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Minimizing Radio Resource Usage for Machine-to-Machine Communications through Da ta-Centric Clustering

Minimizing Radio Resource Usage for Machine-to-Machine Communications through Da ta-Centric Clustering

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Minimizing Radio Resource Usage for Machine-to-Machine Communications through Da ta-Centric Clustering

While clustered communica tion has been considered as one key technology for wireless sensor networ ks, existing work on cluster for mation predominantly takes a pure g raph-theoretic approach with the goal of optimizing the perfor mance of individual machines. Since the radio resource available for M2M communica tions is typically limited yet the amount of dat a to transpor t is large, such “resource-agnostic” and “data-agnostic” clustering techniques could lead to sub-optimal perfor mance. To address this problem, we propose “data-centric” clustering in a resource-constrained M2M networ k by prioritizing the quality of overall data over the perfor mance of individual machines. We first for mulat e an optimization problem to minimize the amount of radio resource needed for suppor ting two-tier clustered communications. We then par tition the for mulated problem into the inner power control and outercluster for ma tion sub-problems and propose algorithms for solving the problems. While power control can be optimally solved for any given cluster str ucture by the proposed algorithm, cluster for mation is an NP-hard problem. Hence, we propose an anytime, guided, stochastic search algorithm to find a reasonably good cluster str ucture without incur ring prohibitive computation complexity. Compared with baseline approaches, our evalua tion results show that da ta-centric clustering can achieve noticeable perfor mance gain by selecting only impor tant machines and for ming a cluster str ucture tha t can balance the radio resource usage of the two tiers. We therefore motiva te da ta-centric clustering as a promising communication model for resource-constrained M2M networ ks


 


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  • Model: PROJ7486
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This product was added to our catalog on Friday 28 July, 2017.

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