Enhanced LoRaWAN Adaptive Data Rate for Mobile Internet of Things Devices

A long-range wide area network (LoRaWAN) is one of the leading communication technologies for Internet of Things (IoT) applications. In order to fulfill the IoT-enabled application requirements, LoRaWAN employs an adaptive data rate (ADR) mechanism at both the end device (ED) and the network server (NS). NS-managed ADR aims to offer a reliable and battery-efficient resource to EDs by managing the spreading factor (SF) and transmit power (TP). However, such management is severely affected by the lack of agility in adapting to the variable channel conditions. Thus, several hours or even days may be required to converge at a level of stable and energy-efficient communication. Therefore, we propose two NS-managed ADRs, a Gaussian filter-based ADR (G-ADR) and an exponential moving average-based ADR (EMA-ADR). Both of the proposed schemes operate as a low-pass filter to resist rapid changes in the signal-to-noise ratio of received packets at the NS. The proposed methods aim to allocate the best SF and TP to both static and mobile EDs by seeking to reduce the convergence period in the confirmed mode of LoRaWAN. Based on the simulation results, we show that the G-ADR and EMA-ADR schemes reduce the convergence period in a static scenario by 16% and 68%, and in a mobility scenario by 17% and 81%, respectively, as compared to typical ADR. Moreover, we show that the proposed schemes are successful in reducing the energy consumption and enhancing the packet success ratio.

[1]  Jae-Young Pyun,et al.  Mobility-Aware Resource Assignment to IoT Applications in Long-Range Wide Area Networks , 2020, IEEE Access.

[2]  Orestis Georgiou,et al.  Low Power Wide Area Network Analysis: Can LoRa Scale? , 2016, IEEE Wireless Communications Letters.

[3]  Mario Di Francesco,et al.  Adaptive configuration of lora networks for dense IoT deployments , 2018, NOMS 2018 - 2018 IEEE/IFIP Network Operations and Management Symposium.

[4]  Stephen Brown,et al.  Analysis and Enhancement of the LoRaWAN Adaptive Data Rate Scheme , 2020, IEEE Internet of Things Journal.

[5]  Chong-kwon Kim,et al.  EARN: Enhanced ADR With Coding Rate Adaptation in LoRaWAN , 2020, IEEE Internet of Things Journal.

[6]  Leïla Azouz Saïdane,et al.  LoRaWAN Analysis Under Unsaturated Traffic, Orthogonal and Non-Orthogonal Spreading Factor Conditions , 2018, 2018 IEEE 17th International Symposium on Network Computing and Applications (NCA).

[7]  Ronan Farrell,et al.  Modeling the Energy Consumption of LoRaWAN in ns-3 Based on Real World Measurements , 2018, 2018 Global Information Infrastructure and Networking Symposium (GIIS).

[8]  Jianli Pan,et al.  Future Edge Cloud and Edge Computing for Internet of Things Applications , 2018, IEEE Internet of Things Journal.

[9]  Proceedings of the 2019 on Wireless of the Students, by the Students, and for the Students Workshop , 2019 .

[10]  Hiroyuki Morikawa,et al.  Evaluation of LoRa receiver performance under co-technology interference , 2018, 2018 15th IEEE Annual Consumer Communications & Networking Conference (CCNC).

[11]  Chiara Buratti,et al.  A Novel Collision-Aware Adaptive Data Rate Algorithm for LoRaWAN Networks , 2021, IEEE Internet of Things Journal.

[12]  Gerhard P. Hancke,et al.  A Survey on the Viability of Confirmed Traffic in a LoRaWAN , 2020, IEEE Access.

[13]  Yasir Saleem,et al.  Network Simulator NS-2 , 2015 .

[14]  Andrea Zanella,et al.  Confirmed traffic in LoRaWAN: Pitfalls and countermeasures , 2018, 2018 17th Annual Mediterranean Ad Hoc Networking Workshop (Med-Hoc-Net).

