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Deep Residue Network Based Modulation Classification for Industrial Wireless Networks
Sanjay Bhardwaj,Jae-Min Lee(이재민),Dong-Seong Kim(김동성) 한국통신학회 2021 한국통신학회 학술대회논문집 Vol.2021 No.2
This paper proposes a efficient deep residue network (E-DRN) for automatic modulation classification (AMC) for industrial wireless networks. The proposed architecture is designed with several residual layers with skip connections so as to preserve more residual informations and thus preventing vanishing gradient problems. Challenging dataset DeepSig:RadioML (2018.01A), is used for the performance analysis. Simulation results show that the classification accuracy performance of the proposed model is outstanding in the signal-to-noise ratio (SNR) range. A comparison in terms of computing time with previous works is also carried out.
Blockchain Technology for Industrial Internet of Things Based on Artificial Intelligence
Rubina Akter,Sanjay Bhardwaj,Jae Min Lee,Dong-Seong Kim 한국통신학회 2019 한국통신학회 학술대회논문집 Vol.2019 No.6
This paper proposes an energy efficient and highly secured blockchain based industrial internet of things (IIoT) system, which is distributed in nature and offer a new direction for the development of IIoT network. Typical IIoT model is based on the centralized architectural scheme, however it is fragile in terms of power consumption and secured transaction, where blockchain can be a possible solution. In this paper, we give the solution of data security and efficient power consuming mechanism through modified proof-of-work (PoW) mechanism and proper data integration mechanism. This paper analyses the dissimilar impact of IoT technology in the area of artificial intelligence. Finally the simulation results show that blockchain based IIoT scheme is more efficient than traditional static architecture in terms of throughput, latency, and security.
Enhanced Faulty Node Detection with Interval Weighting Factor for Distributed Systems
RIESA KRISNA ASTUTI SAKIR,Sanjay Bhardwaj,김동성 한국통신학회 2021 Journal of communications and networks Vol.23 No.1
This paper proposes an enhanced faulty node detectionmethod using interval weighting factor, which monitors nodebehavior using pseudo-random Bose-Chaudhuri-Hocquenghem(BCH) code for distributed networked control systems. Masternode collects the replacement of the cyclic redundancy check(CRC) codes by a single-bit BCH code of each slave node. However,BCH code can only obtain error position of the suspectedfaulty node without consideration of channel nodes. Hence, suspectederror nodes are saved within the detected error interval andnormalized using the weighting factor, which is carried out duringsequential check, for interpretation. Fault judgement is carried outto adequately interpret the data, to guarantee detection accuracy ofthe detected error. The data is represented by statistical characteristicof the raw data and filtered data. This scheme can be appliedto detect and prevent the severe damage by node failure. The simulationresults prove the effectiveness of the interval faulty weightingfactor, in obtaining raw data from the monitoring of the BCH codeand filtered data, which are more accurate representation than theraw data. Moreover, the characteristics of the observed data wereverified as the evaluation result of the suspected faulty node.
Reducing Faulty Node Detection Delay in Industrial Internet of Things
Alifia Putri Anantha,Sanjay Bhardwaj,Jae-Min Lee,Dong-Seong Kim 한국통신학회 2019 한국통신학회 학술대회논문집 Vol.2019 No.6
In industrial internet of things (IIoT), some data obtained may experience problems during the communication process between nodes that cause high latency and network congestion. Therefore, detection of faulty nodes is necessary. Various methods for detecting faulty nodes in IIoT and overviews of their systems are discussed in this paper. However, the previous solutions experienced long delays in identifying and isolating nodes that produced incorrect data. Artificial intelligence (AI) is a good solution to this problem because of its ability to accelerate time, thereby reducing computing time and delays. The principal purpose of this paper is to serve a framework for researchers about how faulty nodes in IIoT can be detected, point out the weaknesses of each system related to the problem of delay, and propose a mechanism which allows each node to identify whether it produces faulty data rapidly.
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.