A Dynamic Rate Adaptation Scheme for M2M Communications

The number of Machine-to-Machine (M2M) devices has continued to grow at an accelerated rate. Without thoughtful and efficient resource management, M2M communications will be asymmetrically handicapped by service rate scarcity as more devices are continually added. To address these issues, in this paper, we propose a dynamic rate adaptation (DRA) scheme to obtain an optimized service rate distribution among a mixture of time-driven and event-driven M2M applications. DRA introduces real time monitoring of M2M traffic arrival rate, building on which service rate distribution between M2M applications can be adjusted momentarily, by using the mean value theorem of integrals (MVTI) and generalized processor sharing (GPS). We have validated the effectiveness of our proposed DRA scheme and our experimental results demonstrate that DRA can significantly improve M2M communications performance with respect to throughput and delay.

[1]  Lusheng Ji,et al.  A first look at cellular machine-to-machine traffic: large scale measurement and characterization , 2012, SIGMETRICS '12.

[2]  Yuan He,et al.  Beta/M/1 Model for Machine Type Communication , 2013, IEEE Communications Letters.

[3]  Honggang Wang,et al.  Quality-Driven Energy-Neutralized Power and Relay Selection for Smart Grid Wireless Multimedia Sensor Based IoTs , 2013, IEEE Sensors Journal.

[4]  Tarik Taleb,et al.  Machine type communications in 3GPP networks: potential, challenges, and solutions , 2012, IEEE Communications Magazine.

[5]  Kwang-Cheng Chen,et al.  Toward ubiquitous massive accesses in 3GPP machine-to-machine communications , 2011, IEEE Communications Magazine.

[6]  Richard J. La,et al.  FASA: Accelerated S-ALOHA Using Access History for Event-Driven M2M Communications , 2013, IEEE/ACM Transactions on Networking.

[7]  Jesus Alonso-Zarate,et al.  Is the Random Access Channel of LTE and LTE-A Suitable for M2M Communications? A Survey of Alternatives , 2014, IEEE Communications Surveys & Tutorials.

[8]  Yunjian Jia,et al.  Statistical Description and Analysis of the Concurrent Data Transmission from Massive MTC Devices , 2014 .

[9]  Hsiao-Hwa Chen,et al.  M2M Communications in 3GPP LTE/LTE-A Networks: Architectures, Service Requirements, Challenges, and Applications , 2015, IEEE Communications Surveys & Tutorials.

[10]  Olivier Hersent,et al.  M2M Communications: A Systems Approach , 2012 .

[11]  Xinyu Yang,et al.  A Real-Time En-Route Route Guidance Decision Scheme for Transportation-Based Cyberphysical Systems , 2017, IEEE Transactions on Vehicular Technology.

[12]  Chonggang Wang,et al.  Priority-Based Traffic Scheduling and Utility Optimization for Cognitive Radio Communication Infrastructure-Based Smart Grid , 2013, IEEE Transactions on Smart Grid.

[13]  Xinyu Yang,et al.  A Survey on Internet of Things: Architecture, Enabling Technologies, Security and Privacy, and Applications , 2017, IEEE Internet of Things Journal.

[14]  Abhay Parekh,et al.  A generalized processor sharing approach to flow control in integrated services networks-the multiple node case , 1993, IEEE INFOCOM '93 The Conference on Computer Communications, Proceedings.

[15]  Nada Golmie,et al.  Toward Integrating Distributed Energy Resources and Storage Devices in Smart Grid , 2017, IEEE Internet of Things Journal.