Clear Channel Assessment in Wireless Sensor Networks - cse.sc.edu

Clear Channel Assessment in Wireless Sensor Networks J. Collin Engstrom, Chase Gray, and Srihari Nelakuditi

Abstract—Wireless sensor networks are a rising commodity that demonstrate merit in the research domain due to their versatile nature. While powerful and feature-laden, these systems also require careful configuration for optimal packet transmission. This experiment aims to examine this phenomena to some extent. By varying clear channel assessment threshold (CCAT) values, it will be demonstrated that changing such thresholds influences overall efficiency. Conversely, it will be shown that mis-configuring a single node will only impact the network under certain conditions. This work may be parlayed into more elaborate energy conservation and transmission efficacy schemes.

I. INTRODUCTION Wireless sensor networks (WSNs) are a branch of technology that demonstrates remarkable potential. These systems are comprised of multiple sensor nodes called motes. These motes can be likened to miniature data miners. When deployed in a given environment, they measure a panoply of conditions, ranging from temperature to humidity to velocity. As opposed to traditional isolated sensors, though, most motes are affordable enough to be deployed en masse, forming a robust mesh topology. Oftentimes motes will be strategically clustered in a given area so that if a single node ceases to function, it will not devastate the entire mesh network. In this setup, the nodes may form multi-hop architectures and pass messages between one another. Ultimately the data concentrates at a gateway (or sink) node where it may be passed to an external network or otherwise processed [1]. Versatile in nature, this equipment can be tailored to suit individual projects. So far WSNs have shown success in monitoring habitats [2], fires (for firemen) [3], volcanoes [4], and even underwater environments to some extent [5]. Despite this progression, the area of sensor technology is just gaining a foothold. With this power comes certain issues that must be taken into consideration. One of the predominant concerns in this technology is packet transmission efficacy.Transmissions must be able to effectively carry the data to its destination. If improperly configured for the given environment, this may not happen [6]. One

aspect that must be considered is that motes, like other wireless devices, will determine if the area is free of signals before sending. That is, the motes must query the wireless channel before communicating. Like a human conversing, a single mote will ”wait its turn” if there is evidence that another mote is using the channel. Should the node proceed with packet transmission at the same time as another, a collision will likely occur and both packets will be dropped entirely. Referred to as listen before talk (LBT) communication, this methodology is the subject of much current wireless research. Part of the problem from a mote’s perspective is knowing when exactly to transmit. A transmitting node may interpret other negligible activities as inhibitive to a successful transmission and wait erroneously for the channel to free up. This results in a drop in performance; in the time in which a node would otherwise transmit data, it instead waits for the channel to open. To allay some of the confusion inherent in packet transmissions and packet collisions, two broad methods for determining the state of the channel were introduced: energy detection (ED) and carrier sensing (CS). The latter of these involves scanning traffic in the channel for meaningful data. It may search for a preamble, or special string of bits, preceding a package (preamble detection or PD) or monitor for a signal that would represent a genuine packet (decorrelation) [7]. If a preamble or packet can be distinguished, the node must assume the channel is busy and transmit the packet later. Energy detection is somewhat of a less sophisticated method for detecting activity on the channel. As opposed to determining the relevance of any activity, this detection typically uses a process called clear channel assessment (CCA) to determine if the channel is open for transmission. CCA checks the signal energy on the channel before transmitting. The basic assumption under CCA is that a packet being transmitted will carry a signal intensity (called a received signal strength indication or RSSI) high enough to exceed a specified threshold and that all extraneous noise will fall below the threshold and be ignored. If the channel detects an RSSI value above

the clear channel assessment threshold (CCAT), it assumes the channel is in use by genuine traffic and will postpone packet transmission [8]. This naturally raises the question of how a given threshold value will affect the overall throughput of the network. The answer to this may likely aid in mitigating transmission limitations currently in place. The controlling purpose of this paper is to focus on how varying a CCAT value among all sensor motes affects the overall network throughput. It will also investigate the ramifications of a single misconfigured node.

CCAT V alue 0 1 2 3 4 5 6 7

dBm -70 -50 -40 -30 -20 -10 -5 -1

Hex 0x00BA 0x00CE 0x00D8 0x00E2 0x00EC 0x00FA 0x00FB 0x00FF

Fig. 1. The CCAT Values Used on the Motes.

