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Smart grid represents an advanced and interconnected system that incorporates modern technologies to enhance efficiency, reliability and sustainability. In comparison to the conventional grid, the smart grid (SG) uses many cutting-edge technologies. This research introduces a fog computing encryption-based model for privacy preservation in the smart grid model. By using different advanced technologies, our model addresses the balance between privacy, security, effectiveness and functionality. The model facilitates efficient communication and function inquiry while mitigating challenges posed by massive Internet of Things (IoT) systems in the smart grid environment. Specifically, it tackles the secure data consolidation challenge by employing encryption-based techniques for transmitting private data from smart meters to fog devices. These devices consolidate the data before updating to cloud. Conventional data consolidation approaches for SGs have high computation and communication costs and suffer from high storage requirement. The proposed model resolves these issues; algorithms for data consolidation and extraction of data at fog device and cloud respectively to obtain the secure communication has also been included. The performance of the developed mechanism has been computed against existing data consolidation mechanisms GCEDA (Grouping of Clusters for Efficient Data Aggregation), SPPDA (Secure Privacy-Preserving Data Aggregation) and LPDA (Lightweight Privacy-preserving Data Aggregation) for numerous performance parameters. And the results proves that the performance of developed mechanism with respect to bytes of storage, communication cost and ratio of transmission is better than the existing ones.

Efficient character recognition in ancient handwritten Devanagari documents is crucial for societal advancements. Challenges such as overlapping characters, missing headlines, and over-inked stains further complicate the recognition process. In response, we propose a Capsule Network (CapsNet) with LSTM to address hierarchical temporal dependencies in Devanagari scripts, following initial implementation of a simple CNN. We also explored a combined CNN+LSTM architecture for character recognition, leveraging CNN’s feature extraction capabilities with LSTM’s sequential modeling to handle temporal dependencies in Devanagari scripts. Our experimentation involved a dataset of 10,825 characters from ancient Devanagari manuscripts, encompassing basic characters, modifiers, and conjuncts, classified into 399 classes. Testing various training–testing ratios (9:1, 8:2, and 7:3), we visually and statistically evaluated the experimental data, demonstrating the superiority of CapsNet and LSTM in handling these challenges. We calculated recognition accuracy, precision, and recall values, with CapsNet achieving a maximum accuracy of 95.98% after 30 epochs. This research underscores the effectiveness of CapsNet and LSTM in advancing character recognition for ancient Devanagari manuscripts.

The performance of drones, especially for time-sensitive tasks, is critical in various applications. Fog nodes strategically placed near IoT devices serve as computational resources for drones, ensuring quick service responses for deadline-driven tasks. However, the limited battery capacity of drones poses a challenge, necessitating energy-efficient Internet of Drones (IoD) systems. Despite the increasing demand for drone flying automation, there is a significant absence of a comprehensive drone network service architecture tailored for secure and efficient operations of drones. This research paper addresses this gap by proposing a safe, reliable, and real-time drone network service architecture, emphasizing collaboration with fog computing. The contribution includes a systematic architecture design and integration of blockchain technology for secure data storage. Fog computing was introduced for the Drone with Blockchain Technology (FCDBT) model, where drones collaborate to process IoT data efficiently. The proposed algorithm dynamically plans drone trajectories and optimizes computation offloading. Results from simulations demonstrate the effectiveness of the proposed architecture, showcasing reduced average response latency and improved throughput, particularly when accessing resources from fog nodes. Furthermore, the model evaluates blockchain consensus algorithms (PoW, PoS, DAG) and recommends DAG for superior performance in handling IoT data. Fog; Drones; Blockchain; PSO; IoT; Vehicular.

