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 resourceconstrained 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 interconnected 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.
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.
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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
 Speed
-
 RAM
Pentium –IV
1.1 Ghz
-
256 MB(min)
 Hard Disk
-
20 GB
 Key Board
-
Standard Windows Keyboard
 Mouse
 Monitor
-
Two or Three Button Mouse
SVGA
SOFTWARE CONFIGURATION
 Operating System
-
 Programming Language  Java Version
Windows Family
JAVA
- JDK 1.6 & above.
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