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    A Comprehensive Survey on Resource Management in 6G Network Based on Internet of Things
    (Institute of Electrical and Electronics Engineers Inc., 2024) Sefati, Seyed Salar; Haq, Asim Ul; Nidhi; Craciunescu, Razvan; Halunga, Simona; Mihovska, Albena; Fratu, Octavian
    The transition to 6th Generation (6G) cellular networks offers significant improvements over 5th Generation (5G), enhancing data transfer, reducing latency, and improving network reliability. Advanced Multiple-Input Multiple-Output (MIMO) technology in 6G boosts network efficiency, particularly benefiting Ultra-Reliable Low-Latency Communications (URLLC). This paper reviews literature on resource management in the Internet of Things (IoT) within the 6G context. We categorize the study into four segments: network-aware resource management, dynamic resource allocation, predictive resource distribution based on traffic and architecture, and energy-centric resource allocation considering IoT device mobility and location. We provide a detailed perspective on current research and highlight future research avenues. Key contributions include a comparative study of IoT resource management techniques, an overview of resource management across LTE, 5G, and 6G networks, insights into applications like Intelligent Transportation Systems (ITS), Industrial IoT (IIoT), and Mobile CrowdSensing (MCS), and an emphasis on upcoming challenges. We emphasize the crucial role of efficient resource management in IoT, particularly in the 6G landscape. © 2013 IEEE.
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    A Bioinspired Test Generation Method Using Discretized and Modified Bat Optimization Algorithm
    (Mdpi, 2024) Arasteh, Bahman; Arasteh, Keyvan; Kiani, Farzad; Sefati, Seyed Salar; Fratu, Octavian; Halunga, Simona; Tirkolaee, Erfan Babaee
    The process of software development is incomplete without software testing. Software testing expenses account for almost half of all development expenses. The automation of the testing process is seen to be a technique for reducing the cost of software testing. An NP-complete optimization challenge is to generate the test data with the highest branch coverage in the shortest time. The primary goal of this research is to provide test data that covers all branches of a software unit. Increasing the convergence speed, the success rate, and the stability of the outcomes are other goals of this study. An efficient bioinspired technique is suggested in this study to automatically generate test data utilizing the discretized Bat Optimization Algorithm (BOA). Modifying and discretizing the BOA and adapting it to the test generation problem are the main contributions of this study. In the first stage of the proposed method, the source code of the input program is statistically analyzed to identify the branches and their predicates. Then, the developed discretized BOA iteratively generates effective test data. The fitness function was developed based on the program's branch coverage. The proposed method was implemented along with the previous one. The experiments' results indicated that the suggested method could generate test data with about 99.95% branch coverage with a limited amount of time (16 times lower than the time of similar algorithms); its success rate was 99.85% and the average number of required iterations to cover all branches is 4.70. Higher coverage, higher speed, and higher stability make the proposed method suitable as an efficient test generation method for real-world large software.
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    Cache Aging with Learning (CAL): A Freshness-Based Data Caching Method for Information-Centric Networking on the Internet of Things (IoT)
    (Multidisciplinary Digital Publishing Institute (MDPI), 2025) Hazrati, Nemat; Pirahesh, Sajjad; Arasteh, Bahman; Sefati, Seyed Salar; Fratu, Octavian; Halunga, Simona
    Information-centric networking (ICN) changes the way data are accessed by focusing on the content rather than the location of devices. In this model, each piece of data has a unique name, making it accessible directly by name. This approach suits the Internet of Things (IoT), where data generation and real-time processing are fundamental. Traditional host-based communication methods are less efficient for the IoT, making ICN a better fit. A key advantage of ICN is in-network caching, which temporarily stores data across various points in the network. This caching improves data access speed, minimizes retrieval time, and reduces overall network traffic by making frequently accessed data readily available. However, IoT systems involve constantly updating data, which requires managing data freshness while also ensuring their validity and processing accuracy. The interactions with cached data, such as updates, validations, and replacements, are crucial in optimizing system performance. This research introduces an ICN-IoT method to manage and process data freshness in ICN for the IoT. It optimizes network traffic by sharing only the most current and valid data, reducing unnecessary transfers. Routers in this model calculate data freshness, assess its validity, and perform cache updates based on these metrics. Simulation results across four models show that this method enhances cache hit ratios, reduces traffic load, and improves retrieval delays, outperforming similar methods. The proposed method uses an artificial neural network to make predictions. These predictions closely match the actual values, with a low error margin of 0.0121. This precision highlights its effectiveness in maintaining data currentness and validity while reducing network overhead. © 2025 by the authors.
