Towards Reliable IEEE 802.15.4g SUN with Re-transmission Shaping and Adaptive Modulation Selection

In this paper, we propose and evaluate two mechanisms aimed at improving the communication reliability of IEEE 802.15.4g SUN (Smart Utility Networks) in industrial scenarios: RTS (Re-Transmission Shaping), which uses acknowledgements to track channel conditions and dynamically adapt the number of re-transmissions per packet, and AMS (Adaptive Modulation Selection), which makes use of reinforcement learning based on MAB (Multi-Armed Bandits) to choose the modulation that provides the best reliability for each packet re-transmission. The evaluation of both mechanisms is performed through computer simulations using a dataset obtained from a real-world deployment and two widely used metrics, the PDR (Packet Delivery Ratio) and the RNP (Required Number of Packet transmissions). The PDR measures the ratio between received and transmitted packets, whereas the RNP is the number of packet repetitions before a successful transmission. The results show that both mechanisms allow to increase the communication reliability while not jeopardizing the battery life-time constraints of end devices. For example, when three re-transmissions per packet are allowed, the PDR reaches 98/96% with a RNP of 2.03/1.32 using RTS and AMS, respectively. Additionally, the combination of both proposed mechanisms allows to reach a 99% PDR with a RNP of 1.7, making IEEE 802.15.4g SUN compliant with the stringent data delivery requirements of industrial applications.

[1]  Ankur Mehta,et al.  Reliability through frequency diversity: why channel hopping makes sense , 2009, PE-WASUN '09.

[2]  Xavier Vilajosana,et al.  Improving Link Reliability of IEEE 802.15.4g SUN with Re-Transmission Shaping , 2020, PE-WASUN.

[3]  Thomas Watteyne,et al.  Evaluation of IEEE802.15.4g for Environmental Observations , 2018, Sensors.

[4]  Joshua Reid,et al.  Multi-Armed Bandit Strategies for Non-Stationary Reward Distributions and Delayed Feedback Processes , 2019, ArXiv.

[5]  Xavier Vilajosana,et al.  Evaluating IEEE 802.15.4g SUN for Dependable Low-Power Wireless Communications In Industrial Scenarios , 2020 .

[6]  Ankur Mehta,et al.  Mitigating Multipath Fading through Channel Hopping in Wireless Sensor Networks , 2010, 2010 IEEE International Conference on Communications.

[7]  Paul Mühlethaler,et al.  Overview of IEEE802.15.4g OFDM and its applicability to smart building applications , 2018, 2018 Wireless Days (WD).

[8]  Paul Mühlethaler,et al.  Why Channel Hopping Makes Sense, even with IEEE802.15.4 OFDM at 2.4 GHz , 2018, 2018 Global Internet of Things Summit (GIoTS).

[9]  Aamir Mahmood,et al.  Machine Learning-Aided Classification Of LoS/NLoS Radio Links In Industrial IoT , 2020, 2020 16th IEEE International Conference on Factory Communication Systems (WFCS).

[10]  MottolaLuca,et al.  Radio link quality estimation in wireless sensor networks , 2012 .

[11]  Pere Tuset,et al.  A Dataset to Evaluate IEEE 802.15.4g SUN for Dependable Low-Power Wireless Communications in Industrial Scenarios , 2020, Data.

[12]  Aurélien Garivier,et al.  On Upper-Confidence Bound Policies for Non-Stationary Bandit Problems , 2008, 0805.3415.

[13]  Marcelo Sampaio de Alencar,et al.  Cooperative Modulation Diversity Applied to Heterogeneous Wireless Sensor Networks , 2014, 2014 IEEE International Conference on Distributed Computing in Sensor Systems.

[14]  Jeroen Famaey,et al.  Towards Slot Bonding for Adaptive MCS in IEEE 802.15.4e TSCH Networks , 2020, ICC 2020 - 2020 IEEE International Conference on Communications (ICC).

[15]  U.B. Desai,et al.  Reconfigurable dual mode IEEE 802.15.4 digital baseband receiver for diverse IoT applications , 2016, 2016 IEEE 3rd World Forum on Internet of Things (WF-IoT).

[16]  Emanuele Goldoni,et al.  A Wiener-Based RSSI Localization Algorithm Exploiting Modulation Diversity in LoRa Networks , 2019, IEEE Sensors Journal.

[17]  Mikio Hasegawa,et al.  Performance Evaluation of Machine Learning Based Channel Selection Algorithm Implemented on IoT Sensor Devices in Coexisting IoT Networks , 2020, 2020 IEEE 17th Annual Consumer Communications & Networking Conference (CCNC).

[18]  Pere Tuset,et al.  Improving Link Reliability of IEEE 802.15.4g SUN with Adaptive Modulation Diversity , 2020, 2020 IEEE 31st Annual International Symposium on Personal, Indoor and Mobile Radio Communications.

[19]  Bhaskar Krishnamachari,et al.  MABO-TSCH: Multihop and blacklist-based optimized time synchronized channel hopping , 2018, Trans. Emerg. Telecommun. Technol..

[20]  Carlo Fischione,et al.  Adaptive IEEE 802.15.4 protocol for energy efficient, reliable and timely communications , 2010, IPSN '10.

[21]  Eli De Poorter,et al.  Adaptive multi-PHY IEEE802.15.4 TSCH in sub-GHz industrial wireless networks , 2021, Ad Hoc Networks.

[22]  Thomas Watteyne,et al.  OpenMote+: a Range-Agile Multi-Radio Mote , 2016, EWSN.

[23]  Marcelo S. Alencar,et al.  Adaptive and Beacon-based multi-channel protocol for Industrial Wireless Sensor Networks , 2019, J. Netw. Comput. Appl..

[24]  Thomas Watteyne,et al.  No Free Lunch—Characterizing the Performance of 6TiSCH When Using Different Physical Layers , 2020, Sensors.

[25]  Raphaël Féraud,et al.  Reinforcement Learning Techniques for Optimized Channel Hopping in IEEE 802.15.4-TSCH Networks , 2018, MSWIM '18.

[26]  Masoud Sabaei,et al.  IEEE 802.15.4.e TSCH-Based Scheduling for Throughput Optimization: A Combinatorial Multi-Armed Bandit Approach , 2019, IEEE Sensors Journal.

[27]  David E. Culler,et al.  Transmission of IPv6 Packets over IEEE 802.15.4 Networks , 2007, RFC.

[28]  Anis Koubaa,et al.  Radio link quality estimation in wireless sensor networks , 2012, ACM Trans. Sens. Networks.

[29]  Pere Tuset,et al.  Reliability through modulation diversity: can combining multiple IEEE 802.15.4-2015 SUN modulations improve PDR? , 2020, 2020 IEEE Symposium on Computers and Communications (ISCC).