show how the PRR changes over space during different times of the day. Figure 18(a) shows the variation of PRR over space for a single link at 12:00 AM when there is minimal human activity. Even with minimal channel variations, PRR changes between 0 and 100% over just one wavelength (12.5 cms). We attribute this variation in PRR to multipath effects. Small variations in distance can cause the multipath components at the receiver to constructively or destructively add, thus, effectively increasing or decreasing the RSSI respectively. Such variations in the signal power will cause the SNR to change and thus affecting the PRR. We also note that the PRR is highly correlated over very short distances on the order of two tenths of a wavelength. We ran the same test again at 2:00 AM. Figure 18(b)

11 shows similar variation of PRR over space. We note that, overall, the locations of high and low PRR remain the same in both the tests. This is consistent with the notion of a staic channel. In a static channel the location of constructive and destructive interference remain the same. As both the tests where done during the night, the channel must have had minimal variations during both the tests. We do observe a couple of locations to have different PRRs in the two tests, which can potentially be attributed to these minimal variations in the channel between the tests. Figures 18(c) and 18(d) show the spatial variation of PRR for tests ran at 4:00 PM and 6:00 PM respectively, when there was some human activity. Clearly, the PRR is changing over short distances as before. The locations of high and low PRRs also change significantly between the two figures. This is possibly due to the difference in variations of the channel state during these tests. Variations in the channel will change the multipath profile of the channel, thus changing the locations of constructive and destructive additions of multipath components. 9. Related Work A sizeable library of literature exists that explored wireless communication technologies, particularly The results of such studies were instrumental in pointing out gaps and uncertainties in our knowledge of the space, and consequently served as important considerations when designing our own experiments. In this section, we review these prior works, pointing out the key findings and the impact this had on our work. 9.1 Prior Work with In [3], Aguayo et al. observed packet delivery behaviors in a 38-node long haul urban mesh network. In their study they found no correlation between PRR and SINR, concluding instead that the packet losses were most likely due to multipath effects. In contrast, our results actually point to SNR as a good predictor for PRR in networks. This discrepancy is potentially due to measuring methodologies: in [3] they average SINR over 1-second intervals, while we use a more fine-grained approach as outlined in section 4. Reis et al [22] carried out an indoor experiment with nodes. They observed pair-wise links to be fairly stable over short durations. Our findings are similar to those of [22], displaying stability over short periods but increasing variability proportional to duration length. Reis et al. also examined link asymmetries and found them to be locationspecific. Previous studies for low power wireless nodes [5, 13, 35], however, found asymmetry to be node-specific, and location independent. We ran experiments as outlined in section 7 with and without swapping nodes that saw asymmetries. We found that the asymmetries were infact nodespecific. It is possible that external noise was at work in 11 the experiment from Reis et al s work as external noise can cause packet losses at a node closer to it making the asymmetry location-specific. The authors confirmed [21] that one of the nodes with the asymmetric link was under desk with running machines and that the node was seeing more noise values than the other node. Koksal et al. [15] used data from Roofnet to come up with routing metrics that capture short-term variability of links. They have proposed the use of modified ETX (metx) and effective number of transmissions (ENT) as new routing metrics that consider both the link variability and the higher layer requirement. In Section 7, we briefly explore the implications of acknowledgements to routing metrics. 9.2 Prior Work with Low Power Wireless Nodes Experiments with early mote platforms demonstrated the complex dynamics of low-power wireless networks. The resulting observations have guided the design and implementation of numerous protocols and system stacks. However, the failure of deployments of these networks to behave as anticipated indicate that the dynamics of these systems are still a mystery to developers, and as such warrant another look. We overview previous such efforts, distilling a set of factors and considerations that remain uncertain or unknown, whose investigation may provide the knowledge that bridges the gap between research evaluation and practical use. We discuss many of these factors as part of our study in subsequent subsections Deployment Experiences Szewczyk et al. [26] presented network data from a deployment on an island off the coast of Maine. The design of the network assumed significant end-to-end packet losses would occur and so oversampled the environment. They measured packet delivery performance for a single-hop and a multihop network which used Woo et al. s algorithms. PRR was initially satisfactory, but the multihop network deteriorated over time, with some networks delivering under 30% of its packets, some of which was due to significant base station outages. They note that while only 15% of the links that the routing algorithm selected were stable and long-lived, those links were responsible for 80% of the packets delivered. Tolle et al. [30] reported similarly low yields from a network designed to monitor the microclimate of redwood trees, although in this case much of the network was unable to form a routing topology. Furthermore, approximately 15% of the nodes in the deployment died one week into the deployment by exhausting their batteries due to a problem in the time synchronization component of the routing protocol. These results suggest that a gap exists between research algorithms evaluated on small-scale testbeds and their performance in real deployments. While studies have quantified many of the difficulties in low-power wireless that make developing efficient and robust protocols difficult, the underlying causes of these challeneges often remain a mystery. If these root causes are left unexplored, protocols and systems

