scholarly journals HYBRID MOBILITY PREDICTION OF 802.11 INFRASTRUCTURE NODES BY LOCATION TRACKING AND DATA MINING

2016 ◽  
Vol 3 (1) ◽  
pp. 15-38
Author(s):  
Biju Issac ◽  
Khairuddin Ab Hamid ◽  
C.E. Tan
Keyword(s):  
Data Mining ◽  
Prediction Accuracy ◽  
Access Point ◽  
Mobile Node ◽  
Mobility Prediction ◽  
Location Tracking ◽  
Reliable Prediction ◽  
Prediction Scheme ◽  
Hand Off ◽  
Future Path

In an IEEE 802.11 Infrastructure network, as the mobile node is moving from one access point to another, the resource allocation and smooth hand off may be a problem. If some reliable prediction is done on mobile node’s next move, then resources can be allocated optimally as the mobile node moves around. This would increase the performance throughput of wireless network. We plan to investigate on a hybrid mobility prediction scheme that uses location tracking and data mining to predict the future path of the mobile node. We also propose a secure version of the same scheme. Through simulation and analysis, we present the prediction accuracy of our proposal.

scholarly journals Support Vector Machine Based Mobility Prediction Scheme in Heterogeneous Wireless Networks

2015 ◽  
Vol 2015 ◽  
pp. 1-10 ◽  
Author(s):  
Jiamei Chen ◽  
Lin Ma ◽  
Yubin Xu
Keyword(s):  
Support Vector Machine ◽  
Wireless Networks ◽  
Prediction Accuracy ◽  
Heterogeneous Wireless Networks ◽  
Support Vector ◽  
Mobility Prediction ◽  
Mobility Patterns ◽  
Prediction Scheme ◽  
The Cost ◽  
Different Characteristics

To improve the intelligence of the mobile-aware applications in the heterogeneous wireless networks (HetNets), it is essential to establish an advanced mechanism to anticipate the change of the user location in every subnet for HetNets. This paper proposes a multiclass support vector machine based mobility prediction (Multi-SVMMP) scheme to estimate the future location of mobile users according to the movement history information of each user in HetNets. In the location prediction process, the regular and random user movement patterns are treated differently, which can reflect the user movements more realistically than the existing movement models in HetNets. And different forms of multiclass support vector machines are embedded in the two mobility patterns according to the different characteristics of the two mobility patterns. Moreover, the introduction of target region (TR) cuts down the energy consumption efficiently without impacting the prediction accuracy. As reported in the simulations, our Multi-SVMMP can overcome the difficulties found in the traditional methods and obtain a higher prediction accuracy and user adaptability while reducing the cost of prediction resources.

Using Stratified Sample and Grid Search to Improve Disease Prediction Accuracy of SVM

2013 ◽  
Vol 295-298 ◽  
pp. 644-647 ◽  
Author(s):  
Yu Kai Yao ◽  
Hong Mei Cui ◽  
Ming Wei Len ◽  
Xiao Yun Chen
Keyword(s):  
Data Mining ◽  
Support Vector Machine ◽  
Classification Accuracy ◽  
Prediction Accuracy ◽  
Support Vector ◽  
Disease Prediction ◽  
Data Mining Algorithm ◽  
Grid Search ◽  
Mining Algorithm ◽  
Stratified Sample

SVM (Support Vector Machine) is a powerful data mining algorithm, and is mainly used to finish classification or regression tasks. In this literature, SVM is used to conduct disease prediction. We focus on integrating with stratified sample and grid search technology to improve the classification accuracy of SVM, thus, we propose an improved algorithm named SGSVM: Stratified sample and Grid search based SVM. To testify the performance of SGSVM, heart-disease data from UCI are used in our experiment, and the results show SGSVM has obvious improvement in classification accuracy, and this is very valuable especially in disease prediction.

scholarly journals Comparing sets of patterns with the Jaccard index

2018 ◽  
Vol 22 ◽  
Author(s):  
Sam Fletcher ◽  
Md Zahidul Islam
Keyword(s):  
Data Mining ◽  
Driving Force ◽  
Prediction Accuracy ◽  
Jaccard Index ◽  
Classification Algorithms ◽  
Single Element ◽  
Temporal Data ◽  
Real World Data ◽  
Actionable Knowledge ◽  
Computational Simplicity

The ability to extract knowledge from data has been the driving force of Data Mining since its inception, and of statistical modeling long before even that. Actionable knowledge often takes the form of patterns, where a set of antecedents can be used to infer a consequent. In this paper we offer a solution to the problem of comparing different sets of patterns. Our solution allows comparisons between sets of patterns that were derived from different techniques (such as different classification algorithms), or made from different samples of data (such as temporal data or data perturbed for privacy reasons). We propose using the Jaccard index to measure the similarity between sets of patterns by converting each pattern into a single element within the set. Our measure focuses on providing conceptual simplicity, computational simplicity, interpretability, and wide applicability. The results of this measure are compared to prediction accuracy in the context of a real-world data mining scenario.

