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This research proposes a novel, low-power routing protocol tailored for IoT networks that merges intelligent data-aggregation clustering with optimized path selection to overcome critical data-handling challenges in smart urban environments. The protocol relies on a layered architecture that includes Data Fusion Heads (DFH), intermediate relay DFHs, and the marine predators algorithm—an effective nature-inspired optimizer equipped with a purpose-built fitness function. This function simultaneously refines multiple criteria: the geographic clustering density of Sensor Nodes (SNs) inside each data fusion group (DFG), the total hop distance toward the sink, intra-group node proximity, residual battery level (RE), Average Scale of Building Occlusions (ASBO), and the rotation interval of the Primary DFH (PDFH). One of the protocol’s core advancements is the systematic application of in-network data fusion, which drastically reduces duplicate packet forwarding and markedly improves the accuracy and usefulness of collected information across all DFGs. By aggregating readings from multiple sensors, the volume of outbound traffic is greatly reduced, yielding substantial improvements in energy efficiency. Two complementary forwarding strategies are supported: single-hop forwarding, where already fused payloads travel directly to the sink, and multi-hop forwarding, in which an appropriate PDF relay is dynamically elected using a weighted relay selection metric that incorporates remaining energy (RE), sink distance, and ASBO. By integrating robust fault-tolerance procedures, intelligent redundancy elimination, and efficient fusion mechanisms, the protocol achieves markedly greater dependability and sustains strong operation under adverse or unpredictable network conditions. Detailed simulation experiments, along with side-by-side benchmarking against contemporary methods, confirm the protocol’s clear advantage across essential performance indicators—namely, network longevity, total energy expenditure, data throughput, and mean end-to-end latency. Compared with leading recent approaches such as the Particle Swarm Optimization-based energy-efficient clustering protocol (PSO-EEC), linearly decreasing inertia weight PSO (LDIWPSO), Optimized Fuzzy Clustering Algorithm (OFCA), and Novel PSO-based Protocol (NPSOP), the presented solution delivers outstanding gains. Concretely, network lifetime is prolonged by 299% versus PSO-EEC, 264% versus LDIWPSO, 306% versus OFCA, and 249% versus NPSOP. Energy usage drops by 254% relative to PSO-EEC, 264% relative to LDIWPSO, 247% relative to OFCA, and 253% relative to NPSOP. Throughput rises by 67% over PSO-EEC, 59% over LDIWPSO, 53% over OFCA, and 50% over NPSOP. By skillfully combining data aggregation with carefully tuned routing decisions, this protocol establishes a new benchmark for power-efficient organization and communication within IoT-based data fusion groups, delivering a dependable and adaptable framework suitable for diverse smart-city and IoT scenarios.

Energy efficiency in industrial systems remains a major challenge, as conventional data-driven models are often constrained by limited precision and insufficient data. Integrating the physical laws that govern energy systems can enhance performance and ensure greater physical plausibility; however, such models often struggle with loss computation and often overlook dynamic interactions among subsystems, leading to excessive simplification and reduced real-world utility. Accordingly, this study explores a theoretical framework for constructing a conservation-law-guided neural network to improve energy-efficiency forecasting in industrial processes. The framework embeds physical principles directly into floating nodes built upon a long short-term memory (LSTM) architecture, enabling the model to capture relationships among process variables more effectively. At the same time, gradient aggregation enhances both fluidity and interpretability. Performance was assessed through two large-scale industrial case studies—vinyl chloride monomer production and detergent powder manufacturing—where the proposed model achieved notable gains in prediction accuracy and overall reliability, delivering test prediction improvements of 12.2% and 5.87% compared to previously published approaches. Compared with methods that rely on network architecture modifications, the proposed model demonstrated superior reliability and reproducibility in energy-efficiency forecasts. Furthermore, the model effectively identified energy inefficiencies, resulting in a 4.21% decrease in energy use and a corresponding reduction of 377.35 tons in carbon emissions.

Amid worsening worldwide environmental degradation and escalating carbon outputs from the construction sector, school campuses—serving as the main settings for young people’s education and daily life—hold significant importance in promoting designs that prioritize low-carbon approaches, enhanced energy conservation, and eco-friendly surroundings. This study examines the renovation of Yezhai Middle School in Qianshan, Anhui Province, as a specific example, investigating methods to improve airflow patterns and to implement facade-based energy conservation measures for hilly-terrain school facilities during upgrades to pre-existing structures. Within the broader push toward intelligent urban systems, embedding cutting-edge tools and eco-conscious methods into communal facilities has emerged as a central priority. By conducting airflow modeling and upgrading building exteriors for greater efficiency, the analysis demonstrates nonlinear increases in airflow velocity with increasing elevation, alongside notable influences of surface texture on fluctuations in wind conditions. Incorporating expanded polystyrene (EPS) boards, along with added thermal barriers, during exterior upgrades reduces heating demand in colder months and air conditioning demand in warmer months. The updated exteriors successfully block chilly drafts while minimizing unwanted solar heat absorption, resulting in energy savings of roughly 24%. These findings offer valuable technical guidance and real-world insights for implementing low-carbon, efficiency-focused upgrades across additional educational and civic structures, supporting the building sector’s shift toward sustainable, lower-emission practices within intelligent urban frameworks.

Improving energy efficiency in the petrochemical sector is vital for reducing energy use and mitigating environmental harm. A reliable model for energy efficiency can provide important guidance for decision-making on operational process adjustments. In real-world settings, however, the varying sampling frequencies common in petrochemical facilities make conventional methods for estimating energy efficiency less dependable and more challenging to apply to multirate datasets. To address this, the present study introduces a multi-channel convolutional neural network framework that leverages transfer learning of model parameters to enhance energy efficiency forecasting despite irregular sampling rates. The multi-channel design enables the extraction of distinct temporal patterns by applying convolutions across the time axis of the data. At the same time, transfer learning enables the model to adapt and capture novel input behaviors after initial training. The effectiveness of this approach for both predicting energy efficiency and evaluating potential savings was demonstrated through its implementation in a vinyl chloride monomer manufacturing scenario. Results indicate that the suggested model surpasses standard benchmark approaches and conventional CNN architectures in predictive precision and consistency, achieving an R² value of 0.97. Incorporating transfer learning helps maintain robust performance without major declines and improves the model’s flexibility for continuous, real-time energy monitoring. In addition, an analysis of energy discrepancies identified substantial conservation opportunities, resulting in an average annual reduction in energy use of 7.25% and a corresponding decrease of 5,709 tons in CO₂ emissions.