AU2021101897A4 - A machine learning and IOT based system for intelligent routing - Google Patents
A machine learning and IOT based system for intelligent routing Download PDFInfo
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- AU2021101897A4 AU2021101897A4 AU2021101897A AU2021101897A AU2021101897A4 AU 2021101897 A4 AU2021101897 A4 AU 2021101897A4 AU 2021101897 A AU2021101897 A AU 2021101897A AU 2021101897 A AU2021101897 A AU 2021101897A AU 2021101897 A4 AU2021101897 A4 AU 2021101897A4
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- machine learning
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- iot
- routing
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- 238000010801 machine learning Methods 0.000 title claims abstract description 25
- 238000000034 method Methods 0.000 claims abstract description 9
- 238000011156 evaluation Methods 0.000 claims 4
- 239000000446 fuel Substances 0.000 claims 1
- 238000007781 pre-processing Methods 0.000 abstract description 3
- 238000004088 simulation Methods 0.000 abstract description 3
- 238000004891 communication Methods 0.000 description 4
- 238000005516 engineering process Methods 0.000 description 4
- 238000005457 optimization Methods 0.000 description 4
- 238000004458 analytical method Methods 0.000 description 2
- 238000010586 diagram Methods 0.000 description 2
- 230000002265 prevention Effects 0.000 description 2
- 238000012545 processing Methods 0.000 description 2
- 230000003466 anti-cipated effect Effects 0.000 description 1
- 230000006399 behavior Effects 0.000 description 1
- 230000005540 biological transmission Effects 0.000 description 1
- 238000007405 data analysis Methods 0.000 description 1
- 238000013480 data collection Methods 0.000 description 1
- 238000001514 detection method Methods 0.000 description 1
- 230000000116 mitigating effect Effects 0.000 description 1
- 238000012544 monitoring process Methods 0.000 description 1
- 238000011160 research Methods 0.000 description 1
Classifications
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- H—ELECTRICITY
- H04—ELECTRIC COMMUNICATION TECHNIQUE
- H04W—WIRELESS COMMUNICATION NETWORKS
- H04W4/00—Services specially adapted for wireless communication networks; Facilities therefor
- H04W4/30—Services specially adapted for particular environments, situations or purposes
- H04W4/40—Services specially adapted for particular environments, situations or purposes for vehicles, e.g. vehicle-to-pedestrians [V2P]
- H04W4/44—Services specially adapted for particular environments, situations or purposes for vehicles, e.g. vehicle-to-pedestrians [V2P] for communication between vehicles and infrastructures, e.g. vehicle-to-cloud [V2C] or vehicle-to-home [V2H]
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- G—PHYSICS
- G01—MEASURING; TESTING
- G01C—MEASURING DISTANCES, LEVELS OR BEARINGS; SURVEYING; NAVIGATION; GYROSCOPIC INSTRUMENTS; PHOTOGRAMMETRY OR VIDEOGRAMMETRY
- G01C21/00—Navigation; Navigational instruments not provided for in groups G01C1/00 - G01C19/00
- G01C21/26—Navigation; Navigational instruments not provided for in groups G01C1/00 - G01C19/00 specially adapted for navigation in a road network
- G01C21/34—Route searching; Route guidance
-
- G—PHYSICS
- G08—SIGNALLING
- G08G—TRAFFIC CONTROL SYSTEMS
- G08G1/00—Traffic control systems for road vehicles
- G08G1/09—Arrangements for giving variable traffic instructions
- G08G1/0962—Arrangements for giving variable traffic instructions having an indicator mounted inside the vehicle, e.g. giving voice messages
- G08G1/0968—Systems involving transmission of navigation instructions to the vehicle
-
- H—ELECTRICITY
- H04—ELECTRIC COMMUNICATION TECHNIQUE
- H04W—WIRELESS COMMUNICATION NETWORKS
- H04W4/00—Services specially adapted for wireless communication networks; Facilities therefor
- H04W4/30—Services specially adapted for particular environments, situations or purposes
- H04W4/38—Services specially adapted for particular environments, situations or purposes for collecting sensor information
-
- G—PHYSICS
- G08—SIGNALLING
- G08G—TRAFFIC CONTROL SYSTEMS
- G08G1/00—Traffic control systems for road vehicles
- G08G1/09—Arrangements for giving variable traffic instructions
- G08G1/0962—Arrangements for giving variable traffic instructions having an indicator mounted inside the vehicle, e.g. giving voice messages
- G08G1/0967—Systems involving transmission of highway information, e.g. weather, speed limits
- G08G1/096766—Systems involving transmission of highway information, e.g. weather, speed limits where the system is characterised by the origin of the information transmission
- G08G1/096783—Systems involving transmission of highway information, e.g. weather, speed limits where the system is characterised by the origin of the information transmission where the origin of the information is a roadside individual element
-
- H—ELECTRICITY
- H04—ELECTRIC COMMUNICATION TECHNIQUE
- H04L—TRANSMISSION OF DIGITAL INFORMATION, e.g. TELEGRAPHIC COMMUNICATION
- H04L67/00—Network arrangements or protocols for supporting network services or applications
- H04L67/01—Protocols
