Development of Micro Wireless Sensor Platforms for Collecting Data of Passenger-Freight Interactions Mohammad Mozumdar Graduate Students: Kyle Ying, Alireza Ameri, Ankit Trivedi, Dilip Ravindra, Darshan Patel, Varun Sharma, Ashutosh Parhad Department of Electrical Engineering California State University Long Beach The thing we don’t like but have to face everyday – Road Traffic! But we can make smarter turn with real time traffic! Google Maps showing optimized route from USC to Orange County How exactly does Google Maps/Garmin/TomTom know how clogged the highway is on your way out to home or office? The traffic information comes from a variety of sources: • Commercial traffic data providers (INRIX, Tele Atlas, HERE, ..) • Departments of Transportation • State agency – Caltrans Raw data is collected from: • Mobile users (Google Maps) • Road sensors • Traffic cameras, and even through aircraft This information is compiled and delivered via radio frequency (FM/HD Radio™ or satellite) to your navigation system. Road Sensors: Inductive Loop Source: US DoT Federal Highway Administration Physical Representation • • • • Loop Detector Schematic Existing traffic/vehicle detection is determined with “Inductive Loop” technologies These loops generate a magnetic field that operates at frequencies typically less than 1kHz Large rectangular loops (4’ x 8’, 6’ x 8’, 6’x 12’ are used to detect larger vehicles Small size loops (i.e. 2’ x 5’, 3’ x 6’, 6’ x6’) are used to detect smaller vehicles, such as motorcycles and automobiles Road Sensors: Inductive Loop Source: US DoT Federal Highway Administration Inductive Loop Pros & Cons Advantages • Detects ferrous objects precisely • Typically immune from environmental effects such as weather, temperature, a terrain variations Disadvantages • Expensive to install and maintain ($$$) • Relatively significant power usage for the generation of the magnetic field. • Large area usage (greater than 10 sq.ft.) Proposed Solution For Smart Road Proposed VS Traditional Inductive Loop Based System Anisotropic Mangeto-Resistive Sensors (AMR) IC Sensor AMR Sensor IC (Honeywell HMC5883L 3-axis magnetometer- 3mm in size ) • Wheatstone bridge variable resistor network that changes resistance w.r.t. changes to the magnetic field • Provides the same advantages to inductive loop technologies without the power and area disadvantages • Power consumption extremely low (~200uA at lower sampling rates ) Source: Honeywell Microcontroller (CC430F5137 ) • Low power modes (LPM) for sleep between computational and communication operations • Single package μproc and RF core for low area wireless transmissions Source: Texas Instruments Machine Learning Based Vehicle Classification • Useful when the sets of data is large enough that human observations for extracting patterns in data become impractical. • Typically associated with the field of data mining • Pattern recognition based on a set of rules General Idea: • Collect vehicle data crossing the AMR sensor • Utilize ML tools to generate a model for classification Our Lab Testbed Setup • 7 different RC Vehicles with a variety of similar and different attributes • 7 ft straight track for each vehicle to make passes • 2 sensors roughly 4’ apart to take gather readings and classify Data Collection for supervised machine learning • We collected data for each of the 7 vehicles across 350 runs over 2 sensors • Total : 700 samples, 100/class for training Why a decision-tree based algorithm? • Simple and computationally efficient tree • Simplicity of implementation in software Implementation Flowchart Normalize Raw Data Vehicle Threshold Detection Record Data in Detection Window Output Vehicle Classification Classify Vehicle using Decision Tree Model Feature Extraction at Window End Adaptive Baseline 2000 Magnetic Field Change (uT) 0 -1000 -2000 -3000 -4000 -5000 0 5 10 15 20 25 30 35 40 45 50 35 40 45 50 Time (seconds) 1500 1000 500 Magnetic Field Change (uT) • Zeroing the background environmental magnetic field by offset • Allows for the reuse of the same vehicle detection and classification algorithm in multiple environments • Noise removal can be implemented at this stage 1000 0 -500 -1000 -1500 -2000 -2500 -3000 -3500 0 5 10 15 20 25 30 Time (seconds) Threshold Detection • Once a vehicle passes the threshold the detection flag triggers and a certain number of samples are recorded for processing Magnetic Field Change (uT) Magnitude with vehicle overhead Time (seconds) Detection Window Threshold Features Collected from Vehicles Interesting Features: • Min: minimum value of an axis during the detection window • Max: maximum value of an axis in the window • Mean: average of all axis values in the window • Range: Maximum – Minimum Using a 3-axis sensor this results in 12 unique features These Features are very simple to calculate and compute Example Plot and Feature Extraction Feature Extraction 1500 DataX DataY DataZ MinX MaxX MinY MaxY MinZ MaxZ 1000 Magnetic Field Change (uT) Features Data (X,Y,Z) 500 MinX=-1097 MinY=-256 MinZ=-834 MaxX=1054 MaxY=267 MaxZ=1011 MeanX=4.01 MeanY=17.13 MeanZ=7.48 RangeX=2151 RangeY=523 RangeZ=1845 0 -500 -1000 -1500 0 10 20 30 40 50 60 Time (seconds) 70 80 90 100 Example Car Data Big Racer Car Data Buggy Car Data 1000 200 X data Y data Z data 800 150 100 Magnetic Distortion (uT) Magnetic Distortion (uT) 600 400 200 0 -200 50 0 -50 -100 -400 -150 -600 -800 X data Y data Z data 0 10 20 30 40 50 60 70 Samples (1/75 seconds) 80 90 100 -200 0 10 20 30 40 50 60 70 Samples (1/75 seconds) 80 Similar Sizes but Different Signatures! 