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Smart Agent based Dynamic Data Aggregation for Delay Sensitive Smart City Services
Md. Shirajum Munir(엠디 시라줌 무니르),Sarder Fakhrul Abedin(살더 파크룰 아베딘),Md. Golam Rabiul Alam(엠디 골람 라비울 알람),Do Hyeon Kim(김도현),Choong Seon Hong(홍충선) Korean Institute of Information Scientists and Eng 2018 정보과학회논문지 Vol.45 No.4
Smart city is the vision of modern intelligent technology toward the sustainable development of green technology and social development. Smart services e.g. smart transportation, smart health, smart home, smart grid, smart security, and IoT based applications are the key enablers of smart city, that ensure the quality life and well-being. In a bid to ensure the functionalities of those services, the IoT applications gather data from numerous IoT nodes. In such a case, it becomes more challenging to managing huge network traffic in the centralized network of smart city. Therefore, in this research, we have focused on the resolution of this problem through the introduction of of smart agent-based dynamic data aggregation (DDA) from distributed dense smart city network for city service fulfillment. In this research study, we purposed to model a peer to peer fully distributed system using distributed hash table chord protocol. We also proposed an algorithm for the IoT network and designed smart agent based IoT node searching algorithm for crowd sourcing. Finally, we simulated the result of the proposed smart agent based dynamic data aggregation model in an effort to achieve a higher performance gain for the proposed approach in respect to service fulfillment time and convergence.
A Deep Learning Approach for Target-oriented Communication Resource Allocation in Holographic MIMO
Apurba Adhikary(아푸르보 아디히카리),Md. Shirajum Munir(엠디 시라줌 무니르),Avi Deb Raha(아비 데브 라하),Min Seok Kim(김민석),Jong Won Choe(최종원),Choong Seon Hong(홍충선) Korean Institute of Information Scientists and Eng 2023 정보과학회논문지 Vol.50 No.5
In this paper, we propose a single-cell massive multiple-input multiple-output (mMIMO) system assisted with holography that performs target-oriented communication resource allocation for heterogeneous users. This paper proposes a technique that can minimize the number of active grids from holographic grid arrays (HGA) for confirming the requirement of lower power toward beamforming to serve target-oriented users. Therefore, we formulated a problem by maximizing the signal-to-interference-noise ratio (SINR), which, in turn, maximizes the efficient resource allocation for the users by generating effective beamforming and controlling the sum-power rule. Additionally, our holography-assisted mMIMO system is capable of serving heterogeneous user equipment simultaneously with a lower power budget. To devise the artificial intelligence (AI)-based solution, we developed a sequential neural network model for grid activation decisions with minimized power constraint. Finally, the simulation and performance evaluation results show that power was allocated efficiently, and effective beams were formed for serving the users with a lower RMSE score of 0.01.
A Transport Theoretic Approach for Computational Task Migration in Multi-Access Edge Computing
Sarder Fakhrul Abedin(사르더 파쿠룰 아베딘),Md. Shirajum Munir(엠디 시라줌 무니르),SeokWon Kang(강석원),Choong Seon Hong(홍충선) Korean Institute of Information Scientists and Eng 2019 정보과학회논문지 Vol.46 No.10
In the present work, the problem of computational task migration in the Multi-Access Edge Computing (MEC) Network has been addressed and the goal is to minimize the computational cost including the task migration cost of the MEC network. Apparently, at first, we have formulated a Hitchcock-Koopmans transportation problem, which corresponds to the task migration from the over-utilized MEC servers to the under-utilized MEC server. Second, we have solved the transportation problem using the Vogel’s Approximation Algorithm (VAM), where the optimal task migration was achieved. Finally, in the simulation, we have demonstrated that the proposed approach significantly outperforms the baseline approach in terms of the task migration cost, average response time, and average queuing delay in the MEC network.