Next-generation smart cities are increasingly reliant on high-speed, secure communication systems to support 6 G networks, IoT infrastructure, and autonomous systems. Classical cryptographic techniques are becoming vulnerable to quantum computing attacks, prompting the integration of quantum-secure communication mechanisms. In this context, Quantum Key Distribution (QKD) over Visible Light Communication (VLC) and Free Space Optical (FSO) networks offers a promising path toward ultra-secure, energy-efficient transmission. We propose an Entangled Photon Quantum Key Distribution (EP-QKD) framework that advances secure communication by integrating polarization-entangled photons, adaptive Quantum Bit Error Rate (QBER) optimization, and hybrid quantum-classical authentication anchored with blockchain. This advancement addresses critical limitations of traditional QKD systems—including high sensitivity to ambient interference, limited scalability, and high error rates—by employing entanglement-based noise resilience and real-time detection threshold control. Existing approaches to QoS and security—such as RSA-based encryption, BB84 and E91 protocols, and VLC-oriented modulation techniques—struggle with low secure key rates, high QBER under ambient light, and vulnerability to man-in-the-middle (MITM) attacks. EP-QKD overcomes these limitations by enhancing photon distribution fidelity, enabling decentralized authentication, and sustaining low error rates across dynamic urban optical environments. Experimental results demonstrate that EP-QKD achieves an average QBER of 1.2% and maintains QBER below 2.5% at 1000 lux, reducing errors by 40% over BB84 and 60% over E91. It attains secure key generation rates of over 1200 Mbps at 5 meters, outperforming BB84 and E91 by a significant margin. At 20 meters, EP-QKD maintains 300 Mbps, while BB84 drops to 40 Mbps. The MITM attack success rate is reduced to 0.01%, compared to 15% with BB84 + RSA. Additionally, EP-QKD achieves energy efficiency of 0.7 nJ/bit—nearly half of BB84’s requirement—making it suitable for energy-constrained IoT and mobile systems. Multi-user scalability testing confirms that EP-QKD can support up to 50 concurrent users with a key rate above 80 Mbps per user and QBER < 1.8%. A 3D analysis further shows that QBER remains below 5% even at 50 meters, demonstrating the framework’s robustness for long-distance secure optical communication. Overall, the proposed EP-QKD framework significantly enhances the performance, scalability, and resilience of quantum communication systems, making it a viable candidate for 6 G, IoT, and satellite-based secure infrastructure.

Abstract The Byzantine Agreement (BA) problem is a fundamental challenge in distributed systems, focusing on achieving reaching an agreement among parties, some of which may behave maliciously. With the rise of cryptocurrencies, there has been significant interest in developing atomic broadcast protocols, which facilitate agreement on a subset of parties’ requests. However, these protocols often come with high communication complexity ( $$O(ln^2 + \lambda n^3 \log n)$$ , where l is the bit length of the input, n is the number of parties, and λ represents the security parameter bit length). This can lead to inefficiency, especially when the requests across parties exhibit little variation, resulting in unnecessary resource consumption. In this paper, we introduce Slim-ABC, a novel atomic broadcast protocol that eliminates the $$O(ln^2 + \lambda n^3 \log n)$$ term associated with traditional atomic broadcast protocols. While Slim-ABC reduces the number of accepted requests, it significantly mitigates resource wastage, making it more efficient. The protocol leverages the asynchronous common subset and provable-broadcast mechanisms to achieve a communication complexity of $$O(ln^2 + \lambda n^2)$$ . Despite the trade-off in accepted requests, Slim-ABC maintains robust security by allowing only a fraction ( f +1) of parties to broadcast requests. We present an extensive efficiency analysis of Slim-ABC, evaluating its performance across key metrics such as message complexity, communication complexity, and time complexity. Additionally, we provide a rigorous security analysis, demonstrating that Slim-ABC satisfies the agreement , validity , and totality properties of the asynchronous common subset protocol.

