Machine to Machine Reliable Scenario: A Congestion Diversion

?The human to machine type communication unable gives affective and desire result  and the mobile services like 3G, 4G that did not provide an efficient speed and coverage for data exchange to perform machine-to-machine communication. Congestion control in the wireless networks is the foremost

Project Title

Machine to Machine Reliable Scenario: A Congestion Diversion

Project Area of Specialization

Information & Communication Technology

Project Summary

•The human to machine type communication unable gives affective and desire result  and the mobile services like 3G, 4G that did not provide an efficient speed and coverage for data exchange to perform machine-to-machine communication. Congestion control in the wireless networks is the foremost problem in this world. Modern Telecommunication, Computer Networks and both wired and wireless communications including the Internet, are being intended for fast transmission of large amount of data, for which Congestion Control is very important. Without proper Congestion Control mechanism, the congestion downfall of such networks would become extremely complex and is a real possibility.

Regarding our research the congestion that arise due to the large number of  MTC(machine type communication) devices /users / subscriber connect with assigned  channels to BTS and make all channels full busy. If next MTC device want to connect with the occupied channels of BTS (BTS1) it will be blocked and doesn’t serve because channel are occupied by subscriber / user.
  we make a scenario to serve the next MTC device by transferring it to another BTS (BTS2) . Which has channels not busy considering the microwave antenna in both BTS's which uses for line of sight communication to control the congestion.

For  implementation we will use  MATLAB Software. We will control the congestion between the BTS's using fuzzy logic. in fuzzy logic we've taken the five parameters named as Data rate , Range of BTS1(high range, medium range,low  range) , Queue size , channel(subscribers) , Range of BTS1 to BTS2 (2000 km) and single output as congestion control value, untill now we have made the rules in fuzzy logic and look forward to get the desired result by keep on changing and trying the different member function and comparison of different functions with each other.

This research can be extended for more than two BTS's while our research is based to control the congestion between two BTS's.

On fuzzy logic researcher have done very low level of work this research will be one of the better choice for the upcoming researcher to perform thier research and task using fuzzy logic to simulate any system scienario.

Project Objectives

Alike other's challenges Congestion is also a major andd challenging problem in wireless communication that arise due to the large number of  MTC(machine type communication) devices connect with assigned  channels to BTS and make all channels full busy. If next MTC device want to connect with the occupied channels of BTS (BTS1) it will be blocked and doesn’t serve because channel are occupied by subscriber / user.
 We make a scenario to serve the next MTC device by transferring it to another BTS (BTS2) . Which has channels not busy considering the microwave antenna in both BTS's which uses for line of sight communication to control the congestion.

Project Implementation Method

For  implementation we will use  MATLAB Software. We will control the congestion between the BTS's using fuzzy logic. in fuzzy logic we've taken the five parameters named as Data rate , Range of BTS1(high range, medium range,low  range) , Queue size , channel(subscribers) , Range of BTS1 to BTS2 (2000 km) and single output as congestion control value, untill now we have made the rules in fuzzy logic and look forward to get the desired result by keep on changing and trying the different member function and comparison of different functions with each other.

Benefits of the Project

This research will enable us a route to MTC ( machine type communication) user's to communicate with each other without any congestion and which will leads us to smart world concept.

Technical Details of Final Deliverable

Fuzzy logic is the way to evaluate any type of system.The essential parameter's like Data rate , Range of BTS1(high range, medium range,low  range) , Queue size , channel(subscribers) , Range of BTS1 to BTS2 (2000 km) and single output as congestion control value ranging from 0 to 1. We've made the rules in fuzzy logic but we keep on changing the member function to get the effective result as much as possible. Further more this research can be extended to consider more BTS's as future work.

Final Deliverable of the Project

Software System

Core Industry

Telecommunication

Other Industries

Telecommunication

Core Technology

Internet of Things (IoT)

Other Technologies

Internet of Things (IoT)

Sustainable Development Goals

Sustainable Cities and Communities

Required Resources

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