II. CCATVARIATION AND MIS-CONFIGURATION

hexadecimal a la two’s complement. (See figure1for the decimal and hex values used.) Finally, to ensure control over the experiment, the power level on all of the nodes was set to -25 dBm and all other variables were held constant. After the testing environment was established, the actual test phase began. All eight of the nodes were disseminated throughout a small office space and allowed to form a stable wireless mesh network. The topology consisted of most traffic being routed through one of the forward nodes which was positioned next to the sink node. Other sensors and forward motes were placed in strategic places, some of which included on top of the microwave, on desk tops, and on table tops. A sink node was attached to a workstation running a modified copy of EasySen’s ListenSBT80v2 Java application; this accepted all of the packets and tracked the number of bytes from packets that were successfully received. After ten minutes, the Java application terminated and the entire byte throughput for the duration was given. This process was then repeated two more times under the same CCAT value. This three-iteration testing process was repeated for each of the remaining CCAT values, using the user button on the motes to cycle between values. Ultimately, eight different CCAT values were tested with three independent tests each, thus giving a grand total of twenty-four test runs.

This section explains how the work with CCAT values and a mis-configured node was accomplished. A. Phase I: CCAT Value Testing The main objective of this part of the experiment was to find out how varied CCAT values would affect network traffic. For the purposes of this experiment, network throughput was defined as the number of bytes of successfully received packets at the sink within a ten-minute interval. To test the throughput of various CCAT values, five tmote skydevices made by moteiv and equipped with EasySen’s SBT80 auxiliary board were used. Additionally, three forward motes of the same brand were used to relay packet transmission. Each of the sensor and forward nodes was programmed with a modified copy of EasySen’s MobileNode code. All of them were set to send packets every five seconds that contained information from six of the onboard sensors (light, sound, temperature, velocity, and x and y movement). Additionally, the motes were programmed to cycle between eight CCAT values by pressing the user button. These values ranged from -70 to 0in decimal and were converted to

B. Phase II: Mis-Configured Node Simulation Too often technology does not function as it ought to. In the context of wireless sensor networking, this could be the result of a single malfunctioning node in some cases. To that end, the aim of the second phase of this project was to purposefully program a mote so that it would behave counter to the rest of the network. The process for this phase of the experiment was very similar to the first part in terms of variables and topology. After testing the CCAT values and their corresponding byte throughputs, a misconfigured node was simulated. In order to do so, a node central to the others was selected to be the ”problem” node. To start off, all motes were initialized to their first value (-70 dBm). This is exactly the same as the first test from Phase I. Once again, three tests were run to observe the byte throughput within ten minutes for that particular CCAT value. After this, the CCAT of the mis-configured node was incremented and the same process was repeated. The remaining motes, however,

were left at their default values. In this manner, the effects of having different values for the anomalous node were observed. This, in effect, was to proactively determine what would happen should a mote be improperly programmed or should some other irregularity occur. In all, eight CCAT values were tested three times on the mis-configured node, giving a grand total of twenty-four tests.

III. RESULTS

This section analyzes the results of the CCAT variations and the impact of a mis-configured node. A. CCAT Values

All results from both experiments were tabulated and compared. The first analysis was done on the CCAT variations from the first phase. As can be seen from figure 2, the average byte throughputs for the given CCAT values start at 23226 for the first CCAT value and steadily decline CCAT Value -70 -50 -40 -30 -20 -10 -5 -1

AverageByte Throughput 23226 19012.667 17057.333 17769.333 17404 19043.333 22223.333 16251.333

Student’s two sample T-test was used to test for a significant difference at the 95 percent confidence level between the group with all default values (the control group) and the remaining seven groups (the test groups). Only two groups were tested simultaneously: the group with a mis-configured node and the said control group. For the purposes of this test, the groups can be thought of as independent of one another, as results obtained in the control group do not directly influence those of the test groups. Additionally, it was assumed that the tests, if replicated, would yield a quasi-normal distribution. (These assumptions are crucial for a Ttest’s accuracy.) This test was completed eight times: once for each CCAT value. The results are displayed in figure 5. It is clear that when a mis-configured node enters the network, there are varying ramifications, depending on the current CCAT value. The only values for which there is a statistical difference with a mis-configured node are CCAT values of -10 and -5 dBm. In both of these cases, the misconfigured node changes the throughput by a significant amount.

