ENERGY-EFFICIENT AND QOS-OPTIMIZED IOT ROUTING USING PROPOSED QEER-MFO HYBRID ALGORITHM

The Internet of Things' (IoT) rapid expansion has transformed connectivity in a number of industries, including smart cities, healthcare, transportation, and agriculture. However, because of their dynamic topology, device heterogeneity, and limited power resources, IoT networks continue to confront difficulties with energy management, routing efficiency, and Quality of Service (QoS). Conventional routing protocols sometimes don't adjust to the changing needs of IoT environments because they were mostly created for static or homogeneous networks. In order to overcome these constraints, this study suggests a brand-new hybrid routing architecture called QoS and Energy-Efficient Routing utilizing Moth-LSTM Optimization (QEER-MFO), which combines Long Short-Term Memory (LSTM) networks with the Moth-Flame Optimization (MFO) method. While the LSTM model forecasts network characteristics like traffic congestion and node energy depletion to enable proactive routing decisions, the MFO component makes sure that the best path is chosen through adaptive exploration and exploitation, reducing energy consumption and transmission latency. The Trust-Aware IIoT Routing Dataset is used to assess the suggested method, and the ns3-gym interface is used to simulate it in NS-3 combined with Python. According to experimental data, QEER-MFO outperforms current bio-inspired and conventional routing protocols, resulting in increased packet delivery ratios, decreased latency, and extended network lifetime. For scalable and intelligent IoT ecosystems, this hybrid architecture provides a self-learning, predictive, and energy-efficient routing approach.