Due to handover failure, call drop occurs frequently. When a large number of incoming and handoff calls arrive at the same time, the performance of the conventional handoff algorithms may fall down. Moreover, multiple factors such as signal quality and available channels of cellular network can’t be evaluated in conventional algorithms. When mobile station (MS) moves, the connection of MS with nearby base station (BS) has to be switched from one to adjacent station. In this case, unnecessary handoffs will be occurred due to lack of proper decision of handoffs or lack of consideration about signal quality with available free channels. As a result call drop will occur frequently. For performing handoff efficiently, fuzzy logic based handoff decision algorithm, adaptive handoff threshold level using neural network and priority based dynamic channel allocation algorithm using neuro-fuzzy system has been proposed in this work. These algorithms will mainly focus on the proper decision of handoff based on evaluating signal strength, available free channels, spectrum efficiency, MS speed and distance from BS so that unnecessary and inefficient handoffs can’t be performed. Simulation revealed that using neuro-fuzzy system, the channel capacity, SIR and Handoff management were improved better than the others in terms of spectrum utilization efficiency, MS speed and SIR. The efficacy of the methodology has been proved by imitating the proposed model using MATLAB software.
A Mobile Ad-hoc Network (MANET) is an independent assortment of mobile users that communicate over moderately bandwidth constrained wireless links. MANET’s topology is dynamic that can change rapidly because the nodes move freely and can organize themselves randomly; has the advantage of being quickly deployable. Although numerous routing protocols have been proposed for mobile ad hoc networks, there is no universal scheme that works well in scenarios with different network sizes, traffic loads and node mobility patterns, so mobile ad hoc routing protocol election presents a great challenge. In this paper, an attempt has been made to compare the performance of three routing protocols in Mobile Ad-hoc Networks – Ad-Hoc On-demand Distance Vector (AODV), Dynamic Source Routing (DSR) and Destination Sequenced Distance Vector (DSDV). We have evaluated the performance of these routing protocols with varying the number of mobile nodes and packet sizes on the basis of four important metrics such as packet delivery ratio, average end to end delay, normalized routing overhead and throughput. Network Simulator version 2.35 (NS-2.35) is used as the simulation tool for evaluating these performance metrics. The outcome of this research shows that AODV protocol outperforms DSDV and DSR protocols.
For socioeconomic development and the well-being of citizens, developing a precise model for predicting housing prices is always required. So that, a real estate broker or a house seller/buyer can get an intuition in making well-knowledgeable decisions from the model. In this work, a various set of machine learning algorithms such as Linear Regression, Decision Tree, Random Forest are being implemented to predict the housing prices using available datasets. The housing datasets of 506 samples and 13 feature variables from January 2015 to November 2019 were taken from the StatLib library which is maintained at Carnegie Mellon University. Since housing price is emphatically connected to different factors like location, area, the number of rooms; it requires all of this information to predict individual housing prices. This paper will apply both traditional and advanced machine learning approaches to investigate the difference among several advanced models to explore various impacts of features on prediction methods. This paper will also provide an optimistic result for housing price prediction by comprehensively validating multiple techniques in model execution on regression.
The most important index for Quality of Service is call drop which has not been accurately discussed before. Sometimes the network disconnects while talking on the phone, this disconnection is called a dropped call. The level of call drop of mobile operators in the every country has increased significantly lately. As a result, thousands of mobile phone users are suffering. To reduce call drop, at first the factors causing call drop have identified in this paper. Based on these factors, dropped call model has developed to find out the parameters associated with call drop. Then some existing techniques have analyzed mathematically to improve the different parameters of these factors to reduce call drop. Three main factors have identified from the developed dropped call model causing call drop such as the lack of available channels, the poor signal quality and the handover failure. In this paper, mathematical analysis of cell splitting, cell sectoring and Microcell zone concept have provided to support extra connecting demand, ongoing service and to improve the signal to interference ratio. Also queuing handoff will be analyzed mathematically with justification to avoid disturbing service call drop. Simulation of mathematical model of these call drop reduction techniques has performed using MATLAB software.
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.
customersupport@researchsolutions.com
10624 S. Eastern Ave., Ste. A-614
Henderson, NV 89052, USA
Copyright © 2024 scite LLC. All rights reserved.
Made with 💙 for researchers
Part of the Research Solutions Family.