CRAWDAD umich/rss

This is a dataset of RSS measurements collected by Mica2 sensor nodes deployed inside and outside a lab room, with anomaly patterns occurring when students walked into and out of the lab. A web camera recorded the activity that could be matched with detected anomalies.   date/time of measurement start: 2006-04-21date/time of measurement end: 2006-04-21collection environment: The experiment was set up on a Mica2 platform, which consisted of 14 sensor nodes randomly deployed inside and outside a lab room. During the measuring period, students walked into and out of lab at random times, which caused anomaly patterns in the RSSI measurements. Finally, a web camera was employed to record activity for ground truth.network configuration: Wireless sensors communicate with each other by broadcasting and the received signal strength (RSS), defined as the voltage measured by a receiver’s received signal strength indicator circuit (RSSI), was recorded for each pair of transmitting and receiving nodes.data collection methodology: The experiment was conducted by Prof. Neal Patwari of the University of Utah in the Winter of 2006, when he was a post-doctoral student at the University of Michigan under the guidance of Prof. Alfred O. Hero III. The site of the experiment was the 4th floor of the EECS building, University of Michigan, Ann Arbor. There were 14 × 13 = 182 pairs of RSSI measurements over a 30 minute period, and each sample was acquired every 0.5 sec.Tracesetumich/rss/sensorTraceset of RSS measurements of a Mica2 sensor network deployed at the University of Michigan in 2006.file: rssdata.zip, rssdata.tar.gzdescription: This is a traceset of RSS measurements collected by Mica2 sensor nodes deployed  inside and outside a lab room, with anomaly patterns occurring when students walked into and out of the lab. A web camera recorded the activity that could be matched with detected anomalies. The mission of this experiment was to use the RSS sequences to detect any intruders (anomalies). The ground truth indicator (camera recordings) can be used for evaluating the detection performance.methodology: Wireless sensors communicated by broadcasting and recorded RSS for each pair of transmitting and receiving nodes every 0.5 sec. A web camera recorded activity  for ground truth.limitation: The original raw data is not synchronous. This is corrected for by using interpolations. Data was also pre-processed to remove temperature drifts. umich/rss/sensor Traceanomaly: Trace of RSS measurements of a Mica2 sensor network deployed at the University of Michigan in 2006. This is a trace of RSS measurements collected by sensor nodes, with anomalies detected when students entered or left the room. The mission of this experiment was to use the RSS sequences to detect any intruders (anomalies). The ground truth indicator can be used for evaluating the detection performance.configuration: This experiment was set up on a Mica2 platform, which consisted of 14 sensor nodes randomly deployed inside and outside a lab room. Sensors communicated by broadcasting every 0.5 sec.format: rssdata.zip contains the original Matlab files and the preprocessing code preprocess.m .rssdata.tar.gz contains the same data converted to css and the preprocessing code preprocess.m.The original raw data is stored in the matrix ‘dataLinear’ (of size 182 x 3191) in the file ‘dataLinear.mat’. The ground truth is recorded in the vector ‘motionCode’ (of size 1 x 3191) in the file ‘motionCode.mat’. A value of 1 in motionCode indicates the presence of an intruder.Pre-processingThis original raw data is not synchronous. This is corrected for by using interpolations to give the modified data in the matrix ‘Y’ (of size 182 x 3127) in the file ‘Y.mat’. The corresponding ground truth is stored in the vector ‘motion’ (of size 1 x 3127) in the file ‘motion.mat’.To remove the temperature drifts of receivers we pre-process the data by removing their local mean values. Let y_i[n] be the n-th sample of the i-th signal and denotey[n] = (y_1[n], …, y_{182}[n])'.Due to temperature drifts, certain trends exist in y[n]. We de-trend the data byz[n] = y[n] – y_m[n]yielding z[n] for anomaly detection, wherey_m[n] = (2m+1)^{-1} \sum_{i=n-m}^{n+m} y[n]is the local mean value. We set m=50 in this experiment. The modified data ‘Z’ is stored in the matrix ‘Z’ (of size 182 x 3127) in the file ‘Z.mat’. This pre-processing was done in Matlab and the code used for doing this pre-processing is given in ‘preprocess.m’.