Allocating Parking Spots with WSN Instructed by: PhD. Zvi Lotker Moshe Cohen Anton Nosikovsky Instructed by: PhD. Zvi Lotker

PROJECT MOTIVATION Finding a parking spot is a difficult task in metropolitan areas, using WSN system to allocate parking spots in big cities can help: Reduce 28%-45% of traffic jams in metropolitan cities. Save time. Gas money. Reduce greenhouse gases produced by circling cars. Get statistics about parking habits.

PROJECT PHASES Our project was made of two phases, Research phase and implementation phase: Research phase: Researching on different algorithms used by people to seek a parking spot. Greedy Algorithm Destination First Algorithm (DFA) Buffer Algorithm Implementation phase: A small Wireless Sensor Network with an interface that can find closest available parking spot.

RESEARCH PHASE Using Omnet++ simulator, we implemented a circle of parking lots, with cars circling and seeking a parking spot by a chosen algorithm. Our results focused on minimizing time to reach destination. Arrival~Poisson(λ). Dest~Uniform(1,N). Parking~Exponential(μ). Total time=Driving+Walking.

GREEDY ALGORITHM Car enters at P1 and chooses its destination. Car parks at first available parking spot. Person walks to destination. G 1 2 3 4 5 6 9 8 7 6

DESTINATION FIRST Car enters at P1 and chooses its destination. Car drives to destination. At destination the car starts act greedy. Person walks to destination. G 1 2 3 4 5 8 9 8 7 6

BUFFER ALGORITHM Car enters at P1 and chooses its destination. β=3 Car enters at P1 and chooses its destination. Person looks ahead β parking spots and acts like DFA, if the β parking spots are occupied it turns Greedy. β=1 => greedy. β=N => pure DFA. G 1 2 3 4 5 9 9 8 7 6

ALGORITHM COMPARISON If μ/λ is lower than 1/N then, entering cars rate is higher than exiting cars from the circle, hence graphs reach INF. Greedy performs better than DFA in high density scenarios. DFA performs better than Greedy in low density scenarios. Buffer algorithm can accumulate to the scenario by adjusting β, lower is for high density, higher for low density. We ran each algorithm with 20 different seeds (until reaching a smooth line). μ/λ

WHY WSN? No need for new infrastructure when deployed in large outdoor area (ie. big city). Low energy consumption – with the ability to get energy from a solar source. Sensors have high endurance to weather and physical pressure. When using a wired network we need new infrastructure, connecting each sensor to the power company.

IMPLEMENTATION PHASE Using Sentilla’s T-mote with PIR sensor, we implemented a small scale Wireless Sensor Network which controls parking spots. PIR is a motion detection sensor (like in every house alarm). PIR sensor on WiEye expansion has 100o sensing angle. We reduced the IR sensing angle to about 10%. In our experiments the sensor is placed in a special box above the parking spot.

CAR DETECTION The plot shows typical behavior of car parking, and human interference. Detection of car is enabled through filtering out the interferences. The match filter was assembled by matching a pattern with 2×Π[0.4×t-τ]. The height is 2 Volts, and width is at least 2.5 seconds.

ENERGY HIERARCHY RF Tx Sensing Sleep

ENERGY vs. EFFICIENCY Every 500 msec IR sensing send msg through RF Check threshold Return to normal send msg through RF Every 50 msec IR sensing Status changed

FUTURE WORK Larger scale of sensor network deployment: Multi-Hop connectivity between gateways. Connecting the motes to a solar source of power. Add the ability to auction on a parking spot. Use additional sensor to get high accuracy on the parking spot.

DEMO MOVIE