IV. DISCUSSIONS This section discusses the results of this experiment.

CCAT

Fig. 2. The average byte throughput for each given CCAT value.

as the threshold is raised. From figure 3, one can clearly see a trend in the negative direction. The only exceptions to this observed pattern are values -10 and -5 dBm. This was most likely due to the threshold’s position above the output power level.

B. Mis-Configured Node The results from both the effects of CCAT value variations and a solitary malfunctioning node suggest that under very specific conditions, a non-conforming node’s presence will have repercussions on the mesh network. To be sure, however, it is necessary to invoke certain statistical measures. More specifically, a

Byte Throughput

3StatisticallyDifferent?

0123 4567

22070 22960 21914 20768 23126 23336 22468 22670 20822 19380 22536 23002 23222 23458 23650 17534 18880 19250 22670 22638 21970 18804 18710 18390

N/A N/A N/A No No No No No No No No No No No No Yes Yes Yes No No No Yes Yes Yes

the muddled conversations. This is representative of a packet collision, in which the receiving

Fig.3. Left: A graph of CCAT values versus their corresponding byte throughputs.

Fig.5. The statisticaleffectsof mis-configuring a node.

A. CCAT Values The inverse relationship between the CCAT value and byte throughput for about the first half of the data suggests that as the threshold is raised, there is a corresponding decrease in the number of packets that successfully reach the sink. The reason for this is fairly straightforward to understand. As the threshold is raised (or moved closer and closer to zero), the sensitivity toward communications on the channel becomes less and less. When this occurs, a mote querying the channel is more apt to ignore any current transmissions. In other words, a CCAT value of -70 will pick up more transmissions than will one at -50. When a node has a high enough CCAT that incoming RSSIs fall below the threshold, the transmissions are ignored, and the node incorrectly assumes that the channel is open. If it transmits at the same time as another nearby node, a collision occurs and both packets are discarded, resulting in a loss in overall transmissions. The scenario is akin to a group of people who decide to enter a conversation with their ears covered and vision shielded. These people are ignorant of any communication that is taking place and begin talking at random times. When two or more of these individuals talk at the same time, an onlooker will be confused at

Fig.4. Right: A graph of CCAT values versus their corresponding byte throughputs with the mis-configured node.

node can make no sense out of two transmissions that happen concurrently. The negative exponential trend for the increasing CCAT value suggests that as the threshold is raised, less traffic is transmitted over the channel. This can be attributed to the channel’s interpretation of neighboring signals. Since our nodes’ transmission power was set to -25 dBm, any threshold (CCAT) above that value will classify all legitimate signals as not being genuine traffic. Because there are no signals above this threshold, it is essentially ”guessing” when to use the channel. That is to say, transmissions from this node will take place despite the state of the channel. For this

reason, our CCAT values of -20 to -1 dBm may be said to ignore all legitimate traffic completely. The net result is that the motes always assume the channel is open and transmit regardless of its state. Since the motes in this experiment were timed for transmission every five seconds, the manner in which their initiation was staggered, and also the other motes that could hear the transmissions, were the two major factors in deciding the volume of traffic that made it successfully to the sink. The rest of the packets were presumably lost to collisions. This is observed as erratic results with no apparent trend, which explains why the graph in figure 3 goes from a fairly tight pattern in values -70 to -30 dBm to seemly random behavior in values -20 to -1 dBm. The startup timing on the nodes was what caused the variation in these four cases. It is important to note that this phenomenon is unique to the particular topology and transmission power implemented in this study. If either of these were to change, there would most likely be a similar shift in observed throughputs.