90 100 Machine Learning: decision tree learning (J48) J48 is the open source Java implementation of C4.5/ID3 developed by John Quinlan Inputs: multiple features corresponding to a single classifier Note: higher # samples per classifier results in a more accurate output tree Output: a decision tree with the highest classification rate given the features Source: University of Waikato, WEKA WEKA output 4 Features Selected Fast! Accuracy Rate Graphical Tree Yellow BigGreen Red Green Diecast Buggy Red BigGreen BigGreen Big Car Buggy Node Num. Min Y 1 2 3 4 5 6 7 8 9 10 0.9527 0.3339 0.3060 0 0.7108 0.9317 0.2047 0.1386 0.5724 0 Information Gain Attribute Max X Mean Z 0.8002 0.9707 0.8411 0.1735 0 0.1864 0.1601 0.4467 0.3189 0 0.9031 0.3978 0.2155 0 0.7108 0.7574 0.3717 0.8798 0.9625 0.0066 Range Z 0.9868 0.3846 0.3068 0.0379 0.0570 0.4976 0.4249 0.0084 0.8676 0.6627 Best Attribute Range Z Max X Max X Max X Min Y Min Y Range Z Mean Z Mean Z Range Z Brute Force Search for Best Results • The output tree doesn’t always generate the best results given a large number of features • Due to fast processing time to generate the output tree, we can easily calculate all combinations (n choose k) 𝑛 ( ) or 𝑛 𝐶𝑘 𝑘 • We use n=2,3,4 where k=12 Feature Performance 2 features (66 combinations): 3 features (220 combinations): 4 features (495 combinations): . Comb# Classification% Features 64 94% minx maxx 58 93% minx rangex 30 93% maxx rangey 60 91% minx meany Comb# Classification% 219 98% minx miny maxx 200 98% minx maxx maxz 194 97.8571% minx maxx rangez 57 97.8571% maxx rangey rangez 149 97.7143% miny maxx rangez Comb# Classification% 479 98.8571% minx miny maxx rangez 270 98.8571% miny maxx meanz rangez 390 98.7143% minx maxx meanz rangez 78 98.7143% maxx meany meanz rangez Features Features Best Results Simulated vs. Testbed Simulated Results Actual Results: Cross-Validation Percentages Number of Features (Attributes) Real world Classification Percentages Accuracy Number of Features (Attributes) Real-world Three Features (maxx, rangey, rangez) 97.86% Three Features (maxx, rangey, rangez) 98.57% Three Features (miny, maxx, rangez) 97.71% Three Features (miny, maxx, rangez) 97.38% Four Features (minx, miny, maxx, rangez) 98.86% Four Features (minx, miny, maxx, rangez) 90.24% Four Features (miny, maxx, meanz, rangez) 98.86% Four Features (miny, maxx, meanz, rangez) 99.05% Simulated results match real world testing values very closely. Note: minx results are lower due to clipping Energy Scavenging Using Piezoelectric Sensors • Mechanical to Electrical energy conversion • Proper implementation can help in continuous operation of wireless sensors • Almost 70% of the overall efficiency of the energy scavenging system depends on Piezoelectric sensors • Applications include consumer electronics, automotive, health, WSN, etc. Pressure generated by tires of cars on the piezoelectric sensors Generated power is stored in batteries Energy Scavenging System Piezoelectric Sensors Mechanical Energy to Electrical Energy Switch Rechargeable Battery Energy Source ADC Voltage Regulation Rectification Circuit Charge Regulation Circuit Power Storage Element Lab Prototype of Energy Scavenging System Advanced Energy Scavenging System • 3 layers instead of 1 layer/ Smaller size and less implementation cost • Increase probability of the sensors being pressed in every tap • Increase number of sensors being pressed in a single tap Energy Scavenging System 1 AA rechargeable battery can be charged in 10 -12 hours with vehicles and pedestrians passing over the sensors in every 5 seconds using the designed hardware The sensors placed on crosswalks can increase the average number of taps Charging rate would be better if the efficiency and the number of sensors used are increased Designed Smart Traffic Sensing Node • Size: 2 ½” x 1 ½” x ¾” (with AA battery pack) • Dimensions will change depending on the battery pack used in future implementations Final Remarks The system described in this presentation can replace current inductive loop technologies with: – Maintain traffic/vehicle detection capabilities – Additional features such as vehicle classification – Lower power consumption – Lower physical area utilization In addition, many classifiers can be used at high accuracy rates compared to other methods utilizing solely novel features. Related Publications • K. Ying, A. Ameri, A. Trivedi, D. Ravindra, D. Patel, M. Mozumdar,” Decision Treebased Machine Learning Algorithm for In-node Vehicle Classification”, Proceedings of IEEE Green Energy and Systems Conference, Long Beach, November 2015, USA • V. Sharma, A. Parhad, M. Mozumdar, “Energy Scavenging Using Piezoelectric Sensors to Power in Pavement Intelligence Vehicle Detection Systems”, METRANS International Urban Freight Conference , Long Beach, 2015 Questions?