Open AccessA Handover Decision Model for Unmanned Vehicles for Ubiquitous Communication in Wireless Networks
Author(s) -
Sonika Singh,
AUTHOR_ID,
Mandeep Kaur Sandhu
Publication year - 2022
Publication title -
international journal of electrical and electronic engineering and telecommunications
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.171
H-Index - 6
ISSN - 2319-2518
DOI - 10.18178/ijeetc.11.2.132-137
Subject(s) - drone , handover , computer science , dependency (uml) , fuzzy logic , wireless , real time computing , throughput , process (computing) , matlab , wireless network , computer network , artificial intelligence , telecommunications , genetics , biology , operating system
Drone technology also referred to as Unmanned Aerial Vehicles (UAVs) provides many benefits to wireless communication systems. In the coming years, the linked drones will be commonly used in different fields such as delivery methods, monitoring services, and defense networks etc. However, security and logical characterization of handover probability in drone systems is still a big problem. Various fuzzy based methods were proposed by a number of researchers to resolve these issues, but the problem with conventional methods was that they considered only limited dependency factors. However, there are some other dependency factors as well that affect the performance of the drones. In this research, a fuzzy based decision model is proposed. The proposed fuzzy system uses five dependency factors such as coverage, speed limit, cost, connection time and security as inputs. To process these inputs 160 rules are defined in the given fuzzy system in order to produce an output in terms of handover probability. The performance analysis of the traditional and proposed model is analyzed in the MATLAB software when they were moving in random and straight directions, in terms of estimation level, PDR, throughput and delay. The simulation results showed that the proposed model is efficient and stable as compared to traditional fuzzy system.