Conference Papers

Permanent URI for this collectionhttps://idr.nitk.ac.in/handle/123456789/28506

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Subject: search.filters.subject.Matching Theory

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    A Novel Method for Energy Trading in Networked Microgrids Using Matching Theory
    (Institute of Electrical and Electronics Engineers Inc., 2022) Biji Varghese, K.V.; Gaonkar, D.N.
    This Paper proposes matching based energy trading mechanism among microgrids to trade energy flexibly in a networked distribution system. We consider an interconnected microgrid network system where some microgrids have an excess of power after their local utilization to sell, termed as sellers, whereas some other microgrids request additional power to meet demand. Energy trading is formulated as a matching problem where the sellers find suitable matches based on the sellers' preference matrix. The preference matrix is generated using the price proposed by the buyer microgrids. The buyer microgrids calculate the trading prices by considering the operational cost, virtual cost and generation cost, and the seller microgrids accept the proposal only if it's greater than the minimum trading price calculated by the seller. We also analyze that due to the power transfer between the microgrids instead of the utility grid., there is a reduction in power loss across the networked system during the power transfer We study the proposed theory on the distribution network containing different numbers of microgrids, and the numerical results are compared with the conventional energy trading methods to verify the effectiveness of the proposed approach. © 2022 IEEE.
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    LBA: Matching Theory Based Latency-Sensitive Binary Offloading in IoT-Fog Networks
    (Institute of Electrical and Electronics Engineers Inc., 2024) Soni, P.; Deshlahre, O.C.; Satpathy, A.; Addya, S.K.
    The Internet of Things (IoT) is growing more popular with applications like healthcare services, traffic monitoring, video streaming, smart homes, etc. These applications produce an enormous amount of data, so a realistic option in this instance is to offload computational tasks to their proximity fog nodes (FNs) instead of the remote cloud. However, a negligent offloading strategy may cause anomalous computational traffic load at the FNs, causing congestion that may adversely affect the latency. However, the latency of task flows from IoT devices comprises communications latency at BS and computational latency at FNs. Therefore, designing offloading algorithms to distribute the computational load at FN evenly and efficiently utilize the FN resources is crucial. To solve this problem, we proposed LBA in a fog network with a binary offloading strategy using the matching theory-based approach. We utilize the Analytic Hierarchy Process (AHP) to generate the preference list. Furthermore, the binary offloading technique follows the deferred acceptance algorithm (DAA) to produce a stable assignment, and the complete offloading problem is modeled as a one-to-many matching game. Comprehensive simulations ensure that LBA can accomplish a better-balanced assignment for homogeneous and heterogeneous input concerning all the baseline algorithms. © 2024 IEEE.