12 will be designed with reactive rather than proactive mechanisms, resulting in a loss of efficiency. At best the resulting networks will be tuned to work well in a single deployment environment, that in which they were developed. Our work attempts to dig into these root causes and examines the implications of our findings on sensornet system development Packet Delivery Ganesan et al [13] analyzed different protocol layers for rene motes, an early-generation sensor node, showing that even simple algorithms such as flooding had significant complexity at large scales. They observed that many node pairs had asymmetric packet reception rates, which they hypothesized were due to receive sensitivity differences, which Cerpa et al. [5] supported after swapping asymmetric node pairs and finding that the asymmetries were a product of the nodes and not the environment. In order to better understand packet reception asymmetries, Woo et al [34] looked at packet reception rates (PRR) over distance for mica motes. They found that for a large range of distances, PRR and distance had no correlation and attributed this to hardware miscalibration. Zhao et al [35] confirmed the prevalence of this grey region but tentatively concluded that multipath effects were the probable cause, noting that further study was needed. All of these studies measured early mote platforms (e.g., rene, mica, and mica2) whose data-link stacks (e.g., encoding, CSMA, start symbol detection) resided primarily in software. Ganesan et al. [13] showed that packet collisions, hidden terminals, link asymmetries, and the broadcast storm problem [16] make flooding a problematic approach for building trees. Whitehouse et al. demonstrated that frequency shift keying (FSK) radios, such as those on the mica2 platform, can recover from packet collisions where the stronger packet starts later by constantly looking for a start symbol [32]. Son et al. [23] took one step further and measured a precise RSSI envelope for when mica2 packets can be recovered. They showed that if the signal to interference plus noise ratio (SINR) is above a threshold, PRR is very high (> 99.9%), and that this threshold varies for different nodes. These results suggest that SINR may be a good way to understand PRR more generally. If noise behaves in a simple fashion and RSSI values are stable over time, then RSSI might be a good determinant of packet delivery success or failure. Cerpal et al. showed that PRR rates can change significantly over time, so that long-term PRR calculation can lead to very inaccurate results [6], suggesting instead that an instanteous measure of RNP required number of packets was preferable to a long-term PRR Sensor Networking The conclusions of these experimental studies have greatly influenced sensor network protocol and system design. The grey region and link asymmetries have led some routing protocols to incorporate link estimation algorithms that maintain tables of candidate next hops. For example, because initial 12 studies suggested that RSSI may not be well correlated with packet delivery success or failure, Woo et al. used packet sequence numbers to directly estimate PRR [34]. The expense of doing so, however, has led several more recent protocols, such as TinyOS s Drain and MultiHopLQI [29], as well as Moteiv Corporation s Boomerang [9], to use single samples of the chip correlation indicator (CCI) of the CC2420 radio as a measure of link quality; to the best of our knowledge, there are no evaluations of this approach in the literature. Mote data link layers generally have a CSMA MAC and use a constant randomized backoff policy. If the link layer detects an active channel, it selects a backoff timer from a uniform distribution over [a, b], where b is usually under a packet time. Each successive busy channel detection resets the timer in the range [a, b]. Some link layers, such as the mica2, will backoff indefinitely, while others, such as the CC2420, will give up after a number (e.g., 8) tries. Many TinyOS ad-hoc routing protocols use transmission queues to absorb bursts of forwarding traffic. TinyDiffusion [12], for example, maintains a 12-packet queue 2, beacon vector routing [11] maintains a 32-packet queue 3, and the TinyOS AODV [1] implementation 4 uses a 10-packet queue. All of these queues use an immediate retransmission policy. Once the link-layer finishes sending a packet, the queue immediately submits the next packet for transmission, where it enters the data-link layer backoff. There is no mechanism to modify queue entries once they are submitted. Together, this means that routing layers often enqueue several packets to a single destination, then transmit them backto-back very quickly. This approach may be appropriate if packet losses are independent and identically distributed, but is not appropriate if packet losses are correlated. 10. Discussion In this paper we have presented several key observations on the behavior of low power wireless networks. In this section we seek to move beyond these observations, pointing out the implications of such findings on future low power wireless research and development. One of the overarching observations of this paper is the importance of timing in low-power wireless networks; many of the other observations fall out from this key principle. Section 3 demonstrated that for short-time periods, on the order of hundreds of milliseconds, link behavior remains largely constant, with packet reception rates forming a bimodal distribution of good and bad links. However, over longer periods of times, link quality can change, often significantly. Consequently, link estimates are often rendered obsolete for traffic with larger inter-packet intervals. The most immediate implications of such an observations are on transmission patterns utilized by scheduling algorithms. When possible, 2 tinyos-1.x/contrib/tinydiff/tos/lib/txman TM.nc 3 tinyos-1.x/contrib/bvr/tos/commstack/bvrqueuedsendm.nc 4 tinyos-1.x/contrib/hsn/tos/lib/simplequeuem.nc