Document Type
Event
Start Date
28-4-2022 5:00 PM
Description
The Last decade saw new advances in LoRaWAN for IoT devices. Recent research in this field is as made it a game-changer for the IoT world. Lora, owing to its ability to establish a long-range communication using low power, has proved upper hand over its counterparts such as Wifi, Bluetooth, or cellular network. LoRa uses much lower bandwidth and as a result, network and power demands reduce. LoRa is suitable for short and periodic communications. But due to lower bandwidth and consequently lower transmission rates and low data volumes, it is not suitable for real-time communications. Frequency-hopping spread spectrum(FHSS) helps to rapidly switch frequencies and occupy larger spectral bands and increase overall data rate and throughput. It is observed that LoRaWAN defines 11 channels but uses only 3 channels frequently, thus keeping the other 8 channels underutilized. If all channels were utilized, the transmission rate would increase multifold. We have proposed a Dynamic approach, Reinforcement Learning-based Frequency Hopping Spread Spectrum (DRL-FHSS), a frequency hopping generation algorithm, to widen the occupancy of the spectral band. Our algorithm keeps a check on the frequency of used channels, overutilized and underutilized frequencies, and generates a strategy that allocates time slots and frequencies for all registered IoT devices in the network. DRL-FHSS learns its strategy based on the previous bandwidth utilization ratio. We introduce an edge-enabled LoRaWAN architecture that delegates the task of frequency hopping strategy generation to the edge server. Further, edge servers also notify transmitters and receivers in the network about the frequency hopping sequence. This also reduces the collision in the network since each transmitter is aware of busy frequencies in the network. Thus DRL-FHSS helps to improve throughput by increasing the transmission rate and lowering collision.
Included in
GR-190 - DRL-FHSS: Dynamic Reinforcement Learning based Frequency Hopping Spread Spectrum Algorithm for Maximizing Data Rate and Minimizing Collision in Edge-enabled LoRaWAN
The Last decade saw new advances in LoRaWAN for IoT devices. Recent research in this field is as made it a game-changer for the IoT world. Lora, owing to its ability to establish a long-range communication using low power, has proved upper hand over its counterparts such as Wifi, Bluetooth, or cellular network. LoRa uses much lower bandwidth and as a result, network and power demands reduce. LoRa is suitable for short and periodic communications. But due to lower bandwidth and consequently lower transmission rates and low data volumes, it is not suitable for real-time communications. Frequency-hopping spread spectrum(FHSS) helps to rapidly switch frequencies and occupy larger spectral bands and increase overall data rate and throughput. It is observed that LoRaWAN defines 11 channels but uses only 3 channels frequently, thus keeping the other 8 channels underutilized. If all channels were utilized, the transmission rate would increase multifold. We have proposed a Dynamic approach, Reinforcement Learning-based Frequency Hopping Spread Spectrum (DRL-FHSS), a frequency hopping generation algorithm, to widen the occupancy of the spectral band. Our algorithm keeps a check on the frequency of used channels, overutilized and underutilized frequencies, and generates a strategy that allocates time slots and frequencies for all registered IoT devices in the network. DRL-FHSS learns its strategy based on the previous bandwidth utilization ratio. We introduce an edge-enabled LoRaWAN architecture that delegates the task of frequency hopping strategy generation to the edge server. Further, edge servers also notify transmitters and receivers in the network about the frequency hopping sequence. This also reduces the collision in the network since each transmitter is aware of busy frequencies in the network. Thus DRL-FHSS helps to improve throughput by increasing the transmission rate and lowering collision.