The rise of quantum computing poses a major threat to traditional ciphers, specifically making the Radio Access Network (RAN) vulnerable to future quantum-based attacks. To address this challenge, this paper proposes quantum-inspired RAN (Q-RAN), a next-generation framework designed to future-proof telecom networks against quantum threats. As a foundation, to evaluate, we integrate quantum network simulators with the Open-Source OpenAirInterface (OAI) stack to deploy a 5G standalone network capable of supporting quantum operations. We analyzed these, and the best fit is taken for quantum proofing. This paper considered four different quantum simulators — QuNetSim, SimQN, SeQuence, and NetSquid — for integrating with the 5G Open source stack. The framework generated by these simulators is called Quantum Gateways (namely Qtunnels) enables seamless message exchange between quantum and classical RAN components. This integrated architecture showcases how quantum simulators can be harmonized on existing 5G infrastructure, establishing a resilient and adaptable telco ecosystem catered to the quantum era or post-quantum era. In addition, this paper quantifies the Qtunnel impact — F1 Interface — between the Central Unit (CU) and Distribution Unit (DU) of RAN. The results show that the Post Quantum Cryptography (PQC)-enabled F1 interfaces provide a two-fold overhead delay when compared to the benchmark delay, however, the PQC-enabled RAN interfaces are resistant to quantum threats.

Quantum networks are set to transform distributed communication and computation. However, achieving energy efficiency and resource optimization in large-scale networks is notably challenging because of the complexity of creating and routing quantum entanglement. This study presents a new quantum routing framework, Quantum Fidelity-Energy Optimized Routing (QFER), which dynamically balances energy use and fidelity loss to improve entanglement generation in diverse quantum networks. It employs a multiobjective optimization strategy that considers fidelity degradation, energy efficiency, and success probability, considering device variability, network traffic, and coherence limits. Real-time path optimization enables effective entanglement creation and optimal resource use in multi-hop networks. This approach significantly improves network scalability, throughput, and energy efficiency, supporting the development of resilient and sustainable quantum Internet frameworks that can meet the needs of advanced quantum communication systems.

ABSTRACT Globally, electric vehicles (EVs) are entirely revolutionizing conventional vehicles owing to the benefits of EVs, such as decarbonization, being environmentally friendly, and lower maintenance costs. The EVs' energy consumption is sensitive to environmental factors like wind speed, parasitic power, rolling resistance, and temperature, which can significantly affect the EVs' energy consumption range. Here, this comprehensive literature survey investigates the techniques used for managing the energy consumption of EVs while analyzing security utilizing Blockchain (BC) and deep learning (DL) algorithms. In the rapidly developing background of EVs, the incorporation of advanced technologies like DL and BC is transforming predictive maintenance (PM) strategies. This approach aims to optimize vehicle performance and minimize downtime by accurately predicting and addressing previous potential system failures. The amalgamation of DL and BC provides a robust approach for PM, enabling proactive maintenance strategies that reduce downtime and costs while improving overall vehicle performance. Thus, this review explains the importance of DL‐enabled PM in EVs, DL models for PM in EVs, the role of BC‐enabled PM in EVs, and the combination of DL models and BC for PM in EVs.

Abstract By combining blockchain technology (BT) and reinforcement learning (RL), the proposed work addresses the difficulties associated with vaccine recommendations. The need for individualized recommendations is obvious as vaccine schedules become more complicated and there are more vaccines available. The proposed work presents a novel approach that combines the adaptability of RL with the security, privacy, and transparency of BT. Layers for data processing, application, consensus, and smart contracts are included in the system architecture. It provides user‐managed secure access, individualized vaccine recommendations, and decentralized data storage. The system aims to improve public health outcomes by using smart contracts to automate procedures and RL to improve recommendations. Using 10‐fold cross‐validation on the Vaccine Adverse Event Reporting System (VAERS) dataset, the experimental study verifies the performance of the system. The study focuses on metrics like accuracy, sensitivity, specificity, and F‐measure when contrasting the proposed model with current solutions. The ability of proposed model to offer precise and well‐informed vaccine recommendations is demonstrated by its consistent outperformance of competitors in accuracy and sensitivity.

Abstract Big services are collections of interrelated web services across virtual and physical domains, processing Big Data. Existing service selection and composition algorithms fail to achieve the global optimum solution in a reasonable time. In this paper, we design an efficient quality of service‐aware big service composition methodology using a distributed co‐evolutionary algorithm. In our proposed model, we develop a distributed NSGA‐III for finding the optimal Pareto front and a distributed multi‐objective Jaya algorithm for enhancing the diversity of solutions. The distributed co‐evolutionary algorithm finds the near‐optimal solution in a fast and scalable way.

Abstract Cloud computing (CC) allows on‐demand networks to access central computer resources, such as servers, databases, storage, and network services. While clouds can handle enormous amounts of data, they still encounter problems due to insufficient cloud resources. Therefore, another computing model, called fog computing, was introduced. However, the inefficient scheduling of user tasks in fog computing can cause more delays than that in CC. To address the issues of resource utilization, response time, and latency, optimal and efficient techniques are required for the scheduling strategies. In this study, we developed an extended particle swarm optimization (EPSO) algorithm with an extra gradient method to optimize the task scheduling problem in cloud‐fog environments. Our primary aim is to improve the efficiency of resources and minimize the time taken to complete tasks. We conducted extensive experiments on the iFogSim simulator in terms of makespan and total cost. We compared the performance of the proposed EPSO method with that of other traditional techniques, such as ideal PSO and modified PSO; the results demonstrated that EPSO achieved a makespan of 342.53 s. Thus, it can be concluded that the performance of the proposed method is comparable to that of other approaches.

Summary Big services are the collection of interrelated web services across virtual and physical domains, integrating service oriented computing and big data. The rapid growth of Big services that offer similar functionality with varying QoS attributes makes the process of selection and composition of these big services as highly challenging and complex. In this paper, we develop an efficient QoS‐aware Big service composition approach by applying a MapReduce based Modified Grey Wolf Optimizer (MR‐MGWO) that explores more search space, especially in a multidimensional environment. Our approach ensures an optimal balance of exploration and exploitation that enhances the convergence rate and minimizes the computational time. The empirical analysis illustrates that the performance of MR‐MGWO is superior to other similar approaches for solving Big service composition.

Web service composition concerns the building of new value added services by integrating the sets of existing web services. Due to the seamless proliferation of web services, it becomes difficult to find a suitable web service that satisfies the requirements of users during web service composition. This paper systematically reviews existing research on QoS-aware web service composition using computational intelligence techniques (published between 2005 and 2015). This paper develops a classification of research approaches on computational intelligence based QoS-aware web service composition and describes future research directions in this area. In particular, the results of this study confirms that new meta-heuristic algorithms have not yet been applied for solving QoS-aware web services composition.