- H04L67/12—Protocols specially adapted for proprietary or special-purpose networking environments, e.g. medical networks, sensor networks, networks in vehicles or remote metering networks
-
- H—ELECTRICITY
- H04—ELECTRIC COMMUNICATION TECHNIQUE
- H04W—WIRELESS COMMUNICATION NETWORKS
- H04W84/00—Network topologies
- H04W84/18—Self-organising networks, e.g. ad-hoc networks or sensor networks
Landscapes
- Engineering & Computer Science (AREA)
- Radar, Positioning & Navigation (AREA)
- Remote Sensing (AREA)
- Computer Networks & Wireless Communication (AREA)
- Signal Processing (AREA)
- Physics & Mathematics (AREA)
- General Physics & Mathematics (AREA)
- Automation & Control Theory (AREA)
- Traffic Control Systems (AREA)
Abstract
A machine learning and IOT based system for intelligent routing
The present invention relates to machine learning and IOT based system for intelligent routing.
In order to improve the reliability and efficacy of public transport, many Internet of Things
attempts have been made. Many problems, such as car traffic congestion, road safety and
improper use of parking spaces for vehicles, have been handled and controlled by the IoT. The
present invention provides an intelligent routing based on a distributed cloud architecture of IoT
for managing the traffic system combined with a machine learning to improve the process of
finding the optimized route in the minimum time based on the state of traffic on the road.
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Figure 3
Description
2/2
r4%ificd ata Pre processing Iafridat a ffor Praeetir ant Simulation ]Siclr Mfacl~ri.Lea~rninrg Algorithm
Figure 3
A machine learning and IOT based system for intelligent routing
Technical field of invention:
The present invention relates to machine learning which is an effective contributor to enable the rapid processing of large amounts of IoT system data to produce trends of interest to data analysts. The proposed intelligent routing is more accurate and effective but quite expensive.
Background of the present invention
With the rise of the Internet of Things (IoT), applications have become smarter and connected devices give rise to their exploitation in all aspects of a modem city. As the volume of the collected data increases, Machine Learning (ML) techniques are applied to further enhance the intelligence and the capabilities of an application.
Smart transportation is considered to be an important term that covers route optimization, parking, street lights, accident prevention/detection, road anomalies, and infrastructure applications.
For predicting traffic jams and also making routes to the specific destination point, the combination of IoT sensors along with machine learning algorithms systems is used.
Using sensors embedded to the vehicles, or mobile devices and devices installed in the city, it is possible to offer optimized route suggestions, easy parking reservations, economic street lighting, telematics for public means of transportation, accident prevention, and autonomous driving.
An IoT infrastructure usually implements methods to handle, store, and analyze big data. [00010] IoT systems have monitoring capabilities, management of nodes, storing and analysing data, configurable data based laws, etc. It is often necessary that some data analysis takes place in the
IoT devices instead of some centralised node, depending on the application, as it happens in the cloud computing platform.
In order to reduce the overload generated by the massive transmission of all data to certain central cloud nodes, an intermediate node is also available with adequate resources to handle sophisticated processing tasks, physically positioned close to the end network components.
The implementation of machine learning algorithms in an IoT infrastructure will bring about major changes in the applications or the infrastructure itself. Machine learning can be applied for network management, congestion mitigation, and resource utilisation optimization, but also for real-time or offline data analysing and decision making.
Finally, the data are stored in cloud servers, where they are available for advanced analysis using a variety of machine learning techniques and sharing among other devices, leading to the creation of modern added value smart transportation.
Brief description of the drawings
The implementation and various embodiments of the present invention have been explained below in detail with the help of various diagrams and illustrations.
FIG. 1 represents the framework for machine learning in IoT.
From a spatial perspective, machine learning in the internet of things has completely changed the way of tracing vehicles and also resulted in increasing accuracy of prediction and effectiveness in routing.
The framework is developed and implemented in accordance with the various components explained in FIG. 1.
FIG.2 depicts the IoT procedure.
According to the embodiment, a distributed solution is introduced to find the reduced traffic for a driver based on the technology of IoT and proposed the shortest route in terms of the distance and the traffic flow to arrive in the best conditions based on the machine learning algorithm.
