Intelligent Multi-Objective Optimization of 4G LTE Performance Using an AI–WOA Framework

The growing demand for efficient and reliable sensor networks in applications such as environmental monitoring, healthcare, and smart cities has highlighted the need for optimizing communication protocols, power consumption, and data management. Traditional methods often focus on optimizing individual layers of the network without considering the interactions across layers. This approach limits the overall performance, especially as networks scale in size and complexity. The lack of effective cross-layer optimization strategies hinders the performance of sensor networks, leading to suboptimal energy usage, low throughput, and high latency. Moreover, sensor nodes in such networks often face constraints such as limited energy, computation power, and memory, making traditional optimization methods inadequate for achieving high efficiency across all layers of the network. This paper proposes a cross-layer design and optimization framework leveraging Artificial Intelligence (AI) and Deep Learning (DL) algorithms, specifically Recurrent Neural Networks (RNN) and Deep Belief Networks (DBN), to address these challenges. The RNN is employed to model the temporal dependencies in the sensor data, while the DBN is used for optimizing decision-making processes across multiple network layers. The proposed framework dynamically adjusts routing, data aggregation, and power control parameters based on real-time conditions, improving overall network performance. Simulation results demonstrate that the proposed approach outperforms traditional cross-layer design methods. The RNN-DBN framework achieved a 35% improvement in energy efficiency, a 25% reduction in latency, and a 40% increase in data throughput compared to existing optimization techniques. These enhancements are particularly significant in large-scale, real-time sensor networks.

Improving energy efficiency is an important way to achieve low-carbon economic development, a common goal of most nations. Based on the comprehensive survey data of enterprises above a designated size in Guangdong Province, this paper studies the impact of artificial intelligence on the energy efficiency of manufacturing enterprises. The results show that: (1) artificial intelligence, as measured by the use of industrial robots, has significantly improved the energy efficiency of manufacturing enterprises. This conclusion is still robust after introducing data on industrial robots in the United States over the same time period as the instrumental variable for the endogeneity test. (2) The mechanism test shows that artificial intelligence mainly promotes the improvement in energy efficiency by promoting technological progress; the impact of artificial intelligence on the technological efficiency of enterprises is not significant. (3) Heterogeneity analysis shows that the age of the manufacturing enterprises inhibits a promoting effect of artificial intelligence on energy efficiency; manufacturing enterprises’ performance can enhance the promoting effect of artificial intelligence on energy efficiency, but this promoting effect can only be shown when the enterprise performance is positive. The paper clarifies both the impact of artificial intelligence on the energy efficiency of manufacturing enterprises and its mechanism of action; this will help provide a reference for future decision-making designed to improve manufacturing enterprises’ energy efficiency.

Incorporating Device-to-Device (D2D) communication and Unmanned Aerial Vehicles (UAVs) into next-generation cellular networks is crucial for addressing the growing demand for high-data-rate applications. However, increasing communication distances in both cellular and D2D environments raises transmission power requirements, leading to higher energy consumption and reduced network efficiency. Additionally, in the conventional technique, UAVs communicate directly with each UE individually, further exacerbating the energy demands of UAVs. To overcome these challenges, this paper presents an energy-efficient clustering technique designed to minimize power consumption for both UEs and UAVs. Hypergraph-based clustering enables adaptive grouping by leveraging received signal strength, while the Whale Optimization Algorithm (WOA) selects central users based on UE distance, residual energy, and connectivity metrics. This structured clustering strategy enhances system throughput and optimizes energy efficiency. Extensive simulations validate the proposed model, demonstrating significant improvements in energy efficiency, reduced computational complexity, and superior network performance compared to existing methods.

Abstract. The global trend towards carbon-neutral energy systems has established hydrogen as a key enabling carrier of energy for green electricity, mobility and industrial use. Advances in informatics/telecom are changing perceptions of government agencies, technology vendors and energy service companies towards hydrogen infrastructure. In this paper, a digitalized approach for hydrogen energy system is proposed regarding data reports, IoT (Internet of Things), cloud computing and 5G/6G communication network. We also show that intelligent hydrogen systems support electrolysis monitoring in real-time, predictive storage maintenance and coordinated integration of renewable resources by employing digital twins, edge computing and artificial intelligence. The proposed DHES system model (Digital Energy System for Hydrogen) also achieves significant performance enhancement with 84.2% reduction in data latency from 95ms to 15ms, 28% gains on predictive maintenance accuracy, 6% improvement in energy efficiency and 9% enhancement in system availability than traditional approaches. Additionally, data transparency and traceability are significantly enhanced. The study addresses cybersecurity, interoperability, and data standardization challenges, while identifying future research directions toward smart, fully connected hydrogen ecosystems.

