DENSITY DETERMINATION IN MOBILE NETWORK USING CLUSTERING CLASSIFICATION
Journal Title: International Journal on Computer Science and Engineering - Year 2011, Vol 3, Issue 12
Abstract
The quality service in the mobile network is achievable via expanding or sharing the existing mobile infrastructure. The expandability or share ability of the mobile network is to be determined through some valuable measures based on its utilization on cluster basis. Here the cluster denotes a specific region of the single base station of mobile network and based on the analysis and the adoptable optimal solution density level is to be calculated. The observed results are represented and critically evaluated, to achieve effective and quality service to the mobile users via, sharing or expanding the mobile infrastructure which includes the future enhancement and the limitations.
Authors and Affiliations
Justin Sophia . I , Dr. N. Rama
Survey of Attacks on Mobile AdhocWireless Networks
Security has always been a key issue with wireless networks since there are no physical boundaries. Experience has shown numerous vulnerabilities to a variety of attacks even when security measures are in place. In the c...
A Recent Survey on Bloom Filters in Network Intrusion Detection Systems
Computer networks are prone to hacking, viruses and other malware; a Network Intrusion Detection System (NIDS) is needed to protect the end-user machines from threats. An effective NIDS is therefore a network security sy...
Rapid and Proactive Approach on Exploration of Database Vulnerabilities
In today's complicated computing environment, managing data has become the primary concern of all industries. Information security is the greatest challenge and it has become essential to secure the enterprise system res...
A GENERIC APPROACH TO CONTENT BASED IMAGE RETRIEVAL USING DCT AND CLASSIFICATION TECHNIQUES
With the rapid development of technology, the traditional information etrieval techniques based on keywords are not sufficient, content - based image retrieval (CBIR) has been an active research opic.Content Based Imag...
Q-Value Based Particle Swarm Optimization for Reinforcement Neuro-Fuzzy System Design
This paper proposes a combination of particle swarm optimization (PSO) and Q-value based safe reinforcement learning scheme for neuro-fuzzy systems (NFS). The proposed Q-value based particle swarm optimization (QPSO) ful...