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Cooperative Data Aggregation and Dynamic Resource Allocation for Massive Machine Type Communication
Author(s) -
Tabinda Salam,
Waheed Ur Rehman,
Xiaofeng Tao
Publication year - 2018
Publication title -
ieee access
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.587
H-Index - 127
ISSN - 2169-3536
DOI - 10.1109/access.2018.2791577
Subject(s) - aerospace , bioengineering , communication, networking and broadcast technologies , components, circuits, devices and systems , computing and processing , engineered materials, dielectrics and plasmas , engineering profession , fields, waves and electromagnetics , general topics for engineers , geoscience , nuclear engineering , photonics and electrooptics , power, energy and industry applications , robotics and control systems , signal processing and analysis , transportation
The accommodation of massive machine-type communication (mMTC) in cellular networks brings up serious technical challenges due to concurrent massive access of MTC devices. These challenges may further be aggravated by the presence of delay tolerant and intolerant services in an MTC network. This paper proposes a cooperative data aggregation (CDA) scheme by employing fixed data aggregator (FDA) and multiple mobile data aggregators (MDAs) to cater MTC devices having variable quality of service (QoS) requirements. In this vein, a distributed MDA selection algorithm is also proposed to designate appropriate user equipment as aggregator. The proposed CDA scheme effectively caters the massive access and provides ubiquitous availability of the aggregating devices in the MTC network. In addition, the limited channel resources impel an FDA to schedule resources besides data aggregation. Therefore, a resource allocation scheme is also proposed to dynamically allocate channels to the MTC devices subject to their QoS requirements. The proposed resource scheduling scheme ensures that transmission requests from delay intolerant MTC devices are contented on priority basis. The proposed CDA and dynamic resource scheduling schemes are analyzed and compared with the existing data aggregation and resource scheduling schemes, respectively. The numerical results corroborate that our proposed CDA scheme in conjunction with dynamic resource allocation improves the outage probability, energy efficiency, and system capacity by 30%, 25%, and 44%, respectively, as compared to the single FDA scheme.

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