Using Received Signal Strength Variation for Surveillance In Residential Areas

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1 Using Received Signal Strength Variation for Surveillance In Residential Areas Sajid Hussain, Richard Peters, and Daniel L. Silver Jodrey School of Computer Science, Acadia University, Wolfville, Canada. ABSTRACT There are various uses of wireless sensor technology, ranging from medical, to environmental, to military. One possible usage is home security. A wireless sensor network could be used to detect the presence of an intruder. We have investigated the use of Received Signal Strength Indicator () values to determine the mobility of an intruder and have found that accurate intruder detection is possible for at least short distances (up to 20 feet). The results of interference monitoring show that a wireless sensor network could be a feasible alternative for security and surveillance of homes. Keywords: Wireless Sensor Networks, Security, Intruder,, LQI. 1. INTRODUCTION Wireless Sensor Networks (WSNs) have a diverse range of applications. This paper deals with the usage of wireless sensor networks for home security and surveillance. The objective is to determine if the interference between two sensor nodes caused by an intruder; the variation of received signal strength indicator (), is sufficient to detect the intruder. We use a light sensor to validate an intrusion event. The light from one sensor node is interrupted at the other node due to the intruder blocking the beam. Although this validation method is only feasible in a dark environment and requires an additional light source, it allows us to collect accurate intrusion data for testing our -based approach. The MicaZ processor boards and MTS 400CA sensor boards of Crossbow Technologies 1 are used in the experiments. 2. RELATED WORK Detection of physical objects is not a new field in wireless sensor networks. There have been several papers published that deal with the detection of human beings and of vehicles. Chen et al. (2006) provide experimental details to detect human movement within a field. The experiment relied on passive infrared motion detectors to detect intruders. Cheung et al. (2005) and He et al. (2006) detail experiments using magnetic sensors to detect vehicles and multiple properties of these vehicles, using magnetic fields. Although He et al. (2006) focus more on military usage; Cheung et al. (2005) provide emphasis on commercial usage. Ploetner and Trivedi (2006) combine wireless sensor network technology with other technologies such as video cameras and microphones to detect both human and vehicle traffic. For wireless sensor networks, both magnetic and passive infrared motion detectors are used. Schlessman et al. (2006) seek to detect human presence. The paper proposes using a camera sensor, combined with a passive infrared motion sensor or an ultrasonic distance sensor. All of the above related works used the sensors provided on the sensor nodes, and in some cases, combine these results with additional technology. On the other hand, the proposed method of using variation in values removes all but the bare minimum required equipment. This will reduce cost of equipment and the energy cost on the nodes themselves. Chen et al. (2005) use wireless LAN environment to develop RF-based surveillance system. Although Wireless LANs can be used for surveillance, they cannot be densely deployed because of cost. Further, the energy consumption of nodes of wireless LAN is significantly higher than the energy consumption of sensor nodes; hence, the WSN-based surveillance system would be more scalable, accurate, and easy to maintain. 1

2 3. PRELIMINARY TESTING Objective: Investigate the possibility of using the variation in Received Signal Strength Indicator () and Link Quality Indicator (LQI) values for intrusion detection. Method: For this test, two sensor nodes were placed 4 feet apart, with a beam of light going across both. The power level of both nodes was set to -15 dbm. A test subject then walked between the nodes, causing interference on both the beam of light and the signal itself. The light sensor is used to validate the results based on LQI and variation. The sensor nodes used for the experiment are programmed to detect light, and then send that information in a packet. The opposite node, upon receiving this packet, adds the and LQI values. The packet is then sent to the base station, to be logged. The experiment is conducted for 30 minutes and the sampling frequency is 1 sample/second. Results: A brief section of the results (one minute of collected data) are shown below, with the LQI results in Figure 1, and results in Figure 2. The horizontal axis shows the time scale and vertical axis provides the LQI or and light levels Node 1 LQI Node 2 LQI 0:00:05 0:00:10 0:00:15 0:00:20 0:00:25 0:00:30 0:00:35 0:00:40 0:00:45 0:00:50 0:00:55 0:01:00 Time Fig. 1. LQI variation is negligible and cannot be used to determine the intrusion Node 1 Node :00:05 0:00:10 0:00:15 0:00:20 0:00:25 0:00:30 0:00:35 0:00:40 0:00:45 0:00:50 0:00:55 0:01:00 Time Fig. 2. variation is significant and can be used for intrusion detection. A simple linear regression model was developed using LQI and as the independent variables and LIGHT as the dependent response variable marking the presence of an intruder. The model has correlation, r 2, of that is due to the dependent variable (with significance p = 2.63E-79). The second independent variable LQI is a poor predictor

