User-Driven Cloud Transportation System for Smart Driving

User-Driven Cloud Transportation System for Smart Driving 1

User-Driven Cloud Transportation System for Smart Driving


Contents

• Existing System

• Drawbacks of Existing System

• Proposed System

• User-Driven CTS application scenario

• CTS components

• Core Functions

• System Architecture

• Prototype Implementation

• References


Existing System

Intelligent Transportation System


Intelligent transportation system

Intelligent Transportation System (ITS) applies advanced technologies of electronics, communications, computers, control and sensing and detecting in all kinds of transportation system in order to improve safety, efficiency and service, and traffic situation through transmitting real-time information.


Drawbacks of existing system

• Focused on surveillance of the roadways.

• The high cost and low mobility of monitoring equipment limit the deployment scale and density

• Pervasive data from transportation has not been collected.

• Large-scale of user leads to the requirements of enormous computing and storage resources


Proposed System

User-Driven Cloud Transportation System for Smart Driving

• A user-driven pattern of ITS based on cloud computing technology named user-driven cloud transportation system (CTS) to enhance data collection capability.


User-Driven Cloud Transportation System for Smart Driving (Cont’d)

The user-driven CTS has unique features in the following aspects:

• CTS operates around users to calculate the gathered data and provide real time guidance back to them

• User data is processed to reconstruct the traffic model, based on which the prediction algorithm can be performed to guide an optimized route for each user and the whole transportation system.

• The central processing system applies the cloud computing architecture to support mass data storage and distributed computing from user cloud.


User-Driven CTS application scenario


User-Driven CTS application scenario (cont’d)

• Each client needs to register in the system and provides username and password for authentication.

• The user data including location and velocity are calculated using the GPS receiver and positioning techniques.

• The mobile application sends message to the cloud server for storage and computing.

• The server collects data, processes them and reconstructs a traffic model.

• Server should send each client personalized and dynamic information about traffic.


CTS components

The CTS is composed of three main components

• cloud server

• user cloud and

• background management system


CTS components (cont’d)


CTS components (cont’d)

• Cloud server is a cluster of virtualized resources providing services.

• The server listens and receives traffic data submitted by the mobile agents.

• Data processing algorithm was adapted to aggregate the data into distributed cloud database with specific standard, such as position, time-stamp and event type.

• Background management system includes the management interface for central cloud server, which implements the function of system performance monitoring, information querying, user management, etc.


Core Functions

Services implemented to support intelligent transportation include the following core functions:

• Service Discovery

• Routing Service

• Timing Service

• Prediction Service

• Communication Service


Core Functions (Cont’d)


Service Discovery

• The Service Discovery (SD) keeps an up-to-date database of services.

• This work is usually done in Cloud and Grid systems by using data replication techniques.


Routing Service

• Routing Service (RS) is responsible for the computing of best route from current position to the destination inputted by mobile agent.

• The RS receives messages and replies the best route and also the time expected for driving with the Timing Service (TS).


Timing Service

• Timing Service (TS) is responsible for the computing of time expected to spend on the giving route from the Routing Service (RS).

• The RS and TS will be recalled when the user change route according to the suggestion replied by Prediction Service (PS).


Prediction Service

• Prediction Service (PS) pushes message of real-time traffic situation and location-specific alerts.

• The function is implemented with algorithm to fuse and analyze the pervasive traffic data collected anonymously from user.

• PS is aware of the road network topology and aims to optimize traffic flow on users’ perspective and also on a macro scale.


Communication Service

• A Communication Service (CS) is a service wrapper for message conveys between cloud server and vehicles mobile devices.

• CS uses the structure of publish/subscribe to push real-time message between server and user bi-directionally according to the subscriptions of user-interested topic.


Other Services

Besides the core CTS services, system allows other types of services implemented easily. Examples of additional services could include:

• POI Service: A service finding POI closest to the selected area or the current position.

• Emergency Service:

• Social Service: Social service maintains a social network in CTS allowing user to share information between friends.


System Architecture

• A three layers structure of cloud application system is designed.

i. Infrastructure as a Service (IaaS)

ii. Platform as a Service (PaaS)

iii. Software as a Service (SaaS)


Infrastructure as a Service (IaaS)

• Fabric Layer containing storage resources, network resources and computing resources.

• A unified source layer is constructed above the fabric layer with the help of virtual machines and their monitoring system.

• NoSQL database, , is applied for traffic data cloud storage .

• Traffic data can be naturally divided by area which fits the NoSQL and MapReduce pattern in cloud storage and computing.


Infrastructure as a Service (Cont’d)



• Vehicle velocity data is stored in key-value pair <VehicleID, Velocity>

• MapReduce computing model consist of two parts namely Map and Reduce.

• MapReduce master uses hash function (Hash(VehicleID) mod M)

• user-defined Map function will be called to calculate the average velocity for each vehicle <VehicleID, avg>

• the mapping from vehicle to corresponding RoadID into intermediate result in the collection of <RoadID, avg>.


Platform as a Service (PaaS)

• System deliver a storage and computing service from IaaS platform for higher level functions.

• Web Service Framework

• Different functions are linked through well-defined interface by SOA-based lightweight web service framework.

• The definition of interface is independent of the hardware platforms, operating systems and programming languages.


Platform as a Service (Cont’d)

• Data Access Object Framework

• ADO .NET entity framework is integrated.

• Publish/Subscribe Mechanism

• CTS uses MQTT protocol to push message between central server and mobile agent.


Software as a Service (SaaS)

• Application software in the cloud and cloud users access the software from cloud clients.

• SaaS model in CTS provides the core business logic of applications including traffic, living, social and user information services.


Software as a Service (Cont’d)


Software as a Service (Cont’d)

• Mobile user calls the service of message broker Subscribe()

• Message broker declares the interface and implements it with WebInvoke() in cloud server when it receives the subscription.

• Cloud server uses readDB() and writeDB() to interact with database for MapReduce calculation.

• CTS uses an open source message broker Mosquitto to push message to mobile user.


System Architecture (Cont’d)


Prototype Implementation


References

• [1] Meng Ma, Yu Huang, Chao-Hsien Chu, Ping Wang, User-Driven Cloud Transportation System for Smart Driving,2012 IEEE 4th International Conference on Cloud Computing Technology and Science.

• http://en.wikipedia.org/wiki/Intelligent_transportation_system

User-Driven Cloud Transportation System for Smart Driving

Engineering

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