Department of Computer Science and Information Systems

Permanent URI for this collectionhttp://localhost:4000/handle/123456789/1928

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Subject: search.filters.subject.Optimization

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Now showing 1 - 5 of 5
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    Efficient routing for QKD network using novel quantum optimization approach
    (IEEE, 2025) Bitragunta, Sainath; Bhatia, Ashuthosh
    With exponential growth and associated milestones set in quantum information and quantum computing (QC) technologies, QC is becoming a threat to existing key encryption strategies that leverage asymmetric cryptographic algorithms like RSA (Rivest, Shamir, Adleman) encryption. Since these algorithms form the backbone of Internet communication, it becomes essential to utilize secure quantum methods for key generation and distribution. The quantum key distribution (QKD) networks have since been extensively researched and implemented with various communication protocols, primarily utilizing the Quantum Entanglement and Quantum Key Correction paradigms. Efficient routing is one of the significant problems in classical and hybrid networks. It is important to propose novel hybrid and efficient routing protocols based on modern optimization approaches to design secure, fidelitous, and efficient quantum information networks. We perform this optimization by generating a cost function to implement quantum optimization algorithms, namely the Quantum Approximate Optimization Algorithm (QAOA). We further draw a comparison with the state-of-the-art graph theory-based optimization techniques. The primary objective of this paper is to fabricate a robust quantum communication network and to subsequently analyze the effectiveness of quantum based techniques to carry out network routing and link optimization, generating scope for the utilization of quantum architecture to enhance security in Q KD networks.
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    Fuzzy Logic and AI-Powered, SDR Relay for Secure and Efficient Cooperative Radio Communication
    (IEEE, 2024) Bitragunta, Sainath; Bhatia, Ashuthosh
    In this article, we develop a novel approach that leverages the capabilities of fuzzy logic and artificial intelligence (AI) to develop an intelligent, efficient cooperative RCN. Software defined radio (SDR) is flexible, scalable, and reconfigurable. Considering heterogeneous radio communication networks (RCNs), conventional relays do not perform well due to their limitations (security vulnerabilities in cooperative Internet-of-Things (IoT), inefficiencies in half-duplex relaying, etc.). We propose an AI-powered, fuzzy logic-based SDR relay to address these issues. These intelligent relays could be useful and outperform conventional relays due to their adaptability and reconfigurabilty, with added intelligence based on AI and fuzzy logic. The proposed next generation SDR relays offer significant advantages over traditional relays and have the potential to revolutionize the field of radio communication. Specifically, we analyze the decimation technique in SDR signal-to-interference plus noise ratio (SINR) resampler, Mamdani fuzzy logic controller, and use a machine learning (ML) model that uses RADIOML data set. Based on the simulation results, we show that applying fuzzy logic with an ML-enabled SDR relay could improve energy efficiency and reliability performance in advanced radio networks.
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    Fair Scheduling of Concurrent Transmissions in Directional Antenna Based WPANs/WLANs
    (IEEE, 2018-07) Rajya Lakshmi, L.
    With their capability to support high data rates, millimeter-Wave (mmWave) communications are evolving as a promising and potential technology to support high data rate applications in short range networks. This paper addresses the problem of fair scheduling in mmWave wireless personal and local area networks (WPANs/WLANs) to support applications with varying quality of service (QoS) requirements. To ensure fairness while exploiting the spatial reuse facilitated by directional antennas, concurrent transmission scheduling in mmWave WPANs/WLANs is formulated as a multi-objective optimization problem. Two heuristic schedulers are developed to obtain a schedule in real-time. These schedulers first satisfy the minimum QoS requirements of as many flows as possible, and then, allocate the remaining bandwidth to various flows while ensuring long-term and short-term fairness among the flows. Results from extensive simulations conducted in a dense mmWave WPAN show that the proposed fair schedulers provide better fairness and throughput, compared to existing methods.
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    Achieving Fairness in IEEE 802.11ah Networks for IoT Applications with Different Requirements
    (IEEE, 2019-07) Rajya Lakshmi, L.
    The IEEE 802.11ah standard can provide cost-effective Internet access to a large number of devices in newly evolving Internet-of-Things (IoT) and machine-to-machine (M2M) networks. To handle high collision probability caused by a large number of devices, it adopts a group-based protocol at the MAC layer and divides nodes (or sensors) into a number of groups. The formed groups may not be uniform in terms of data rate requirements, since each group is a combination of sensors with different traffic characteristics. To achieve fair resource utilization across the groups which in turn maximizes the channel utilization, this paper formulates fair grouping in IEEE 802.11ah networks as an optimization problem, and we develop a heuristic method to solve the problem in real-time. In addition, to ensure fair channel utilization by the nodes in each group, a contention window selection and adjustment method is proposed. Results from extensive simulations conducted in a dense IoT network show that the proposed fairness model achieves a superior performance than the existing methods in terms of throughput, packet delay, energy efficiency, and fairness.
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    Achieving Fairness in IEEE 802.11ah Networks for IoT Applications with Different Requirements
    (IEEE, 2019) Lakshmi, L.R.
    The IEEE 802.11ah standard can provide cost-effective Internet access to a large number of devices in newly evolving Internet-of-Things (IoT) and machine-to-machine (M2M) networks. To handle high collision probability caused by a large number of devices, it adopts a group-based protocol at the MAC layer and divides nodes (or sensors) into a number of groups. The formed groups may not be uniform in terms of data rate requirements, since each group is a combination of sensors with different traffic characteristics. To achieve fair resource utilization across the groups which in turn maximizes the channel utilization, this paper formulates fair grouping in IEEE 802.11ah networks as an optimization problem, and we develop a heuristic method to solve the problem in real-time. In addition, to ensure fair channel utilization by the nodes in each group, a contention window selection and adjustment method is proposed. Results from extensive simulations conducted in a dense IoT network show that the proposed fairness model achieves a superior performance than the existing methods in terms of throughput, packet delay, energy efficiency, and fairness.