<![CDATA[One of the key issues of Internet of Things (IoT)-based wireless sensor networks (WSNs) is energy efficiency because battery-powered nodes have to work within a set of severe resource limitations. Conventional protocols do not always work well in nonhomogeneous dynamic environments and this results in poor performance and longevity. The design and validation of an unified framework that intelligently operates network clustering, routing, and resource allocation with the use of machine learning are the research contributions. The framework is represented through a dynamic clustering scheme based on neural networks, routing scheme based on reinforcement learning (Q-learning) and a scheme of Lagrangian optimization-based resource allocation. MATLAB and NS-3 simulations were run with different sizes of networks (100-500 nodes) and traffic. The flow of methodology has formed a scheme whereby the adaptive decision-making was to be made at several levels of the communication stack. The average power savings, increment in network lifetime, and improvement in the percentage packet delivery ratio of the proposed model was 31, 17.9 and 6.2, respectively, over the classical schemes like LEACH and TEEN. Findings were also uniform at various levels of deployments and statistical validation was made to prove it is significant (p < 0.01). The model exhibits better adaptability and performance aspects in both the case of a static network and dynamic network as compared to the recent machine learning-based approaches. To sum up, the paper provides a scaled, smart communication system of IoT networks. Its applications in a real world can be found in smart farming, industrial IoT, and healthcare. The next steps involve the prototype development and integration of the blockchain based node authentication.]]>