FIG.3 shows a schematic diagram of the intelligent routing process.
Summary of the invention
A main object of the present invention is to provide optimized routing. Route optimization is provided to find the best route for a specified destination. The idea is to explore the capabilities of mobile crowd-sensing for intelligent routing by using a machine learning algorithm for route optimization. In conclusion, traffic information for routes that do not have VANET information available is also obtained from the google maps application. The traffic data from the two sources is combined and the user is given the best route.
Detailed description of invention
Information communication technologies have gained increasing attention and importance in modem transportation systems. Advances in information and communication technologies in areas such as hardware, software, and communications have created new opportunities for developing a sustainable, intelligent transportation system. By seamlessly integrating vehicles and sensing devices, their sensing and communication capabilities can be leveraged to achieve smart and intelligent transportation systems. Sensor technology can be integrated with the transportation to achieve a sustainable intelligent transportation system that addresses issues such as high levels of traffic congestions, and improved road safety. In analysis, cameras on particular roads and a vehicle fitted with a data logger are used to collect traffic data. Emissions are measured on the basis of a vehicle-specific power model, and Google Maps collects common time data. Fixed location sensors or other tracking devices did not collect the traffic information. Mobile devices for end users can only submit anonymous information about their speed and position using the map application. A machine learning algorithm is implemented in the research that uses crowdsourcing to obtain information and make suggestions about the last mile of a trip.
Several IoT devices have been used to classify the presence of cars in a parking spot and send the data to a centralised system. In addition, the great progress that has already been made with the aid of IoT and ML in the field of smart transportation has become obvious, although even better progress on this subject is anticipated in the coming years.
According to the embodiment of machine learning in IoT, sensors used for traffic data collection. Based on the traffic data simulation, preprocessing is done. The data acquired from each sensor during traffic were transmitted to a smartphone via Bluetooth. Machine learning algorithms are used for prediction to find the optimized route.
Features of intelligent routing process
The current invention provides the best path is viewed in terms of two elements: the total distance of the trip and the total time consumed to arrive at the location of the parking which is related to the traffic flow on this path.
The current invention will allow us to choose between a set of alternatives routes.
The current invention implements a distributed architecture which contains a set of distributed intelligent agents each one of them has a predefined behaviour.
Routing information uses different sensors (such as cameras, weather sensors, radars, ultrasonic, loops) placed on roads that collect data about traffic conditions to enable users to make informed decisions regarding alternate routes and expected arrival times.
The current invention helps drivers to find the best route by taking into consideration a set of constraints such as the total travelling time to reach destination and the state of traffic road to reach this destination.
Claims (5)
1. A machine learning andJOT based system for intelligent routing comprising;
a set of sensors and devices based on the concept of IoT to control and manage all the traffic data in the city;
wherein the performance of the intelligent routing helps the people to find the optimized route identification.
2. The machine learning and JOT based system for intelligent routing as claimed in claim 1 wherein the performance evaluation of the intelligent routing is developed for improving the prediction of the routing process.
3. The machine learning and JOT based system for intelligent routing as claimed in claim 1 wherein the performance evaluation of the intelligent routing uses lower computational burden than methods relying on live tracking.
4. The machine learning and JOT based system for intelligent routing as claimed in claim 1 wherein the performance evaluation of the intelligent routing is a continuous connection between unlimited smart things to the internet.
5. The machine learning and JOT based system for intelligent routing as claimed in claim 1 wherein the performance evaluation of the active prediction anticipates the topology of the road to optimize fuel usage and assist drivers by adjusting the speed when the vehicle starts a descent or ascent.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| AU2021101897A AU2021101897A4 (en) | 2021-04-13 | 2021-04-13 | A machine learning and IOT based system for intelligent routing |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| AU2021101897A AU2021101897A4 (en) | 2021-04-13 | 2021-04-13 | A machine learning and IOT based system for intelligent routing |
Publications (1)
| Publication Number | Publication Date |
|---|---|
| AU2021101897A4 true AU2021101897A4 (en) | 2021-06-03 |
Family
ID=76132922
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| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| AU2021101897A Ceased AU2021101897A4 (en) | 2021-04-13 | 2021-04-13 | A machine learning and IOT based system for intelligent routing |
Country Status (1)
| Country | Link |
|---|---|
| AU (1) | AU2021101897A4 (en) |
-
2021
- 2021-04-13 AU AU2021101897A patent/AU2021101897A4/en not_active Ceased
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| FGI | Letters patent sealed or granted (innovation patent) | ||
| MK22 | Patent ceased section 143a(d), or expired - non payment of renewal fee or expiry |