[15]  Filip Lemic,et al.  Empirical Analysis of LoRaWAN Adaptive Data Rate for Mobile Internet of Things Applications , 2019, S3@MobiCom.

[16]  H. Tenhunen,et al.  A Survey on LoRa for IoT: Integrating Edge Computing , 2019, 2019 Fourth International Conference on Fog and Mobile Edge Computing (FMEC).

[17]  Vojtech Hauser,et al.  Proposal of Adaptive Data Rate Algorithm for LoRaWAN-Based Infrastructure , 2017, 2017 IEEE 5th International Conference on Future Internet of Things and Cloud (FiCloud).

[18]  Andrea Zanella,et al.  A Thorough Study of LoRaWAN Performance Under Different Parameter Settings , 2019, IEEE Internet of Things Journal.

[19]  Jae-Young Pyun,et al.  Improving Indoor Fingerprinting Positioning With Affinity Propagation Clustering and Weighted Centroid Fingerprint , 2019, IEEE Access.

[20]  Martin Heusse,et al.  Optimal SF Allocation in LoRaWAN Considering Physical Capture and Imperfect Orthogonality , 2019, 2019 IEEE Global Communications Conference (GLOBECOM).

[21]  Yeqiong Song,et al.  Enhanced ADR for LoRaWAN networks with mobility , 2019, 2019 15th International Wireless Communications & Mobile Computing Conference (IWCMC).

[22]  David Plets,et al.  Combining TDoA and AoA with a particle filter in an outdoor LoRaWAN network , 2020, 2020 IEEE/ION Position, Location and Navigation Symposium (PLANS).

[23]  Jae-Young Pyun,et al.  Scalability of LoRaWAN in an Urban Environment: A Simulation Study , 2019, 2019 Eleventh International Conference on Ubiquitous and Future Networks (ICUFN).

[24]  Jae-Young Pyun,et al.  Resource Allocation to Massive Internet of Things in LoRaWANs , 2020, Sensors.

[25]  Megumi Kaneko,et al.  LoRa Throughput Analysis With Imperfect Spreading Factor Orthogonality , 2018, IEEE Wireless Communications Letters.

[26]  Rachel Kufakunesu,et al.  A Survey on Adaptive Data Rate Optimization in LoRaWAN: Recent Solutions and Major Challenges , 2020, Sensors.

[27]  Luc Martens,et al.  TDoA-Based Outdoor Positioning with Tracking Algorithm in a Public LoRa Network , 2018, Wirel. Commun. Mob. Comput..

[28]  Chiara Buratti,et al.  Intent‐based service management for heterogeneous software‐defined infrastructure domains , 2018, Int. J. Netw. Manag..

[29]  Davide Magrin,et al.  Performance evaluation of LoRa networks in a smart city scenario , 2017, 2017 IEEE International Conference on Communications (ICC).

[30]  Jae-Young Pyun,et al.  Practical Fingerprinting Localization for Indoor Positioning System by Using Beacons , 2017, J. Sensors.

[31]  Usman Raza,et al.  How Agile is the Adaptive Data Rate Mechanism of LoRaWAN? , 2018, 2018 IEEE Global Communications Conference (GLOBECOM).

[32]  Konstantin Mikhaylov,et al.  Analysis of Capacity and Scalability of the LoRa Low Power Wide Area Network Technology , 2016 .

[33]  Ingrid Moerman,et al.  A Survey of LoRaWAN for IoT: From Technology to Application , 2018, Sensors.

[34]  Jong Hyuk Park,et al.  Adaptive data rate control in low power wide area networks for long range IoT services , 2017, J. Comput. Sci..

[35]  Ilenia Tinnirello,et al.  Impact of Spreading Factor Imperfect Orthogonality in LoRa Communications , 2017, TIWDC.

[36]  Utz Roedig,et al.  Do LoRa Low-Power Wide-Area Networks Scale? , 2016, MSWiM.