The working of the Internet of Things (IoT) ecosystem indeed depends extensively on the mechanisms of real-time data collection, sharing, and automatic operation. Among these fundamentals, wireless sensor networks (WSNs) are important for maintaining a countenance with their many distributed Sensor Nodes (SNs), which can sense and transmit environmental data wirelessly. Because WSNs possess advantages for remote data collection, they are severely hampered by constraints imposed by the limited energy capacity of SNs; hence, energy-efficient routing is a pertinent challenge. Therefore, in the case of clustering and routing mechanisms, these two play important roles where clustering is performed to reduce energy consumption and prolong the lifetime of the network, while routing refers to the actual paths for transmission of data. Addressing the limitations witnessed in the conventional IoT-based routing of data, this proposal presents an FL-oriented framework that presents a new energy-efficient routing scheme. Such routing is facilitated by the ADDQL model, which creates smart high-speed routing across changing scenarios in WSNs. The proposed ADDQL-IRHO model has been compared to other existing state-of-the-art algorithms according to multiple performance metrics such as energy consumption, communication delay, temporal complexity, data sum rate, message overhead, and scalability, with extensive experimental evaluation reporting superior performance. This also substantiates the applicability and competitiveness of the framework in variable-serviced IoT-oriented WSNs for next-gen intelligent routing solutions.

In cloud computing, task scheduling has become more and more important for maximizing resource allocation, cutting down on energy use, and enhancing system performance. However, traditional algorithms often fall short in dynamic environments, leading to performance inefficiencies. For cloud computing energy efficiency, this research suggests a hybrid task scheduling method that combines Transformer-based fine-tuning and Cuckoo Search (HCS) optimization. The integration of bio-inspired optimization (HCS) and Modified BERT-Based Transformer allows for dynamic task reallocation, enhancing both energy consumption and resource utilization while maintaining high Service Level Agreement (SLA) compliance. Fine-tuning with Transformer models enables adjustments to improve scheduling efficiency and minimize energy wastage. The results show that the proposed method reduced energy consumption to 0.430 J (from 0.440 J) while maintaining 100% SLA compliance and task completion rate. Resource utilization improved to 88.2%, with energy efficiency of 0.0043 J/task. This hybrid approach offers significant advantages over traditional methods, such as BENBO + Bi-LSTM (0.687 J), PSO-PGA (0.617 J), and GAECS (0.537 J), achieving a reduced energy consumption of 0.440 J for 15 PM with 20 VMs, making it an ideal solution for scalable cloud computing environments.