Distributed systems are widely used in modern environments such as cloud platforms, Internet of Things (IoT) networks, smart grids, and blockchain-based systems. These platforms often require digital signatures to maintain trust between devices or users. When many signatures are shared at once, the size of the communication grows, and processing takes more time. This becomes a problem in applications where quick message verification is important. Classical digital signature schemes, such as Rivest-Shamir-Adleman (RSA) and Elliptic Curve Digital Signature Algorithm (ECDSA), are not safe against quantum attacks. Post-quantum cryptography offers better protection, but it often increases communication size and computation cost. This paper introduces a quantum-resistant model that supports signature aggregation and constant-time verification. The method is based on lattice-based techniques, using structures similar to CRYSTALS-Dilithium. By combining several signatures into a single aggregated form, the model reduces the time needed for verification and the overall communication overhead. The design is suitable for real-time applications, offering strong accuracy and faster message handling. Simulation results show that the proposed method performs better than recent approaches in multiple areas. The model achieves a low authentication time, high verification accuracy, reduced message size, and improved throughput. These benefits help meet the needs of distributed systems where speed, accuracy, and security must work together. The architecture is designed to be flexible for use in real-world deployments. Future directions include support for multi-party signing, adaptive quantum-safe policies, and integration with quantum co-processors. This research supports the development of secure and efficient communication frameworks that remain effective even in the presence of future quantum threats.

Abstract A network of wireless sensors is a self‐infrastructure approach with many sensory nodes. The distributed sensory nodes communicate with each other via sensory points. In wireless sensor network (WSN), the sensory nodes collect information for healthcare, military and monitoring systems. Such networks require an exclusive arrangement of the nodes to challenge inherent limitations and energy deficiency. The conventional design of a communication system consumes more energy with high latency causing degraded performance. This study provided a machine learning‐based path optimization mechanism using the least energy resources in designing an effective wireless network system with enhanced three measures of network performance, including throughput, packet delivery efficiency and energy usage. The proposed methodology is validated through network simulation tools.

Reliable and accessible cloud applications are essential for the future of ubiquitous computing, smart appliances, and electronic health. Owing to the vastness and diversity of the cloud, a most cloud services, both physical and logical services have failed. Using currently accessible traces, we assessed and characterized the behaviors of successful and unsuccessful activities. We devised and implemented a method to forecast which jobs will fail. The proposed method optimizes cloud applications more efficiently in terms of resource usage. Using Google Cluster, Mustang, and Trinity traces, which are publicly available, an in-depth evaluation of the proposed model was conducted. The traces were also fed into several different machine learning models to select the most reliable model. Our efficiency analysis proves that the model performs well in terms of accuracy, F1-score, and recall. Several factors, such as failure of forecasting work, design of scheduling algorithms, modification of priority criteria, and restriction of task resubmission, may increase cloud service dependability and availability.

Summary Load balancing and task scheduling in cloud have gained a significant attention by many researchers, due to the increased demand of computing resources and services. For this purpose, there are various load balancing methodologies are developed in the existing works, which are mainly focusing on allocating the tasks to Virtual Machines (VMs) based on their priority, order of tasks, and execution time. Still, it facing the major difficulties in finding the best tasks for allocation, because the sequence of patterns are normally used to categorize the relevant tasks with respect to the load. Thus, this research work intends to develop an intelligent group of mechanisms for efficiently allocating the tasks to the VMs by finding the best tasks with respect to the scheduling parameters. Initially, the user tasks are given to the load balancer unit, where the Probabilistic Gray Wolf Optimization (PGWO) technique is used to find the best fitness value for selecting the tasks. Then, the Adaptive Vector Searching (AVS) methodology is utilized to cluster the group of tasks for efficiently allocating the tasks with improved Quality of Service (QoS). Finally, the Recursive Data Acquisition (RDA) based scheduler unit can allocate the clustered tasks to the appropriate VMs in the cloud system by analyzing the properties of storage capacity, balancing load of VM, CPU usage, memory consumption, and execution time of tasks. During evaluation, the performance of the proposed load balancing model is validated by using various measures. Then, the obtained results are compared with some state‐of‐the‐art models for proving the betterment of the proposed scheme.