Cooperative Data Aggregation and Dynamic Resource Allocation for Massive Machine Type Communication

• Published in: IEEE access Vol. 6; pp. 4145 - 4158
• Main Authors: Salam, Tabinda, Rehman, Waheed Ur, Tao, Xiaofeng
• Format: Journal Article
• Language: English
• Published: Piscataway IEEE 01.01.2018; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: 5G Networks; Agglomeration; Algorithms; Cellular communication; Cellular networks; cooperative aggregation; Data aggregation; Data management; Delays; device-to-device communication; Dynamic scheduling; Electronic devices; M2M; machine-to-machine communication; Quality of service; Resource allocation; Resource management; Resource scheduling; Schedules
• ISSN: 2169-3536; 2169-3536
• DOI: 10.1109/ACCESS.2018.2791577

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.