This study investigates the integration of Artificial Intelligence (AI) to enhance energy efficiency in Italian hospitals, known for their high energy consumption due to continuous operations and stringent environmental controls. Using AI strategies from the Italian Ministry of Economic Development, the research focuses on optimizing hospital energy management. Analyzing data from 996 major hospitals (2016-2023) revealed an average annual energy consumption of 1,062 kTep. Key parameters were benchmarked against European standards, and MATLAB was utilized for hourly consumption analysis to identify inefficiencies. AI optimization, leveraging historical data, aids in predictive maintenance and reduces equipment downtime. The study underscores the complexities of managing energy in aging hospital infrastructures. Despite the critical need for energy efficiency, many opportunities have been missed due to prioritization of medical technologies and urgent structural repairs. AI offers a solution by enhancing energy management through data analysis, predictive maintenance, and real-time adjustments, leading to significant energy savings and operational improvements. Challenges include high initial investment for AI technology and training, technical integration issues, and variable energy consumption patterns. Nonetheless, the benefits of AI in improving energy efficiency and sustainability in hospitals are substantial, presenting a promising avenue for future research and investment. Strategic AI incorporation can significantly boost operational performance and sustainability, benefiting hospitals across Italy and beyond.

Two approaches are proposed for short-term forecast of the parameters of expected operating conditions. The Kalman filter based algorithms and the modern technologies of an artificial intelligence and nonlinear optimization algorithms are employed for dynamical state estimation. The new approach combining the artificial neural networks and the Hilbert-Huang transform is designed in order to increase the accuracy of operating conditions forecasting. Numerical experiments on real time series have demonstrated the improvement of the prediction.

10 - Improving energy efficiency in next-generation IoT networks with beam adjustment technique

Learning from nature for healthcare, energy, and environment

Sensor technology advancements have provided a viable solution to fight COVID and to develop healthcare systems based on Internet of Things (IoTs). In this study, image processing and Artificial Intelligence (AI) are used to improve the IoT framework. Computed Tomography (CT) image‐based forecasting of COVID disease is among the important activities in medicine for measuring the severity of variability in the human body. In COVID CT images, the optimal gamma correction value was optimised using the Whale Optimisation Algorithm (WOA). During the search for the optimal solution, WOA was found to be a highly efficient algorithm, which has the characteristics of high precision and fast convergence. Whale Optimisation Algorithm is used to find best gamma correction value to present detailed information about a lung CT image, Also, in this study, analysis of important AI techniques has been done, such as Support Vector Machine (SVM) and Deep‐Learning (Deep‐Learning (DL)) for COVID disease forecasting in terms of amount of data training and computational power. Many experiments have been implemented to investigate the optimisation: SVM and DL with WOA and without WOA are compared by using confusion matrix parameters. From the results, we find that the DL model outperforms the SVM with WOA and without WOA.

Decades of techno-economic energy policymaking and research have meant evidence from the Social Sciences and Humanities (SSH)—including critical reflections on what changing a society’s relation to energy (efficiency) even means—have been underutilised. In particular, (i) the SSH have too often been sidelined and/or narrowly pigeonholed by policymakers, funders, and other decision-makers when driving research agendas, and (ii) the setting of SSH-focused research agendas has not historically embedded inclusive and deliberative processes. The aim of this paper is to address these gaps through the production of a research agenda outlining future SSH research priorities for energy efficiency. A Horizon Scanning exercise was run, which sought to identify 100 priority SSH questions for energy efficiency research. This exercise included 152 researchers with prior SSH expertise on energy efficiency, who together spanned 62 (sub-)disciplines of SSH, 23 countries, and a full range of career stages. The resultant questions were inductively clustered into seven themes as follows: (1) Citizenship, engagement and knowledge exchange in relation to energy efficiency; (2) Energy efficiency in relation to equity, justice, poverty and vulnerability; (3) Energy efficiency in relation to everyday life and practices of energy consumption and production; (4) Framing, defining and measuring energy efficiency; (5) Governance, policy and political issues around energy efficiency; (6) Roles of economic systems, supply chains and financial mechanisms in improving energy efficiency; and (7) The interactions, unintended consequences and rebound effects of energy efficiency interventions. Given the consistent centrality of energy efficiency in policy programmes, this paper highlights that well-developed SSH approaches are ready to be mobilised to contribute to the development, and/or to understand the implications, of energy efficiency measures and governance solutions. Implicitly, it also emphasises the heterogeneity of SSH policy evidence that can be produced. The agenda will be of use for both (1) those new to the energy-SSH field (including policyworkers), for learnings on the capabilities and capacities of energy-SSH, and (2) established energy-SSH researchers, for insights on the collectively held futures of energy-SSH research.