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    Cybersecurity in a Scalable Smart City Framework Using Blockchain and Federated Learning for Internet of Things (IoT)
    (Multidisciplinary Digital Publishing Institute (MDPI), 2024) Sefati, Seyed Salar; Craciunescu, Razvan; Arasteh, Bahman; Halunga, Simona; Fratu, Octavian; Tal, Irina
    Highlights: What are the main findings? Implementation of blockchain enhances the security and scalability of smart city frameworks. Federated Learning enables efficient and privacy-preserving data sharing among IoT devices. What are the implications of the main finding? The proposed framework significantly reduces the risk of data breaches in smart city infrastructures. Improved data privacy and security can foster greater adoption of IoT technologies in urban environments. Smart cities increasingly rely on the Internet of Things (IoT) to enhance infrastructure and public services. However, many existing IoT frameworks face challenges related to security, privacy, scalability, efficiency, and low latency. This paper introduces the Blockchain and Federated Learning for IoT (BFLIoT) framework as a solution to these issues. In the proposed method, the framework first collects real-time data, such as traffic flow and environmental conditions, then normalizes, encrypts, and securely stores it on a blockchain to ensure tamper-proof data management. In the second phase, the Data Authorization Center (DAC) uses advanced cryptographic techniques to manage secure data access and control through key generation. Additionally, edge computing devices process data locally, reducing the load on central servers, while federated learning enables distributed model training, ensuring data privacy. This approach provides a scalable, secure, efficient, and low-latency solution for IoT applications in smart cities. A comprehensive security proof demonstrates BFLIoT’s resilience against advanced cyber threats, while performance simulations validate its effectiveness, showing significant improvements in throughput, reliability, energy efficiency, and reduced delay for smart city applications. © 2024 by the authors.
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    A Hybrid Heuristic Algorithm Using Artificial Agents for Data Replication Problem in Distributed Systems
    (Mdpi, 2023) Arasteh, Bahman; Sefati, Seyed Salar; Halunga, Simona; Fratu, Octavian; Allahviranloo, Tofigh
    One of the key issues with large distributed systems, such as IoT platforms, is gaining timely access to data objects. As a result, decreasing the operation time of reading and writing data in distributed communication systems become essential demands for asymmetric system. A common method is to replicate the data objects across multiple servers. Replica placement, which can be performed statically or dynamically, is critical to the effectiveness of distributed systems in general. Replication and placing them on the best available data servers in an optimal manner is an NP-complete optimization problem. As a result, several heuristic strategies for replica placement in distributed systems have been presented. The primary goals of this research are to reduce the cost of data access time, reduce the number of replicas, and increase the reliability of the algorithms for placing replicas. In this paper, a discretized heuristic algorithm with artificial individuals and a hybrid imitation method were developed. In the proposed method, particle and gray-wolf-based individuals use a local memory and velocity to search for optimal solutions. The proposed method includes symmetry in both local and global searches. Another contribution of this research is the development of the proposed optimization algorithm for solving the data object replication problem in distributed systems. Regarding the results of simulations on the standard benchmark, the suggested method gives a 35% reduction in data access time with about six replicates. Furthermore, the standard deviation among the results obtained by the proposed method is about 0.015 which is lower than the other methods in the same experiments; hence, the method is more stable than the previous methods during different executions.
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    Meet User's Service Requirements in Smart Cities Using Recurrent Neural Networks and Optimization Algorithm
    (Ieee-Inst Electrical Electronics Engineers Inc, 2023) Sefati, Seyed Salar; Arasteh, Bahman; Halunga, Simona; Fratu, Octavian; Bouyer, Asgarali
    Despite significant advancements in Internet of Things (IoT)-based smart cities, service discovery, and composition continue to pose challenges. Current methodologies face limitations in optimizing Quality of Service (QoS) in diverse network conditions, thus creating a critical research gap. This study presents an original and innovative solution to this issue by introducing a novel three-layered recurrent neural network (RNN) algorithm. Aimed at optimizing QoS in the context of IoT service discovery, our method incorporates user requirements into its evaluation matrix. It also integrates long short-term memory (LSTM) networks and a unique black widow optimization (BWO) algorithm, collectively facilitating the selection and composition of optimal services for specific tasks. This approach allows the RNN algorithm to identify the top-K services based on QoS under varying network conditions. Our methodology's novelty lies in implementing LSTM in the hidden layer and employing backpropagation through time (BPTT) for parameter updates, which enables the RNN to capture temporal patterns and intricate relationships between devices and services. Further, we use the BWO algorithm, which simulates the behavior of black widow spiders, to find the optimal combination of services to meet system requirements. This algorithm factors in both the attractive and repulsive forces between services to isolate the best candidate solutions. In comparison with existing methods, our approach shows superior performance in terms of latency, availability, and reliability. Thus, it provides an efficient and effective solution for service discovery and composition in IoT-based smart cities, bridging a significant gap in current research.