Abstract:
Wireless sensor networks (WSNs) are crucial in supporting automated and real-time monitoring essential for Internet of Things (IoT) and Machine-to-Machine (M2M) communications. However, they face substantial challenges in maintaining optimal Quality of Service (QoS) due to bandwidth and energy constraints. Traditional QoS enhancement strategies often utilize static transmission techniques that fail to adapt to changing network demands and environmental conditions, leading to inefficiencies in energy use, increased latency, and compromised network performance. The proposed Network Adaptive Multimode Transmission (NAMT) algorithm presents an innovative approach designed to optimize QoS by dynamically adjusting transmission modes in real-time based on network conditions. Building on the foundational principles of the Low-Energy Adaptive Clustering Hierarchy (LEACH) protocol, the NAMT algorithm enhances it by incorporating adaptive modulation, real-time network monitoring, and predictive analytics. This allows for intelligent switching between transmission modes, balancing energy efficiency with data transmission quality. The enhanced protocol, termed NAMT-LEACH, specifically addresses the limitations of classical LEACH by integrating multiple parameter-based cluster head selection and topology-adaptive multimode transmission capabilities. By doing so, NAMT-LEACH significantly improves key performance metrics, reducing energy consumption by up to 0.30%, decreasing latency by 0.25%, and enhancing throughput by 0.20%. These improvements enhance both the efficiency and reliability of WSNs compared to conventional methods such as Direct Transmission and Minimum Transmission Energy (DTMTE), Power-Efficient Gathering in Sensor Information Systems (PEGASIS), and Hybrid Energy-Efficient Distributed Clustering (HEED). The NAMT-LEACH protocol thus emerges as a robust solution that adapts to dynamic environmental and network changes, conserves resources, and optimizes performance, potentially revolutionizing the operational dynamics of sensor networks in critical IoT and M2M applications.
