Several advanced features exist in fifth-generation (5G) correspondence than in fourth-generation (4G) correspondence. Centric cloud-computing architecture achieves resource sharing and effectively handles big data explosion. For data security problems, researchers had developed many methods to protect data against cyber-attacks. Only a few solutions are based on blockchain (BC), but are affected by expensive storage costs, network latency, confidence, and capacity. Things are represented in digital form in the virtual cyberspace which is the major responsibility of the communication model based on cybertwin. A novel cybertwin-based UAV 6G network architecture is proposed with new concepts such as cloud operators and cybertwin in UAV. Here, IoE applications have to be energy aware and provide scalability with less latency. A novel Compute first networking (CFN) framework named secure blockchain-based UAV communication (BC-UAV) is designed which offers network services such as computing, caching, and communication resources. The focus of the blockchain was to improve the security in the cloud using hashing technique. Edge clouds support core clouds to quickly respond to user requests.

Wireless sensor networks (WSNs) are considerably used for various environmental sensing applications. The architecture and internal specifications of WSNs have been chosen based on the requirements of particular applications. On this basis, WSNs consist of resource (energy and memory)-limited wireless sensor nodes. WSNs initiate data communication from source to destination via physical layer management principles, channel slot scheduling principles (time division multiple access), wireless medium access control (WMAC) protocols, wireless routing protocols and application protocols. In this environment, the development of WMAC principles, routing protocols and channel allotment schemes play crucial roles in network communication phases. Consequently, these layering functions consume more energy at each sensor node, which leads to minimal network lifetime. Even though the channel management schemes, medium control protocols and routing protocols are functionally suitable, the excessive energy consumption affects the overall network performance. In this situation, energy optimization algorithms are advised to minimize the resource wastage of WSNs during regular operations (medium control and routing process). Many research works struggle to identify the optimal energy-efficient load balancing strategies to improve WSN functions. With this in mind, the proposed article has conducted a detailed literature review and notable experimental comparisons on energy-efficient MAC protocols, channel scheduling policies and energy-efficient routing protocols. To an extent, the detailed analysis over these wireless network operations helps to understand the benefits and limitations of recent research works. In the experimental section of this article, eight existing techniques are evaluated under energy optimization strategies (WMAC, channel allocation, sleep/wake protocols, integrated routing and WMAC policies, balanced routing and cooperative routing). The proposed review and the classified technical observations collected from notable recent works have been recognized as crucial contributions. The results infer the suggestions for feasible WSN communication strategies with optimal channel management policies and routing policies. Notably, the simulation results show that cross-layer or multi-layer energy optimization policies perform better than homogeneous energy optimization models.

This review focuses on Distributed Generation Planning within Multi-Energy Microgrids (MES), a transformative approach where various energy forms like electricity, heat, and cooling interact optimally. MES offers significant technical, economic, and environmental benefits, making it crucial to explore intricate modelling and assessment techniques for such systems, including microgrids, virtual power plants (VPPs), and energy hubs. Amid global energy challenges and environmental crises, renewable energy adoption is vital. Distributed Generation (DG) plays a pivotal role, but integrating it at scale poses challenges. Microgrids are a viable solution, and this review introduces their core concepts, emerging trends, and challenges, such as integrating more renewable energy sources, multienergy forms, complex architectures, demand-side management, and advanced storage. Furthermore, the paper delves into Multi-Energy Microgrid planning complexities, particularly in off-grid areas. It presents a stochastic investment model that minimizes costs and carbon emissions while optimizing the distributed energy resource mix, locations, and sizes. This model considers intricate energy flows and uncertainties associated with renewable generation and energy demands. Case studies confirm significant cost and emission reductions, establishing it as a promising multi-energy microgrid planning solution for the future.

Machine learning algorithms have quickly risen to the top of several fields' decision-making processes in recent years. However, it is simple for these algorithms to confirm already present prejudices in data, leading to biassed and unfair choices. In this work, we examine bias in machine learning in great detail and offer strategies for promoting fair and moral algorithm design. The paper then emphasises the value of fairnessaware machine learning algorithms, which aim to lessen bias by including fairness constraints into the training and evaluation procedures. Reweighting, adversarial training, and resampling are a few strategies that could be used to overcome prejudice. Machine learning systems that better serve society and respect ethical ideals can be developed by promoting justice, transparency, and inclusivity. This paper lays the groundwork for researchers, practitioners, and policymakers to forward the cause of ethical and fair machine learning through concerted effort.

Managing patients in modern healthcare is getting harder and harder, mostly because more people are getting chronic diseases and the world's population is getting older. To deal with these problems, Intelligent Health Management Systems (IHMS) are being built into the healthcare system. These systems promise to make tracking and diagnosing patients much better. This essay looks at how IHMS could change real-time patient care by using cutting-edge information systems technologies like blockchain, artificial intelligence (AI), and machine learning (ML). IHMS really works because they can constantly collect and look at huge amounts of health data from many places, like personal sensors, electronic health records, and what patients say about their results. With this combination, a full picture of a patient's health can be seen at any time. AI and ML algorithms are very important because they can understand large datasets and find patterns to find trends, predict health events, and suggest specific treatment plans. The use of blockchain technology protects the data's stability, security, and privacy, easing major worries about data leaks and illegal access in healthcare. This paper discuss about several examples of how IHMS has used predictive analytics and quick actions to improve patient results and make diagnoses more accurate. Problems like how systems can talk to each other, data safety issues, and the need for strong legal frameworks are also looked at. The results show that using IHMS in healthcare can improve patient care, make better use of resources, and eventually lead to better health outcomes, even though there are some problems to solve. In the end, we suggest a strategy for integrating these technologies into current healthcare systems that will work well. We stress how important it is for engineers, healthcare workers, and lawmakers to work together.