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RISS 인기검색어
- 검색키워드
- 저자 : Arslan Musaddiq (검색결과 4 건)
- 무료
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Federated Learning Framework for Intelligent IoT Networks
Arslan Musaddiq,Tariq Rahim,Dong-Seong Kim 한국통신학회 2021 한국통신학회 학술대회논문집 Vol.2021 No.6
The use of Internet of Things (IoT) devices has increased significantly due to their diverse application areas. The IoT sensors are normally deployed in a complicated environment. Maintaining these tiny devices is challenging and often incurs high system costs. These devices are expected to handle data processing and communication tasks independently. For network layer communication, a reinforcement learning mechanism is utilized to generate routing table entries intelligently. The Q-values in RL algorithm may have error variance in nodes having similar environmental conditions. Thus, federated reinforcement learning (FRL) is proposed to represents the fair estimation of Q-value. The proposed FRL mechanism enhances the communication capabilities of IoT networks. The performance evaluation of the proposed mechanism is provided through Contiki 3.0 Cooja simulation.
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Deep Learning-based Colorectal Cancer Detection in Endoscopic Images
Tariq Rahim,Arslan Musaddiq,Dong Seong Kim 한국통신학회 2021 한국통신학회 학술대회논문집 Vol.2021 No.6
Colorectal cancer (CRC) is the most prevalent cancer found in the small bowel of the human gastrointestinal (GI) tract. Polyps are antecedents to CRC and are detected in approximately half of the people at age 50 within the GI. In this paper, an improved version of You Only Live Once (YOLO) is presented for the detection of polyp within the endoscopic images. We have improved the YOLOv3-tiny model by adding more convolutional layers to extract enriched and deeper features. For fair benchmarking, the efficacy of the proposed model is evaluated against the default version of YOLOv3-tiny in terms of recall, precision, F1-score, and F2-score.
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LWA in 5G: State-of-the-Art Architecture, Opportunities, and Research Challenges
Bajracharya, Rojeena,Shrestha, Rakesh,Ali, Rashid,Musaddiq, Arslan,Kim, Sung Won Institute of Electrical and Electronics Engineers 2018 IEEE communications magazine Vol.56 No.10
<P>To cater to the exponential increase in wireless data demands under limited availability of licensed spectrum, the Federal Communications Commission has released extra bandwidth of 295 MHz in the 5 GHz unlicensed national information infrastructure bands for wireless communications. This free, unlicensed band has drawn considerable attention from academia and cellular operators worldwide. Several standards are being developed for flexible integration, aggregation, and interworking of this unlicensed band with licensed networks or spectrum. Of many candidate approaches, in this article, we introduce LTE WLAN aggregation (LWA). LWA, capable of leveraging the LTE and WLAN spectra simultaneously, has emerged as a prominent solution to increase network capacity and enhanced end users' quality of experience. Further, we present latest advances in this exciting technology by reviewing the state-of-the-art LWA architecture, and identify several opportunities and open challenges related to LWA design for future research.</P>
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Hoa Tran-Dang,Sanjay Bhardwaj,Tariq Rahim,Arslan Musaddiq,Dong-Seong Kim 한국통신학회 2022 Journal of communications and networks Vol.24 No.1
In the IoT-based systems, the fog computing allowsthe fog nodes to offload and process tasks requested from IoTenableddevices in a distributed manner instead of the centralizedcloud servers to reduce the response delay. However, achievingsuch a benefit is still challenging in the systems with high rate ofrequests, which imply long queues of tasks in the fog nodes, thusexposing probably an inefficiency in terms of latency to offloadthe tasks. In addition, a complicated heterogeneous degree inthe fog environment introduces an additional issue that many ofsingle fogs can not process heavy tasks due to lack of availableresources or limited computing capabilities. Reinforcement learningis a rising component of machine learning, which providesintelligent decision making for agents to response effectively tothe dynamics of environment. This vision implies a great potentialof application of RL in the concept of fog computing regardingresource allocation for task offloading and execution to achievethe improved performance. This work presents an overview of RLapplications to solve the resource allocation related problems inthe fog computing environment. The open issues and challengesare explored and discussed for further study.
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