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1. CONTACT: PRAVEEN KUMAR. L (, +91 – 9791938249)
MAIL ID: sunsid1989@gmail.com, praveen@nexgenproject.com
Web: www.nexgenproject.com, www.finalyear-ieeeprojects.com
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 graphtheoretic approach with the goal of
optimizing the performance of individual machines. Since the radio resource
available for M2M communications 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. We first formulate an optimization problem to minimize the
amount of radio resource needed for supporting two-tier clustered
communications. We then partition the formulated problem into the inner
power control and outer cluster formation sub-problems and propose
algorithms for solving the problems. 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

2. CONTACT: PRAVEEN KUMAR. L (, +91 – 9791938249)
MAIL ID: sunsid1989@gmail.com, praveen@nexgenproject.com
Web: www.nexgenproject.com, www.finalyear-ieeeprojects.com
baseline approaches, our evaluation results show that data-centric clustering
can achieve noticeable performance gain by selecting only important
machines and forming a cluster structure that can balance the radio resource
usage of the two tiers. We therefore motivate data-centric clustering as a
promising communication model for resource-constrained M2M networks.
CONCLUSIONS
In this work, we have presented data-centric clustering for data gathering in
resource-constrained M2M networks. We have formulated an MINLP problem
for forming a two-tier cluster structure with the objective of minimizing the
total radio resource usage under the fidelity requirement for data gathering.
The formulated problem involves joint optimization of cluster formation and
power control, and hence we have divided it into two sub-problems and then
proposed two algorithms involving iterative power update and cluster
structure search to solve the two sub-problems respectively. While the inner
power control sub-problem can be solved optimally for any given cluster
structure, the outer cluster formation sub-problem is NP-hard. Hence, we have
proposed an anytime, guided, stochastic search algorithm to find a reasonably
good cluster structure without incurring prohibitive computation complexity.
Evaluation results against no clustering and baseline clustering approaches
have shown that datacentric clustering is effective in terms of radio resource
reduction and transmission energy savings. The promising results motivate
further investigation on protocol implementation for data-centric clustering in
M2M wireless networks.

3. CONTACT: PRAVEEN KUMAR. L (, +91 – 9791938249)
MAIL ID: sunsid1989@gmail.com, praveen@nexgenproject.com
Web: www.nexgenproject.com, www.finalyear-ieeeprojects.com
REFERENCES
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4. CONTACT: PRAVEEN KUMAR. L (, +91 – 9791938249)
MAIL ID: sunsid1989@gmail.com, praveen@nexgenproject.com
Web: www.nexgenproject.com, www.finalyear-ieeeprojects.com
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