Performance Enhancement of IoT LoRa Network for Mobile Devices based on Improved AMILCC Technique

In Internet of Things (IoT) networks, long-range (LoRa) technology is well-known for its wide communication range, but using it with mobile devices presents unique difficulties. Frequently occurring channel attenuation, which causes packet loss, increased energy consumption, and longer transmission times, is one of these difficulties. An improved Adaptive Mobility-based IoT LoRa Clustering Communication (AMILCC) strategy addressed to solve these problems. AMILCC partitions the network into optimal Spreading Factor (SF) areas by using a 2D random waypoint mobility model. The Hybrid Adaptive Data Rate (HADR) technique, which combines intra-and inter-SF region modifications to improve resource allocation for mobile IoT LoRa devices, is a key component of AMILCC. The simulation findings demonstrate the advantages of AMILCC over conventional stationary techniques, even under low mobility conditions (up to 5 m/s). In particular, AMILCC decreases energy usage by 56.5%, improves packet success ratio (PSR) by 72 %, and decreases end-to-end delay by 45.32%. These outcomes demonstrate how well AMILCC works to enhance efficiency and performance for mobile LoRa applications.