Exploiting Data Correlation for Optimal Resource Allocation in Machine-to-Machine Communications
Date Issued
2012
Date
2012
Author(s)
Chang, Chih-Hua
Abstract
Many applications involving machine-to-machine (M2M) communications are characterized by the large amount of data to transport. To address the ``big data'' problem introduced by these M2M applications, we argue in this thesis that instead of focusing on serving individual machines with better quality, one should focus on solutions that can better serve the data itself. To substantiate, we consider the scenario of data gathering in a wide area with clustering, where the cluster head (CH) collects the correlated data of machines in each cluster and feedback to the data aggregator through wireless links. Since each cluster has limited radio resources, the problem arises as to how the resources can be effectively utilized for supporting such an M2M application. We start with a simple scenario of independent coding and transmission, and propose an approach that takes into consideration ``useful'' information content of the correlated data for resource allocation. The NP-hard problem is solved by separating the resource allocation sub-problem from the node selection problem. Numerical results show that although the aggregate data rate or the number of machines that can be supported is not maximized, the data collected at the CH exhibits significant quality gain for the target M2M scenario. To further explore the performance gain under different coding schemes, we then consider the problems of machines applying distributed source coding and dependent source coding via overhearing to reduce redundancy of correlated data. Simulation results indicate significant benefits of distributed source coding especially for highly correlated data, thus substantiating our argument regarding the move towards data-centric communication for M2M applications.
Subjects
Machine-to-machine communications (M2M)
Data gathering
Data correlation
Resource allocation
Information theory
Distributed source coding
Type
thesis
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