MINIMIZING RADIO RESOURCE USAGE FOR MACHINE-TO-MACHINE COMMUNICATIONS THROUGH...Nexgen Technology
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NEXGEN TECHNOLOGY provides total software solutions to its customers. Apsys works closely with the customers to identify their business processes for computerization and help them implement state-of-the-art solutions. By identifying and enhancing their processes through information technology solutions. NEXGEN TECHNOLOGY help it customers optimally use their resources.
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The incredible rising of online networks show that these networks are complex and involving massive data.Giving a very strong interest to set of techniques developed for mining these networks. The clique problem is a well known NP-Hard problem in graph mining. One of the fundamental applications for it is the community detection. It helps to understand and model the network structure which has been a fundamental problem in several fields. In literature, the exponentially increasing computation time of this problem make the quality of these solutions is limited and infeasible for massive graphs. Furthermore, most of the proposed approaches are able to detect only disjoint communities. In this paper, we present a new clique based approach for fast and efficient overlapping
community detection. The work overcomes the short falls of clique percolation method (CPM), one of most popular and commonly used methods in this area. The shortfalls occur due to brute force algorithm for enumerating maximal cliques and also the missing out many vertices thatleads to poor node coverage. The proposed work overcome these shortfalls producing NMC method for enumerating maximal cliques then detects overlapping communities using three different community scales based on three different depth levels to assure high nodes coverage and detects the largest communities. The clustering coefficient and cluster density are used to measure the quality. The work also provide experimental results on benchmark real world network to
demonstrate the efficiency and compare the new proposed algorithm with CPM method, The proposed algorithm is able to quickly discover the maximal cliques and detects overlapping community with interesting remarks and findings.
MINIMIZING RADIO RESOURCE USAGE FOR MACHINE-TO-MACHINE COMMUNICATIONS THROUGH...Nexgen Technology
TO GET THIS PROJECT COMPLETE SOURCE ON SUPPORT WITH EXECUTION PLEASE CALL BELOW CONTACT DETAILS
MOBILE: 9791938249, 0413-2211159, WEB: WWW.NEXGENPROJECT.COM,WWW.FINALYEAR-IEEEPROJECTS.COM, EMAIL:Praveen@nexgenproject.com
NEXGEN TECHNOLOGY provides total software solutions to its customers. Apsys works closely with the customers to identify their business processes for computerization and help them implement state-of-the-art solutions. By identifying and enhancing their processes through information technology solutions. NEXGEN TECHNOLOGY help it customers optimally use their resources.
A Proposed Algorithm to Detect the Largest Community Based On Depth LevelEswar Publications
The incredible rising of online networks show that these networks are complex and involving massive data.Giving a very strong interest to set of techniques developed for mining these networks. The clique problem is a well known NP-Hard problem in graph mining. One of the fundamental applications for it is the community detection. It helps to understand and model the network structure which has been a fundamental problem in several fields. In literature, the exponentially increasing computation time of this problem make the quality of these solutions is limited and infeasible for massive graphs. Furthermore, most of the proposed approaches are able to detect only disjoint communities. In this paper, we present a new clique based approach for fast and efficient overlapping
community detection. The work overcomes the short falls of clique percolation method (CPM), one of most popular and commonly used methods in this area. The shortfalls occur due to brute force algorithm for enumerating maximal cliques and also the missing out many vertices thatleads to poor node coverage. The proposed work overcome these shortfalls producing NMC method for enumerating maximal cliques then detects overlapping communities using three different community scales based on three different depth levels to assure high nodes coverage and detects the largest communities. The clustering coefficient and cluster density are used to measure the quality. The work also provide experimental results on benchmark real world network to
demonstrate the efficiency and compare the new proposed algorithm with CPM method, The proposed algorithm is able to quickly discover the maximal cliques and detects overlapping community with interesting remarks and findings.
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Minimizing Radio Resource Usage for Machine-to-Machine Communications through
Data-Centric Clustering
ABSTRACT
While clustered communication has been considered as one key technology for wireless
sensor networks, existing work on cluster formation predominantly takes a pure graph theoretic
approach with the goal of optimizing the performance of individual machines. Since the radio
resource available for M2M communications. the proposed algorithm, cluster formation is an
NP-hard problem. Hence, we propose an anytime, guided, stochastic search algorithm to find a
reasonably good cluster structure without incurring prohibitive computation complexity.
EXISTING SYSTEM
Work on cluster formation predominantly takes a pure graph theoretic approach with the
goal of optimizing the performance of individual machines. Since the radio resource available for
M2Mcommunications is typically limited yet the amount of data to transport is large, such
“resource-agnostic” and “data-agnostic” clustering techniques could lead to sub-optimal
performance. To address this problem, we propose “data-centric” clustering in a resource-
constrained M2M network by prioritizing the quality of overall data over the performance of
individual machines.
DISADVANTAGES
1. It has been envisioned that a huge amount of machines will be installed and inter-
connected in the near future to facilitate better living experiences for human beings
through various M2M (machine-to-machine) applications such as home automation,
neighborhood surveillance, intelligent transportation, and smart energy.
2. Different from conventional wireless sensor networks (WSNs), in many of these M2M
applications machines are not necessarily limited in the form factor.
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PROPOSED SYSTEM
While power control can be optimally solved for any given cluster structure by the
proposed algorithm, cluster formation is an NP-hard problem. Hence, we propose an anytime,
guided, stochastic search algorithm to find a reasonably good cluster structure without incurring
prohibitive computation complexity. Compared with baseline approaches, our evaluation results
show that data-centric clustering can achieve noticeable performance gain by select in only
important machines and forming a cluster structure that can balance the radio resource usage of
the two tiers. We there for motivate data-centric clustering as a promising communication model
for resource-constrained M2M networks.
ADVANTAGES
1. densely distributed machines with high aggregate data rate, one promising approach for
overcoming the problem of radio resource scarcity is to leverage spatial reuse by
grouping machines into clusters.
2. The random access channel (RACH) when a large number of machines need to access
the network in a short burst. While these endeavors can potentially alleviate the
performance bottleneck by exploiting the delay tolerant nature of the target M2M
applications, the proposed solutions still fall short for many M2M applications involving
a considerable volume of real-time data to transport .
MODULES
1. problem formulation
2. performance evaluation
3. cluster formation sub-problem
ARCHITECTURE
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SYSTEM CONFIGURATION
HARDWARE CONFIGURATION
Processor - Pentium –IV
Speed - 1.1 Ghz
RAM - 256 MB(min)
Hard Disk - 20 GB
Key Board - Standard Windows Keyboard
Mouse - Two or Three Button Mouse
Monitor - SVGA
SOFTWARE CONFIGURATION
Operating System - Windows Family