A rising meta-heuristic swarm intelligent computation algorithm, the Whale optimization Algorithm (WOA), which has been proved competitive with other intelligent optimization algorithms in solving different optimization problems. To accelerate the convergence rate of WOA, this paper has proposed a new improved whale optimization algorithm, called Chaos Regulation Whale optimization Algorithm (CRWOA). The chaotic map generator composed with three kinds of chaotic maps has been used to initialize the population. Furthermore, Cauchy distribution and Gauss distribution are introduced to regulate the position in each iteration. To analyze the superiority of CRWOA further, ten single-objective benchmark test functions are chosen. Through the comparison and analysis of the optimal results, CRWOA is demonstrated superior in general. It has faster convergence speed and better results than the other algorithms when less iteration number, less population quantity and wider ranges of variables are set.

The increasing demand for higher data rates pushes the boundaries of the currently used radio spectrum. The terahertz (THz) frequency band (0.1-10 THz) is widely considered by scientific community to address spectrum scarcity. In this paper we developed a theoretical model for device-to-device (D2D) communication operating in THz band. We also derived a close form formula of data rates, outage probability, and energy efficiency. Our simulation results show an improvement of data rates and energy efficiency while decreasing the outage probability of D2D communication in THz. For instance, there is 86% of increase in energy efficiency when the transmission power is 19dBm. Finally, the improvement of energy efficiency is 87% using optimal transmission power due to 50 resource blocks.

Improvement of energy efficiency has been a major public policy concern in Korea. Investment cost for location of avoidance facility such as nuclear power and radioactive waste management facility, environmental pollution etc could significantly reduced from improvement of energy efficiency. Energy efficiency improvement could be one of good solution to cope with volatile energy market. Korea has been high oversea dependency in fossil fuels supply to meet its energy requirement. Its import dependency on fossil fuels in 1995 and 2000 is respectively 96.8% and 97.2%. The fact that Korea depends heavily on imported energy shows it should make more efforts to promote improvement of energy efficiency. Although Korean Government has supported industrial sector in improvement of energy efficiency, some energy analysts and critics point out that Korea has little progressed in energy efficiency improvement. This paper estimated its energy efficiency and analyzed its improvement with energy Input- Output table 90-95-2000 of Korea. The results are appeared differently. The cross section and time series analysis result of the energy efficiency is that energy elasticity for GDP, energy intensity and induced energy consumption between 1995 and 2000 significantly improved. From 1990 to 1995, energy elasticity for GDP is bigger than 1. It means that energy consumption increased rapidly more than GDP growth rate. This is the evidence that energy efficiency was not improved. Energy intensity of each industrial sector between 1995 and 2000 is lower than in cases of 1990 and 1995. It shows that energy efficiency was improved. As energy intensity of each industry becomes lower, induced energy consumption of economy goes smaller because of interaction among sectors.

In this paper, the optimization of energy efficiency (EE) and the improvement of user data rate in cloud radio access networks (C-RANs) are studied by introducing a weighted sparse beamforming (WSB) method. Using stochastic geometry tools, analytical expressions are derived to describe downlink ergodic rate and coverage probability for coordination multipoint joint transmission in C-RAN. Based on these expressions and by taking advantage of central processing and coordination in baseband unit pool, power allocation among remote radio heads is optimized using the WSB. The WSB optimization algorithm is based on water-filling power allocation strategy and redistributing the power among RRHs. The improvement in both EE and ergodic rate is achieved. The improvement in EE is pronounced up to 28% using WSB technique compare to the equal power beamforming method in the proposed C-RAN network.

Background The use of artificial intelligence (AI) algorithms for the diagnosis of skin diseases has shown promise in experimental settings but has not yet been tested in real-life conditions. The COVID-19 pandemic led to a worldwide disruption of health systems, increasing the use of telemedicine. There is an opportunity to include AI algorithms in the teledermatology workflow. Objective The aim of this study is to test the performance of and physicians’ preferences regarding an AI algorithm during the evaluation of patients via teledermatology. Methods We performed a prospective study in 340 cases from 281 patients using patient-taken photos during teledermatology encounters. The photos were evaluated by an AI algorithm and the diagnosis was compared with the clinician’s diagnosis. Physicians also reported whether the AI algorithm was useful or not. Results The balanced (in-distribution) top-1 accuracy of the algorithm (47.6%) was comparable to the dermatologists (49.7%) and residents (47.7%) but superior to the general practitioners (39.7%; P=.049). Exposure to the AI algorithm results was considered useful in 11.8% of visits (n=40) and the teledermatologist correctly modified the real-time diagnosis in 0.6% (n=2) of cases. Algorithm performance was associated with patient skin type and image quality. Conclusions AI algorithms appear to be a promising tool in the triage and evaluation of lesions in patient-taken photographs via telemedicine. Conflicts of Interest None declared.