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Information Technology Journal

Year: 2014  |  Volume: 13  |  Issue: 13  |  Page No.: 2196 - 2203

Grouping Aggregation and On-Demand Parsing Mechanism for Congestion Mitigation in 3GPP Machine-to-Machine Communications

Shimin Yang, Xiangming Wen, Wei Zheng and Zhaoming Lu


Challenges associated with the deployment of Machine Type Communication (MTC) over 3GPP cellular network focus on the overload control and congestion mitigation introduced by potential numbers of ‘smart devices’. Bulk processing mechanism, common to handling a group of similar control sessions, is a promising solution, however, there are still little work exploring the protocol-layer procedure optimization for the signaling sessions of group-based MTC application. This study mainly examines the in-building smart monitoring scenario and puts forward some novel schemes that efficiently aggregate and parse a bulk of Session Initial Protocol (SIP) sessions simultaneously from numerous MTC machines. By investigating the unique features of the group-based MTC applications and the media description fields of the Session Discovery Protocol (SDP), this study generates some new extension attributes which are used as the group identifier for a bulk of MTC devices with common behaviors and then illustrates the aggregate procedure for SIP bulk sessions to control overload and then, in addition, carefully presents a Group-based On-demand Parsing Mechanism (GOPM) to efficiently process concurrent SIP sessions in downlink direction. Theory analysis and performance evaluation both prove that the MTC signaling traffics can be controlled when the network is overloaded and therefore the network congestion can be reduced effectively.

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