TY  - EJOU
AU  - Akula, Vijaya Krishna 
AU  - Tak, Tan Kuan 
AU  - Kshirsagar, Pravin Ramdas 
AU  - Sonekar, Shrikant Vijayrao 
AU  - Ginnela, Gopichand 

TI  - A Tolerant and Energy Optimization Approach for Internet of Things to Enhance the QoS Using Adaptive Blended Marine Predators Algorithm
T2  - Computers, Materials \& Continua

PY  - 2025
VL  - 83
IS  - 2
SN  - 1546-2226

AB  - The rapid expansion of Internet of Things (IoT) networks has introduced challenges in network management, primarily in maintaining energy efficiency and robust connectivity across an increasing array of devices. This paper introduces the Adaptive Blended Marine Predators Algorithm (AB-MPA), a novel optimization technique designed to enhance Quality of Service (QoS) in IoT systems by dynamically optimizing network configurations for improved energy efficiency and stability. Our results represent significant improvements in network performance metrics such as energy consumption, throughput, and operational stability, indicating that AB-MPA effectively addresses the pressing needs of modern IoT environments. Nodes are initiated with 100 J of stored energy, and energy is consumed at 0.01 J per square meter in each node to emphasize energy-efficient networks. The algorithm also provides sufficient network lifetime extension to a resourceful 7000 cycles for up to 200 nodes with a maximum Packet Delivery Ratio (PDR) of 99% and a robust network throughput of up to 1800 kbps in more compact node configurations. This study proposes a viable solution to a critical problem and opens avenues for further research into scalable network management for diverse applications.
KW  - Internet of things; trust; energy; marine predators algorithm (MPA); differential evolution (DE); nodes; throughput; lifetime

DO  - 10.32604/cmc.2025.061486