TSCH-Based Scheduling of IEEE 802.15.4e in Coexistence with Interference Network Cluster: A DNN Approach

다국어 초록 (Multilingual Abstract)

In the paper, we propose a TSCH-based scheduling scheme for IEEE 802.15.4e, which is able to perform the scheduling of its own network by avoiding collision from interference network cluster (INC). Firstly, we model a bipartite graph structure for presenting the slot-frame (channel-slot assignment) of TSCH. Then, based on the bipartite graph edge weight, we utilize the Hungarian assignment algorithm to implement a scheduling scheme.
We have employed two features (maximization and minimization) of the Hungarian-based assignment algorithm, which can perform the assignment in terms of minimizing the throughput of INC and maximizing the throughput of own network. Further, in this work, we called the scheme “dual-stage Hungarian-based assignment algorithm”. Furthermore, we also propose deep learning (DL) based deep neural network (DNN) scheme, where the data were generated by the dual-stage Hungarian-based assignment algorithm. The performance of the DNN scheme is evaluated by simulations. The simulation results prove that the proposed DNN scheme provides similar performance to the dual-stage Hungarian-based assignment algorithm while providing a low execution time.

참고문헌 (Reference)

1 T. Watteyne, "eliability through frequency diversity: Why channel hopping makes sense" 116-123, 2014

2 J. Ma, "Social account linking via weighted bipartite graph matching" 31 (31): 1-14, 2018

3 L. P. Sachs, "Performance evaluation" 20 : 1980

4 R. Mennes, "Multi-Agent Deep Learning for Multi-Channel Access in Slotted Wireless Networks" 8 (8): 95032-95045, 2020

5 D. De Guglielmo, "IEEE 802.15.4e: A survey" 88 : 1-24, 2016

6 N. Taheri Javan, "IEEE 802.15.4.e TSCH-Based Scheduling for Throughput Optimization: A Combinatorial Multi-Armed Bandit Approach" 20 (20): 525-537, 2020

7 L. Alkama, "IEEE 802.15.4 historical revolution versions: A survey" 103 (103): 99-131, 2021

8 D. D. G. authorGiuseppe, "From IEEE 802.15.4 to IEEE 802.15.4e: A Step Towards the Internet of Things" 135-152, 2014

9 S. Hammoudi, "External Interference Free Channel Access Strategy Dedicated to TSCH" 350-355, 2018

10 I. Hameed, "Exploiting a deep neural network for efficient transmit power minimization in a wireless powered communication network" 10 (10): 2020

11 L. Liang, "Deep-Learning-Based Wireless Resource Allocation with Application to Vehicular Networks" 108 (108): 341-356, 2020

12 R. Mennes, "Deep Learning-Based Spectrum Prediction Collision Avoidance for Hybrid Wireless Environments" 7 : 45818-45830, 2019

13 I. Hameed, "Deep Learning – Based Energy Beamforming With Transmit Power Control in Wireless Powered Communication Networks" 142795-142803, 2021

14 The Cicso Company, "Cisco Visual Networking Index : Global Mobile Data Traffic Forecast Update , 2016 – 2021 white paper" San Jose 2017

15 J. Ansari, "Channel selection in spectrum agile and cognitive MAC protocols for wireless sensor networks" 83 (83): 83-90, 2010

16 R. Natarajan, "Analysis of coexistence between IEEE 802.15.4, BLE and IEEE 802.11 in the 2.4 GHz ISM band" 6025-6032, 2016

17 M. Ojo, "An efficient centralized scheduling algorithm in IEEE 802.15.4e TSCH networks" 2016

18 S. Kharb, "A survey on network formation and scheduling algorithms for time slotted channel hopping in industrial networks" 126 : 59-87, 2018

19 Q. Tong, "A RFID authentication protocol based on infinite dimension pseudo random number generator" 1 : 292-294, 2009