Abstract Distributed denial-of-service (DDoS) attacks persistently proliferate, impacting individuals and Internet Service Providers (ISPs). Deep learning (DL) models are paving the way to address these challenges and the dynamic nature of potential threats. Traditional detection systems, relying on signature-based techniques, are susceptible to next-generation malware. Integrating DL approaches in cloud-edge/federated servers enhances the resilience of these systems. In the Internet of Things (IoT) and autonomous networks, DL, particularly federated learning, has gained prominence for attack detection. Unlike conventional models (centralized and localized DL), federated learning does not require access to users’ private data for attack detection. This approach is gaining much interest in academia and industry due to its deployment on local and global cloud-edge models. Recent advancements in DL enable training a quality cloud-edge model across various users (collaborators) without exchanging personal information. Federated learning, emphasizing privacy preservation at the cloud-edge terminal, holds significant potential for facilitating privacy-aware learning among collaborators. This paper addresses: (1) The deployment of an optimized deep neural network for network traffic classification. (2) The coordination of federated server model parameters with training across devices in IoT domains. A federated flowchart is proposed for training and aggregating local model updates. (3) The generation of a global model at the cloud-edge terminal after multiple rounds between domains and servers. (4) Experimental validation on the BoT-IoT dataset demonstrates that the federated learning model can reliably detect attacks with efficient classification, privacy, and confidentiality. Additionally, it requires minimal memory space for storing training data, resulting in minimal network delay. Consequently, the proposed framework outperforms both centralized and localized DL models, achieving superior performance.

Diagnosing brain tumors is difficult, especially at an early stage of the disease. Conventional approaches often cause delays in providing required treatment to the patients and shorten their lifespan. This paper presents a novel integrated approach with advanced subsets of artificial intelligence, including deep learning and supervised learning algorithms. These new technologies have demonstrated outstanding potential due to their ability to capture the appropriate features based on the input data. They can assist medical experts in identifying abnormal growth of cells inside the brain. We use publicly available brain magnetic resonance imaging (MRI) datasets to diagnose brain tumors and develop an automated system. The proposed approach uses data augmentation to enhance the image sizes and maintain standardization. We then deploy a visual geometry group with 16 layers following transfer learning to help minimize the medical experts’ workload in making accurate decisions. We extract the most significant features and improve the diagnostic speed and accuracy using a supervised learning algorithm and linear support vector machines (SVM). The proposed model outperforms the existing approaches with an accuracy of 98.87%, precision of 99.09%, recall of 98.73%, specificity of 99.02%, and F1-score of 98.91%.

Abstract The real world applications are more prone to difficulties of challenges due to fast growth of technologies and inclusion of artificial intelligence (AI) based logical solutions. The massive internet-of-things (IoT) devices are involved in number of Industry 5.0 applications like smart healthcare, smart manufacturing, smart agriculture, smart transportation. Advanced wireless techniques, customization of services and different technologies are experiencing a major transformation. The desire to increase the communication reliability without adding energy overhead is the major challenge for massive IoT enabled networks. To cope up with the above challenges, Industry 5.0 requirements needs to be monitored at the remote level which again adds on the communication challenge. Use of relays in 6G based wireless networks is denied due to high requirement of energy. Therefore in this paper, Intelligent reflecting surfaces (IRSs) assisted energy constrained 6G wireless networks are studied. To provide seamless connection between the communicating mobile nodes, IRS with an array of reflecting elements are configured in the system set up. A use-case scenario of IRS enabled network in Internet-of-Underwater things (IoUT) for smart ocean transportation is also provided. The IRS assisted wireless network is evaluated for target rates achieved. A power consumption model of the IRS supported system is also proposed to optimise the energy efficiency of the system. Further, the paper evaluates the impact of number of reflecting elements N on the IRS and the phase resolution b of each element on the system performance. The energy efficiency improves by 20% for IRS with $$N=100$$ N = 100 with $$b=2$$ b = 2 over IRS with $$b=1$$ b = 1 .

