MDLBA-LORA : A MULTIDIMENSIONAL LEARNING-BASED LIGHTWEIGHT ROUTING PROTOCOL FOR SECURE AND EFFICIENT COMMUNICATION IN LORAWAN.

LoRa WAN deployments still suffer from unreliable routing, energy constraints, and other vulnerabilities to DoS, injection, jamming, and Man-in-the-Middle attacks. Against this backdrop, this work proposes an intelligent and secure routing framework called MDLBA-LoRa (Multidimensional Learning Based Algorithm for LoRa), which is developed upon lightweight fuzzy logic, a BRNN-inspired anomaly detector, cluster-based priority scheduling, and symmetric encryption with AES-128. MDLBA-LoRa incorporates dynamic reconfiguration through continuous fuzzy trust updates, real-time attacker list management, priority-based queue reordering, and adaptive routing that avoids malicious or low-trust nodes based on evolving network conditions. In this paper, the system is implemented in NS-2, utilizing an LoRa -specific agent that emulates sensor nodes, gateways, and network-server operations. Neighbor trust is evaluated based on real-time metrics such as RSSI, queue length, bandwidth usage, and processing delay using rule-based fuzzy scoring. In this regard, a bidirectional temporal evaluator classifies the traffic as either legitimate or malicious. On the other hand, online clustering identifies high-priority packets for immediate encrypted forwarding, buffering normal traffic for scheduled transmission. Duty-cycle emulation and priority-aware queueing are implemented to ensure realistic energy and delay behavior. Through experimental evaluation against multiple attack scenarios, it significantly outperforms the baseline DLBA, RLLoRa, and DLBA-LoRa frameworks. Packet delivery ratio reaches approximately 87.84%, throughput reaches up to 84 kbps, delay goes down to about 1.28 seconds, and energy consumption reduces to 0.083 J. The accuracy of intrusion detection is over 99.06%. Strong precision, recall, and F1 values are demonstrated. MDLBA-LoRa effectively ensures secure, reliable, and energy-efficient communication for large-scale IoT networks.