CTMC Modeling for M2M/H2H Coexistence in a NB-IoT Adaptive eNodeB

The next generation of mobile systems are expected to support the new promising Machine-to-Machine (M2M) technology carried by the advance of Internet of Things (IoT) devices. In the near future, an exponential growth of the number of M2M devices is expected due to their ubiquity. In normal situations, a limited bandwidth in Narrow Band-Internet of Things (NB-IoT) technology may help in improving IoT requirements effectively. However, in emergency and disastrous moments, M2M expected storms lead inevitably to network saturations. In this manuscript, we propose a novel Adaptive eNodeB (A-eNB), which solves the network overload problem gradually, while keeping Human-to-Human (H2H) traffic not to be affected dreadfully. The network adaptation is provided through a dynamic NB-IoT bandwidth reservation aiming to increase the number of M2M connections accessing NB-IoT network with minimal overload congestion problems. A Continuous-Time Markov Chain (CTMC) is proposed helping the H2H/M2M coexistence to become more approachable especially during disaster scenarios. Our results show that by leasing 18 resource blocks using an A-eNB for NB-IoT traffic can result a completion rate of 98% on M2M traffic throughout emergency scenarios.

[1]  Olga Galinina,et al.  Analyzing Impacts of Coexistence between M2M and H2H Communication on 3GPP LTE System , 2014, WWIC.

[2]  Amitava Ghosh,et al.  NB-IoT system for M2M communication , 2016, 2016 IEEE Wireless Communications and Networking Conference.

[3]  István Z. Kovács,et al.  Coverage and Capacity Analysis of LTE-M and NB-IoT in a Rural Area , 2016, 2016 IEEE 84th Vehicular Technology Conference (VTC-Fall).

[4]  Sung-Min Oh,et al.  An Efficient Small Data Transmission Scheme in the 3GPP NB-IoT System , 2017, IEEE Communications Letters.

[5]  Ali Mansour,et al.  New challenges in wireless and free space optical communications , 2017 .

[6]  Laurence T. Yang,et al.  Energy-Efficient Resource Allocation for D2D Communications Underlaying Cloud-RAN-Based LTE-A Networks , 2016, IEEE Internet of Things Journal.

[7]  Mohamad Najem,et al.  LTE-M adaptive eNodeB for emergency scenarios , 2017, 2017 International Conference on Information and Communication Technology Convergence (ICTC).

[8]  Stefania Sesia,et al.  LTE - The UMTS Long Term Evolution , 2009 .

[9]  Peng Li,et al.  An Adaptive Dropout Deep Computation Model for Industrial IoT Big Data Learning With Crowdsourcing to Cloud Computing , 2019, IEEE Transactions on Industrial Informatics.