Team-16Madan Jhawar(201202018)Darpan Baheti(201202036)

Shrenik Jain(201403006)Arsh Anand(201405531)

CLOUD COMPUTINGMAJOR PROJECT

A REAL TIME EVENT MONITORING SYSTEM USING APACHE SPARK

STREAMING

INTRODUCTION

● Event Monitoring is the process of collecting, analyzing and signalling event

occurrences which may stem from arbitrary sources in both hardware or

software.

● Event Monitoring is used to analyse the change in the behaviour of a

particular object.

● The Object which is monitored is called the Monitored Object.

● A Monitored object must be properly conditioned with event sensors to enable

event monitoring.

ABOUT THE PROJECT

● Our aim is to build a Real Time Vehicle Monitoring System.

● Incidents of Road Rage have increased considerably over the last decade

resulting in many casualties.

● Most of these casualties can be attributed to the long response time required

to reach the place of accident.

● This is due to the fact that the process of determining the location of the

accident and communication with the concerned authorities is quite lengthy.

● The delay in response time can increase the severity of the accident.

OBJECTIVE

● Reduce the time required to report an accident and to determine its

location more precisely.

● Monitor Real-Time GPS Data to identify possible accident locations.

● Monitor sensor data to keep a check on speed limits.

● Notifying the concerned authorities in case of anomaly or potential

accident.

DESCRIPTION

● The idea is to make location identification automatic in order to reduce the

response time in case of an accident.

● Vehicular Sensors are attached to each of the vehicles.

● These sensors send a continuous stream of real-time data containing relevant

information about the position(in terms of longitude and latitude) and speed of

the corresponding vehicle.

● The underlying algorithm will look for certain anomalies in data such as

sudden drop in speed of the vehicle.

TECHNOLOGIES USED● Python

● Apache Spark Streaming

● Open Street Maps Integration

APPROACH

● Firstly we need to simulate the vehicular sensors.

● For this we generated random data using a python script.

● This data was generated at an interval of 15 seconds.

● It contained all relevant information about a particular vehicle like its GPS

coordinates, speed etc.

● Apache spark streaming server is used to collect and analyse the incoming

data from these sensors.

● The incoming data is processed by the spark streaming server.

● This data is then plotted on the map to look for possible accident spots.

● Following three kinds of markers are used in the map

○ Red Marker- This indicates the location where accident has actually

occurred.

○ Yellow Marker- This indicates about speed limit crossing.

○ Blue Marker- This indicates about the heavy traffic in a particular

location.

● This is data is then stored for further processing.

DESIGN

SCREENSHOTS

CONCLUSIONReal-time visibility into driver behaviorMonitor driver speedsDetailed event recordsAbility to set alarm criteria