scholarly journals Data Mining dalam Akurasi Tingkat Kelayakan Pakai terhadap Peralatan Perangkat Keras

2020 ◽  
pp. 83-88
Author(s):  
Nurhidayat ◽  
Sarjon Defit ◽  
Sumijan
Keyword(s):  
Data Mining ◽  
Information System ◽  
Rough Set ◽  
Access Point ◽  
Learning Processes ◽  
Discernibility Matrix ◽  
Student Work ◽  
Hard Drives ◽  
Good Shape ◽  
Equivalency Class

Hardware is a computer that can be seen and touched in person. Hardware is used to support student work and learning processes. The hardware should always be in good shape. If any damage should be done quickly. The benefits of this study provide a viable level of data against hardware tools. The purpose of this study determines that hardware that is worth using quickly and precisely so easily can be repaired and replaced. Hard-processed action consists of 12 projectors, 2 units of access point, 6 units of monitors, and 20 CPU units. To see the level of appropriateness regarding hard drives requires a rough set algorithm with that stage: information system; Decision system; Equivalency class; Discernibility matrix; Discernibility Matrix module D; Reduction; Generate Rules. The results of the 40 devices of study STMIK Indonesia Padang subtract college have 10 rules of policy on whether the hardware is still viable, repaired or replaced. So using a rough set algorithm is particularly appropriate to apply in a verifiable level of accuracy to fast and precise hardware.

Simulation of a Mobility Prediction Scheme Based on Neuro-Fuzzy Theory in Mobile Computing

SIMULATION ◽  
2000 ◽  
Vol 75 (1) ◽  
pp. 6-17 ◽  
Author(s):  
Joon-Min Gil ◽  
Chan Yeol Park ◽  
Chong-Sun Hwang ◽  
Youn-Hee Han ◽  
Young-Sik Jeong
Keyword(s):  
Mobile Computing ◽  
Fuzzy Theory ◽  
Mobility Prediction ◽  
Neuro Fuzzy ◽  
Prediction Scheme

Simulating Game Applications in Mobile IPv6 Protocol

2012 ◽  
pp. 22-40
Author(s):  
Omar Raoof ◽  
Hamed Al-Raweshidy
Keyword(s):  
Energy Consumption ◽  
Decision Process ◽  
Access Point ◽  
Mobile Node ◽  
Mobile Ipv6 ◽  
Network Selection ◽  
Mobile Nodes ◽  
Ipv6 Protocol ◽  
Wireless Interface ◽  
The Right

This chapter proposes a novel game-based green interface/network selection mechanism that is an extension to the multi-interface fast-handover mobile IPv6 protocol and works when the mobile node has more than one wireless interface. The mechanism controls the handover decision process by deciding whether a handover is needed or not and helps the node to choose the right access point at the right time. Additionally, the mechanism switches the mobile nodes interfaces “ON” and “OFF” when needed to control the mobile node’s energy consumption and improves the handover latency.

A Fast Handover Method for Real-Time Multimedia Services

2011 ◽  
pp. 179-190
Author(s):  
Jani Puttonen ◽  
Ari Viinikainen ◽  
Miska Sulander ◽  
Timo Hamalainen
Keyword(s):  
Real Time ◽  
Mobile Node ◽  
Mobile Ipv6 ◽  
Network Simulator ◽  
Fast Handover ◽  
Ipv6 Network ◽  
Hand Off ◽  
Flow Information ◽  
Speed Up ◽  
Performance Results

Mobile IPv6 (MIPv6) has been standardized for mobility management in the IPv6 network. When a mobile node changes its point of attachment in the IPv6 network, it experiences a time due MIPv6 procedures when it cannot receive or send any packets. This time called the handover delay might also cause packet loss resulting undesired quality-of-service degradation for various types of applications. The minimization of this delay is especially important for real-time applications. In this chapter we present a fast handover method called the flow-based fast handover for Mobile IPv6 (FFHMIPv6) to speed up the MIPv6 handover processes. FFHMIPv6 employs flow information and IPv6-in-IPv6 tunneling for the fast redirection of the flows during the MIPv6 handover. Also, FFHMIPv6 employs a temporary hand-off-address to minimize the upstream connectivity. We present the performance results comparing the FFHMIPv6 method to other fundamental handover methods with Network Simulator 2 (ns-2) and Mobile IPv6 for Linux (MIPL) network.

Sign in / Sign up

Export Citation Format

Share Document