3 of an intruder s presence (p = ). These results agree with the graphs of Figure 2 showing a match in the reduction in and light levels as a person moves back and forth between the sensor nodes. Our conclusion is that cam be used on its own to determine the presence of an intruder. 4. THEORY The next step is to devise an algorithm for detecting abnormal deviation in levels that would strongly correlate with abnormal deviations in LIGHT levels as an indicator of presence of an intruder. A simple approach is to calculate the required deviation in the that indicates sufficient abnormality. We assumed that ambient noise in the could be modeled by a normal curve. We then examined the reduction in when an intruder was present and found that values one standard deviation below the mean (based on a window of several seconds) acted as a good indicator of an intruder. This can be compueted as follows: ALGORITHM // window size determines the number of samples considered // in mean and standard deviation calculations var WINDOW_SIZE; // total number of readings var TOTAL_ENTRIES; // keep track of total number of intruders intruder_count=0; // index for current sample t=0; // skip t entries in order to get a window of data t = t + WINDOW_SIZE; DO // compute mean over previous WINDOW_SIZE entries // i.e. mean is computed over samples: (t-window_size)... t Compute µ; // similarly, compute standard deviation over previous WINDOW_SIZE entries Compute σ; IF t < µ σ intruder_detected = TRUE; intruder_count++; ELSE intruder_detected = FALSE; t = t + 1; WHILE t < TOTAL_ENTRIES The above algorithm shows the computation details to identify an intruder s presence based on values. The mean and standard deviation are computed over a given number of samples, called WINDOW_SIZE. If the value is less than the deviation µ σ, an intruder is detected and the counter is incremented; otherwise, there is no intruder. A suitable window size can be determined based on some preliminary investigation of the deployment environment. Table 1 shows the results of applying the above algorithm for data obtained in the preliminary testing of Section 3. As the window size increases, the number of false alarms is reduced until a stable point is reached. As an example, based on the results shown below, a window size of 60 seconds would be used for the estimation of mean and standard deviation.

4 Window Size False alarms Missed Intruders Table 1. Window Size Comparisons (220 intruders) 5. EXPERIMENT Objective: Investigate the effect of using our algorithm of Section 4 for detecting and intruder as the transmission distance between two sensor nodes is increased. Method: The sensors are deployed in a standard living room with typical furniture such as sofa, couch, dining table, and exercise machine. In order to avoid absorption of signals because of ground, the sensors are placed at the height of 3 feet, using stools. Two sensor nodes were used, both set to a power level of -15dBm. The nodes were tested at distances of 5, 10, 15, and 20 feet. The nodes were programmed as specified in Section 3, and the various distance tests were done for 20 minutes each. During the experiment, only one person was allowed in the area. The power levels for the nodes were maintained at a constant level by using fresh batteries for each run. Results: Graphs of the and LIGHT levels for a 5 minute interval of each tested distance are shown in Figures 3 to 6. The statistics from regression models for the runs are shown in Table 2. The level of correlation and significance of the with respect to LIGHT remains high (r 2 > 0.35 and p-value < ). We conclude that, with a power level of -15dBm, up to a distance of 10 feet there is consistently a good correlation between the and LIGHT levels and therefore presence of an intruder. Beyond 10 feet the relationship degrades slightly; however, even at 20 feet the correlation remains acceptable. The loss in accuracy due to distance is caused by at least two factors. First, as distance increases, the will decrease and this can lead to lost packets. At 5 feet, approximately packets were lost; whereas 520 packets were lost at distance 20 feet. The packet loss occurs more frequently when an intruder is present because the interference they cause can prevent packets from reaching to the opposite node, or to the base station. Second, as levels fall due to distance, the impact of the intruder on the signal falls below the one standard deviation limit. One can see the difference in the spikes caused by an intruder at a distance of 5 feet (Figure 3) as compared to that of an intruder at a distance of 20 feet (Figure 6). Distance (feet) Observations Multiple R R Square Standard Error Significance F 1.4E E E E-137 Number of Intruders False Alarm Rate Missed Intruders Rate Table 2. Regression Statistics: Y = light level, X = value.

