Designing Intelligent Energy Efficient Scheduling Algorithm to support Massive IoT Communication in LoRa Networks
Disciplines
Digital Communications and Networking
Abstract (300 words maximum)
We are about to enter a new world with sixth sense ability – “Network as a sensor -6G”. The driving force behind digital sensing abilities is IoT. Due to their capacity to work in high frequency, 6G devices would have voracious energy demand. Hence, there is a growing need to work on green solutions to support the underlying 6G network by making it more energy efficient. Low cost, low energy, and long-range communication capability make LoRa the most adopted and promising network for IoT devices. Since LoRaWAN uses ALOHA for multi-access channels, collision management is essential. Moreover, in massive IoT, collision becomes a concern due to the increased number of devices and their ad hoc transmissions. Due to ALOHA-based transmissions, we see that scalability in LoRaWAN is challenging to achieve. Also, increased collisions and retransmissions eventually drain the batteries of IoT devices. Furthermore, in long-range communication, such as in forests, agriculture, and remote locations, the IoT devices must be powered using a battery. They cannot be attached to an energy grid. Frequently replacing their batteries is complex, motivating us to work on improving energy efficiency in massive IoT. To address Massive IoT collision and gateway load handling issues, we propose an intelligent scheduling algorithm to optimize the energy efficiency of LoRaWAN with cross-layer architecture in massive IoT with star topology. In our solution, we exploit the interdependence and interaction among PHY and MAC layers in an integrated manner. Also, we propose improving the existing multi-access strategy, channel activity detection (CAD), in LoRaWAN to reduce collisions and improve energy efficiency. We have designed a reinforcement learning-based scheduling strategy for the selection of transmission parameters and CAD improvement. We have also designed a LoRaWAN simulator for evaluating CAD-based algorithms.
Academic department under which the project should be listed
CCSE - Computer Science
Primary Investigator (PI) Name
Ahyoung Lee
Designing Intelligent Energy Efficient Scheduling Algorithm to support Massive IoT Communication in LoRa Networks
We are about to enter a new world with sixth sense ability – “Network as a sensor -6G”. The driving force behind digital sensing abilities is IoT. Due to their capacity to work in high frequency, 6G devices would have voracious energy demand. Hence, there is a growing need to work on green solutions to support the underlying 6G network by making it more energy efficient. Low cost, low energy, and long-range communication capability make LoRa the most adopted and promising network for IoT devices. Since LoRaWAN uses ALOHA for multi-access channels, collision management is essential. Moreover, in massive IoT, collision becomes a concern due to the increased number of devices and their ad hoc transmissions. Due to ALOHA-based transmissions, we see that scalability in LoRaWAN is challenging to achieve. Also, increased collisions and retransmissions eventually drain the batteries of IoT devices. Furthermore, in long-range communication, such as in forests, agriculture, and remote locations, the IoT devices must be powered using a battery. They cannot be attached to an energy grid. Frequently replacing their batteries is complex, motivating us to work on improving energy efficiency in massive IoT. To address Massive IoT collision and gateway load handling issues, we propose an intelligent scheduling algorithm to optimize the energy efficiency of LoRaWAN with cross-layer architecture in massive IoT with star topology. In our solution, we exploit the interdependence and interaction among PHY and MAC layers in an integrated manner. Also, we propose improving the existing multi-access strategy, channel activity detection (CAD), in LoRaWAN to reduce collisions and improve energy efficiency. We have designed a reinforcement learning-based scheduling strategy for the selection of transmission parameters and CAD improvement. We have also designed a LoRaWAN simulator for evaluating CAD-based algorithms.