Manuscripts serve as a wealth of knowledge for future generations and are a useful source of information for locating material from the Middle Ages. Ancient manuscripts can be found in handwritten form, thus they must be translated into digital form so that computing equipment can access them and additional indexing and search operations can be performed with ease. Manuscript recognition is already possible using a variety of methods. Regional languages like Devanagari, Gurmukhi, Sanskrit, etc., however, have very few methods available. In this study, the Devanagari characters from the manuscripts is recognised using a CapsNet-based method. 33 fundamental characters, 3 conjuncts, and 12 modifiers make up the Devanagari alphabet. The complete dataset is divided into 399 classes for the recognition of basic, modifiers, and conjunct characters. Due to spatial relationship, CapsNet is used to recognize the handwritten characters. The proposed model was run using 10:70, 20:80, and 30:70 as test: train ratio of characters. Also, the number of epochs was varied for better recognition accuracy. The authors observed the best recognition accuracy of 94.6% was achieved to recognize the Devanagari characters using CapsNet.

Vehicular Networks (VN) utilizing Software Defined Networking (SDN) have garnered significant attention recently, paralleling the advancements in wireless networks. VN are deployed to optimize traffic flow, enhance the driving experience, and ensure road safety. However, VN are vulnerable to Distributed Denial of Service (DDoS) attacks, posing severe threats in the contemporary Internet landscape. With the surge in Internet traffic, this study proposes novel methodologies for effectively detecting DDoS attacks within Software-Defined Vehicular Networks (SDVN), wherein attackers commandeer compromised nodes to monopolize network resources, disrupting communication among vehicles and between vehicles and infrastructure. The proposed methodology aims to: (i) analyze statistical flow and compute entropy, and (ii) implement Machine Learning (ML) algorithms within SDN Intrusion Detection Systems for Internet of Things (IoT) environments. Additionally, the approach distinguishes between reconnaissance, Denial of Service (DoS), and DDoS traffic by addressing the challenges of imbalanced and overfitting dataset traces. One of the significant challenges in this integration is managing the computational load and ensuring real-time performance. The ML models, especially complex ones like Random Forest, require substantial processing power, which necessitates efficient data handling and possibly leveraging edge computing resources to reduce latency. Ensuring scalability and maintaining high detection accuracy as network traffic grows and evolves is another critical challenge. By leveraging a minimal subset of features from a given dataset, a comparative study is conducted to determine the optimal sample size for maximizing model accuracy. Further, the study evaluates the impact of various dataset attributes on performance thresholds. The K-nearest Neighbor, Random Forest, and Logistic Regression supervised ML classifiers are assessed using the BoT-IoT dataset. The results indicate that the Random Forest classifier achieves superior performance metrics, with Precision, F1-score, Accuracy, and Recall rates of 92%, 92%, 91%, and 90%, respectively, over five iterations.

Abstract Vehicular Content Networks (VCNs) represent key empowering solution for content distribution in fully distributed manner for vehicular infotainment applications. In VCN, both on board unit (OBU) of each vehicle and road side units (RSUs) facilitate content caching to support timely content delivery for moving vehicles when requested. However, due to limited caching capacity available at both RSUs and OBUs, only selected content can be cached. Moreover, the contents being demanded in vehicular infotainment applications are transient in nature. The transient content caching in vehicular content networks with the use of edge communication for delay free services is fundamental issue and need to get addressed (Yang et al. in ICC 2022-IEEE international conference on communications. IEEE, pp 1–6, 2022). Therefore, this study focuses on edge communication in VCNs by firstly organizing a region based classification for vehicular network components including RSUs and OBUs. Secondly, a theoretical model is designed for each vehicle to decide its content fetching location (i.e. either RSU or OBU) in current region or neighboring region. Further, the caching of transient contents inside vehicular network components (such as RSU, OBU) is based on content caching probability. Finally, the proposed scheme is evaluated under different network condition in Icarus simulator for various performance parameters. The simulation results proved outstanding performance of the proposed approach over various state of art caching strategies.