5 :00:20 0:00:42 0:01:01 0:01:21 0:01:43 0:02:04 0:02:25 0:02:45 0:03:06 0:03:27 0:03:47 0:04:07 0:04:27 0:04:49 Fig. 3. Sample of and readings at 5 feet :00:22 0:00:41 0:01:01 0:01:22 0:01:42 0:02:02 0:02:22 0:02:42 0:03:04 0:03:25 0:03:48 0:04:08 0:04:30 0:04:52 Fig. 4. Sample of and readings at 10 feet :00:13 0:00:34 0:00:54 0:01:15 0:01:36 0:01:56 0:02:18 0:02:40 0:03:08 0:03:27 0:03:51 0:04:14 0:04:37 0:04:57 Fig. 5. Sample of and readings at 15 feet :00:23 0:00:51 0:01:13 0:01:37 0:01:59 0:02:25 0:02:47 0:03:15 0:03:42 0:04:09 0:04:36 Fig. 6. Sample of and readings at 20 feet.

6 6. CONCLUSION The results have shown that while the LQI of the sensor nodes has no significant relationship with the presence of an intruder, the does. This is because of the radio interference that is cause by the intruder s body. We have shown that this variation is significant enough to act as a detector for sensors placed up to 20 feet apart. Thus, intruder detection via could well become a cheap, quickly deployed security system. These findings could be the basis for many future endeavors. Such a usage of wireless sensor networks could extend beyond home security into military usage; such as setting up a perimeter to detect incoming enemies. On the industrial side, using a more complex setup and a little logic, it would be feasible to keep track of human traffic within a building or to identify the relative number of individuals in each room. For security, one could use paired keys between neighboring nodes to hinder the ability of intruders to hide themselves by injecting their own packets. Another possible enhancement is using relay nodes to carry the result packets back to a distant base station, or having redundant nodes that can be activated upon sensor node death to enhance the lifetime of the network. In future work, we plan to further study the relationship between an intruder s position and the effect on. More specifically, we will investigate the use of three or more sensors for intruder localization using triangulation strategies and possibly path traversal through a mesh network of sensors. This will require the use of more advanced modeling techniques from statistics and machine learning. REFERENCES [1] [2] [3] [4] [5] [6] P. Chen, S. Oh, M. Manzo, B. Sinopoli, C. Sharp, K. Whitehouse, G. Tolle, J. Jeong, P. Dutta, J. Hui, S. Shaert, S. Kim, J. Taneja, B. Zhu, T. Roosta, M. Howard, D. Culler, and S. Sastry Booth, N. and Smith, A. S., Experiments in instrumenting wireless sensor networks for real-time surveillance, IEEE International Conference on Robotics and Automation (ICRA), pp (2006). S. Y. Cheung, S. C. Ergen, and P. Varaiya, Traffic surveillance with wireless magnetic sensors," Proceedings of the 12 th World Congress on Intelligent Transport Systems (ITS), pages 13, (2005). T. He, S. Krishnamurthy, L. Luo, T. Yan, L. Gu, R. Stoleru, G. Zhou, Q. Cao, P. Vicaire, J. A. Stankovic, T. F. Abdelzaher, J. Hui, and B. Krogh, Vigilnet: An integrated sensor network system for energy-efficient surveillance, ACM Transactions on Sensor Networks (TOSN), v.2 n.1, p.1-38 (2006). J. Ploetner and M. M. Trivedi, A multimodal approach for dynamic event capture of vehicles and pedestrians, Proceedings of the 4th ACM international workshop on Video surveillance and sensor networks, pp , (2006). J. Schlessman, J. Shim, I. Kim, Y. C. Baek, and W. Wolf, Low power, low cost, wireless camera sensor nodes for human detection, Proceedings of the 4th ACM international workshop on Video surveillance and sensor networks, pp (2006). J. Chen, Z. Safar, J. A. Sørensen, and K. J. Kristoffersen, An RF-based surveillance system using commercial offthe-shelf wireless LAN components, Proceedings of the 13 th European Signal Processing Conference, pages 4 (2005).

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