The convergence of blockchain, artificial intelligence (AI), and cloud computing is catalyzing a paradigm shift in developing secure, intelligent, and scalable digital infrastructures. This triad of technologies is increasingly utilized to improve performance, transparency, and trust in engineering-driven and socio-technical environments. This study systematically reviews the evolution, integration strategies, and applications of blockchain, AI, and cloud computing in digital ecosystems. The analysis is based on 108 peer-reviewed studies spanning the years 2012 to 2025. A comprehensive literature analysis was conducted to identify trends, synergies, and sector-specific implementations of these systems. The review explores how their integration supports real-world engineering and operational use cases. Blockchain contributes to decentralized architectures, secure data exchange, and identity verification. AI supports adaptive behavior, autonomous decision-making, and predictive analytics. Cloud computing offers the scalable infrastructure necessary for deployment. Key challenges addressed include interoperability, latency, security trade-offs, and resource allocation. Use cases in digital finance, supply chain management, and industrial automation demonstrate the effectiveness of this integration in building resilient, ethically aligned, and high-performance infrastructures. The findings offer valuable insights and technical considerations for engineers and architects seeking to design next-generation cyber-physical systems that are secure, intelligent, and socially responsive. Clinical Trial Number Not applicable. This article accentuates the extensive use of blockchain, cloud computing, and AI principles, emphasizing their crucial significance in the contemporary digital environment and paths in which blockchain technology may be employed to automate financial transactions, enhance the security of personal data and records, facilitate cross-border transactions, verify identities, execute smart contracts, utilize cryptocurrencies, and manage supply chains. This research systematically assesses the development and practical application of blockchain, cloud computing, and artificial intelligence technologies over the past decade. This study primarily focuses on monitoring the progress, practical applications, and benefits of blockchain technology in the context of financial transactions. This article probes the symbiotic relationship between artificial intelligence, cloud and blockchain technology, analyzing the evolution and fundamental concepts of their integration from 2012 to 2025. This paper also presents a detailed analysis of 108 research works acquitted over the past decade. The study provides a comprehensive examination of the current state and fundamental principles related to the integration of artificial intelligence, cloud, and blockchain technology. This paper offers a comprehensive analysis of the major obstacles confronted by blockchain technology, examining its wide-ranging applications over several industries and its socko role in transaction systems and the domain of cryptocurrencies.

Mobile Social Networks (MSNs) consist of numerous mobile nodes that exhibit social characteristics such as gender, age, and more. Nowadays, with the rise in popularity of smartphones, these devices serve as nodes in mobile social networks, inheriting their users’ characteristics. These networks utilize a “store-carry-and-forward” mechanism for transmitting and delivering packets. New applications, like smart city monitoring, necessitate the deployment of sensors in urban areas to gather relevant information. The sensed data must be collected through mobile nodes (in this case, smartphones) and transmitted to a base station or other interested nodes. In these applications, if the data collection period is brief, smartphones will experience high energy consumption, and a significant amount of redundant data will be produced. Conversely, if the collection period is extended, some data may be lost. Several schemes have been proposed for data collection in wireless sensor networks. Unfortunately, in these schemes, nodes are either constantly in the data collection phase or gather data at fixed time intervals through simple scheduling. It appears that adaptive data collection based on the differences in the collected data could be more effective. This paper proposes a new Data Gathering Scheduler based on the Differences in collected data, DGSD. In this method, if the difference between the last two data points is low, the next time slot is set to be longer; as the difference between the last two data points increases, the time slot is shortened. Simulation results indicate that energy consumption with DGSD improves when compared to related works in discovering the same number of events.

Due to the Internet’s rapid expansion, substantial user population, and growing reliance on broadband networks, congestion is one of the most significant network challenges. Congestion occurs when the number of packets on link exceeds the network’s capacity. Wireless Mesh Networks (WMNs) belong to self-configuring, multi-hop, and self-healing network types and are simple to use. The present research provides a novel reinforcement-based TCP-Int (Intelligent TCP) method aimed at improving WMN performance, in which the size of the congestion window is maintained to preserve WMN performance while maintaining throughput. The peculiarity of this work is that it integrates the recently published Open AI gym library, ns3-gym, with a conventional network simulator, ns3. This connection enables the Reinforcement Learning (RL) agent to interact with and learn from the environment provided by ns3. In this work, TCP variations such as TCP New Reno, TCP Yeah, TCP Vegas, TCP Hybla, and TCP High-speed are implemented in the ns3 simulator. These algorithms generate the data (throughput), an important performance indicator of the algorithm. The proposed TCP-Int shows an improvement in the throughput of the WMN of 81 %. This result shows that by regulating the CWND size using the TCP-Int, the wireless mesh network’s performance can be improved significantly.

