
Empirical Analysis of Intelligent Handover Mechanism using Numerous Fuzzy Strategies
Publication year - 2019
Publication title -
international journal of innovative technology and exploring engineering
Language(s) - English
Resource type - Journals
ISSN - 2278-3075
DOI - 10.35940/ijitee.b1065.1292s319
Subject(s) - computer science , handover , quality of service , computer network , fuzzy logic , throughput , distributed computing , node (physics) , wireless , artificial intelligence , engineering , telecommunications , structural engineering
In the communication world, each and every individual belongs different types of communication modes to establish their data transmission needs around globe. Most preferable and well known communication medium is a Wireless Communication Mode, which provides stability and standardization to users to make their communication perfect. However, the communication lacking occurs due to low-latency and energy oriented issues, which indirectly affects the overall network throughput and it leads the overall network lifetime to low. So, there is a necessity to introduce a new algorithm to produce a stabilized platform to satisfy the user's communication needs with proper Quality-of-Service (QoS) norms. A new system is introduced, which is helpful to achieve high throughput and energy efficient communication between clusters by using fuzzy laws. In this system, Genetic Algorithm is used to generate Optimized Solutions with high Accuracy as well as it is used to reduce number of handovers by means of identify the energy level of each member nodes and design the pathway for communication according to that. And if dynamically generates the Node Analyzer (NA) for identifying the best nodes for communication and using that way the communication is established. The main concept in the proposed approach is achieved by means of designing a new algorithm called "Fuzzy Logic based Handover Decision Algorithm (FLHDA)", which provides better efficiency by means of integrating the following three different approaches such asFuzzification Stage, Fuzzy Inference Stage andDeffuzification Stage. For all the entire proposed approach guarantees that the outcome assures the Quality-of-Service, which is achieved by means of enhanced throughput, minimized end to end delay and energy improvements with less consumption strategies.