was no considerable difference between the control group and the other three test groups. In essence, as long as the CCAT for the mis-configured node was under the transmission power of -25 dBm, there were no repercussions experienced as a result of the misbehaving node’s presence. This is confirmed by the first three T-tests performed, which are shown in figure 5. The logical explanation for this is that even though the single mote had a CCAT that was more ”deaf” to traffic on the channel than the other motes, it could still overhear transmissions taking place and used exponential backoff, which set it to a different time cycle, thereby causing it to interfere less. On the contrary, raising the CCAT past -25 dBm caused result deviations. Since only one node was configured to misbehave, the fluctuations were slightly less than those of Phase I. Nonetheless, the node’s position as a forwarder in a high-traffic area was enough to cause noticeable variations. In fact, the only two values that digress a statistically significant distance from the control group are -10 and -1 dBm, both of which are Even though CCAT values lower than -70 were not within this ”belt of uncertainty” (-25 to0 dBm). tested, it is reasonable to assume that there would It is worth reiterating that these observations are have been a similar decline in performance. Whereas limited to the topology used in this study. Had less the values above -70 dBm may be said to not be traffic been routed through the problem node, it would selective enough, those far below -70 dBm would have likely had a more negligible influence on the become too selective. If dropped to an unreasonably throughput. low level, the motes would begin detecting lower and lower RSSI values and classifying them as traffic. When the transmissions are within a range where this could cause collisions, this behavior is desired. The problem arises when other distant motes are detected when they pose no potential problem to successful transmission. At extremely low thresholds, the motes would even detect external activity like microwaves and so on. If these extremely weak RSSI values cause a node to wait unnecessarily, less traffic is passed along. The paragon of a wireless sensor network would strike a balance between the extremes of ”not selective enough” and ”overly-selective” in examining signal strengths. Consequently, there really is no ”best” universal CCAT. The fact of the matter is that the optimal CCAT settings are intimately tied to factors like the network’s physical layout and power level.

V. FUTURE W ORK The purpose of this experiment was not to exhaust all possible circumstances under which the motes were tested. Instead, the idea was to provide a paradigm for future studies on similar cases. Consequently, it must be understood that the results from these tests are in fact unique to the topology used.

While the process and/or results of this project may be recycled, future projects will likely focus on certain aspects that were not covered in this study. Namely, only one individual node was used in the simulated misconfiguration. This was primarily due to the small number of motes available. Perhaps further research will focus on varying the number of mis-configured motes instead of (or in addition to) the CCAT values. A larger number of these motes would likely amplify the B. Mis-Configured Node observations taken from this experiment. Depending on the specific values used for the mis-configuration and In line with Phase I, the effects of a mis-configured the ratio of incorrectly programmed motes to those that node varied based upon which CCAT value was being are set up correctly, the observed results would likely tested. Like the first phase, the first four CCAT values change. exhibited a fairly constant pattern. That is to say, there As was seen with the single anomalous node, the

traffic remains the same as the CCAT value is raised toward zero, so long as it remains below the transmission power level. Another interesting change would be to drop the misconfigured node’s CCAT value below -70 to determine for certain how a more selective threshold would affect the throughput. Along the same lines, it would be worth examining what would happen if multiple problem nodes were used. Furthermore in this study, a fixed topology was used. In order to know the full extent to which the physical layout affected the results, it would be necessary to examine this variable as well. The answer would be particularly crucial to underwater environments where the motes are allowed to move freely. Such deployments introduce dynamictopologies, which should be considered in further studies.

VI. CONCLUSION

The subtopic of wireless sensor networks is rather encompassing. This experiment aimed at illuminating the concepts of clear channel assessment and misconfigurations in the realm of wireless networking efficiency. With the rising ubiquity of wireless sensor networks has come the need to fine tune transmissions. As was outlined in this paper, clear channel assessment thresholds directly determine how well data will flow through a given topology. By listening to how strong a neighbor’s signal is, an individual node can determine whether or not it is an appropriate time to transmit. For this reason, the results will vary on a topological basis; however, it is clear that CCAT values within a given wireless sensor mesh network do play a role in determining transmissions in the overall wireless environment. On the other hand, the only case in which a mis-configured node (or one with a stray CCAT value) will change the network throughput is when it is raised above the motes’ configured transmission power. Further investigation into this phenomenon may usher in new successes in the realm of wireless sensor networks.

VII. ACKNOWLEDGEMENTS

This work was done as part of the Research Experience for Undergraduates in Multidisciplinary Computing project at the University of South Carolina and supported in part by the National Science

Foundation (award #0649105). Special thanks to Dr. John Bowles and Dr. Caroline Eastman, the on-site directors of this program.

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