In the never-ending battle against rising malware threats, cybersecurity professionals were constantly challenged by malware researchers. Businesses and institutions that have fallen prey to these threats that have suffered significant financial losses and enormous disruption to countless lives. As a result, security approaches have evolved to include preemptive measures such as the widespread use of HoneyPots. However, data-driven decision-making was required to improve the effectiveness of such approaches. Therefore, this paper describes a quantitative analysis that assesses various malware samples using system metrics and network log data. The goal is to properly visualise this information and analyse if it can aid in decision-making processes, ultimately leading to the construction of more robust and secure networks. To help with this research, a dashboard application was created that allows the installation of virtual machines, the configuration of virtual networks, and the collection of system metric data from outside sources. The findings of this paper can help greatly improve network security and stay ahead of threats in the cat-and-mouse game.

Rendering circles on digital platforms is essential in computer graphics. The widely adopted midpoint circle algorithm is a premier tool for this task. This article dives deep into the foundational aspects of this algorithm, highlighting its prowess in enabling accurate and effortless circle depiction on digital displays. It further contrasts its merits with alternative circle drawing techniques. Importantly, our research presents a refined technique aimed at minimizing inaccuracies inherent in the standard cartesian-based Midpoint Circle Drawing Algorithm (MPCDA). By incorporating an error-reduction strategy, we ensure greater accuracy in depicting circles. Comparative tests on diverse circle sizes and placements confirmed the efficacy of our enhanced method. When set against results from the traditional MPCDA, our approach showcases significant improvements.

Binary search trees (BSTs) are one of the most important data structures in the field of computer science. We may easily write a parallel construction program of a BST by extending the sequential algorithm straightly. However, in such conventional approaches, the order of nodes inserted into a BST is determined dynamically, depending on the occasional state of the parallel thread execution. It results in a BST with a different structure (node position) generated on every execution of the parallel program. On the other hand, we have been developing parallel construction schemes of the BST with the same structure as a BST generated by the sequential algorithm. One is the speculatively parallel construction of a BST. And another is the purely (non-speculatively) parallel construction, but it was derived through the concept of thread-level speculation. This paper evaluates the performances of those construction schemes on several types of shared-memory multiprocessors. For the large enough size of BST, our new parallel programs can construct a BST with always the same structure on a little lower or sometimes higher performance than the program that makes a BST with a different structure on every execution. And in contrast with the general expectation that simply enlarging the size of parallel tasks increases misspeculation and damages the performance, we found that it sometimes enhances the performance of speculatively parallel execution.

In a medical emergency situation, real-time patient data sharing may improve the survivability of a patient. In this paper, we explore how Digital Twin (DT) technology can be used for real-time data storage and processing in emergency healthcare. We investigated various enabling technologies, including cloud platforms, data transmission formats, and storage file formats, to develop a feasible DT storage solution for emergency healthcare. Through our analysis, we found Amazon AWS to be the most suitable cloud platform due to its sophisticated real-time data processing and analytical tools. Additionally, we determine that the MQTT protocol is suitable for real-time medical data transmission, and FHIR is the most appropriate medical file storage format for emergency healthcare situations. We propose a cloud-based DT storage solution, in which real-time medical data are transmitted to AWS IoT Core, processed by Kinesis Data Analytics, and stored securely in AWS HealthLake. Despite the feasibility of the proposed solution, challenges such as insufficient access control, lack of encryption, and vendor conformity must be addressed for successful practical implementation. Future work may involve Hyperledger Fabric technology and HTTPS protocol to enhance security, while the maturation of DT technology is expected to resolve vendor conformity issues. By addressing these challenges, our proposed DT storage solution has the potential to improve data accessibility and decision-making in emergency healthcare settings.