Survey On Applications Of Internet Of Things Using Machine Learning

Majumdar, Namrata; Shukla, Supriya; Bhatnagar, Anisha

doi:10.1109/confluence.2019.8776951

Cited by 14 publications

(8 citation statements)

References 30 publications

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“…It can provide a new idea to solve the security problems of the IoT. Majumdar et al deeply analyze a variety of machine learning technologies and conclude that machine learning can be used not only in intelligent monitoring, traffic analysis, model training, and classification, but also in cloud computing, big data, and artificial intelligence [27]. Based on machine learning, Doshi and others build the overall framework of the IoT security system, and realize a reasonable security management method [24].…”

Section: Related Workmentioning

confidence: 99%

IoT Equipment Monitoring System Based on C5.0 Decision Tree and Time-Series Analysis

et al. 2022

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Abnormal traffic and vulnerability attack monitoring play an important role in today's Internet of things (IOT) applications. The existing solutions are usually based on machine learning for traffic, and its disadvantage is that a large number of manual operations are needed in the classification process, and the adaptability is poor. Moreover, for unknown attacks, the system can not make a relative response in time. In this work, we design a monitoring system of IoT based on C5.0 decision tree and time-series analysis. The system transforms time-series into GAF graph, and uses CNN-LSTM combination model to monitor the traffic. The time-series model based on deep learning can also improve the inefficiency and manual intervention caused by data analysis. At the same time, the system introduces LSTM technology, which can solve a series of problems that may be caused during long sequence training. We select KDD Cup 99 data set for simulation experiments and comparison with traditional traffic monitoring methods. The results show that the average error rate of abnormal traffic attack types is 3.22%. The evaluations show that the system can effectively monitor unknown attacks with 96% accuracy. We further use whitelist matching technology to identify IoT device models. After comparison of experiments, it is proved that this method has its superiority in the monitoring of IoT devices. INDEX TERMS Time-series model; C5.0 decision tree; IoT device identification; Traffic feature extraction.

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Section: Related Workmentioning

confidence: 99%

IoT Equipment Monitoring System Based on C5.0 Decision Tree and Time-Series Analysis

et al. 2022

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“…Surveillance capitalism is based on the imperative of constant need for data growth, and the prediction imperative of modifying people toward specific behaviour with new sources of behaviour via sensors (Internet of Things, IoT 5 Majumdar et al, 2019) based on which the data are stored, processed and commercialised in reality business (Zuboff, 2019).…”

Section: Surveillance Capitalismmentioning

confidence: 99%

“…Nadzirući kapitalizam temelji se na imperativu stalne potrebe za rastom podataka te predikcijskom imperativu modificiranja ljudi prema određenom ponašanju s novim izvorima ponašanja praćenim putem senzora (internet stvari, IoT 5 Majumdar et al, 2019) na temelju kojih se ti podaci pohranjuju, obrađuju i komercijaliziraju u reality business (Zuboff, 2019).…”

Section: Surveillance Capitalismunclassified

Privatno i javno u nadzirućem kapitalizmu

Peša

2022

Acta econ. tur. (Online)

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The paper deals with the protection of the private and public spheres in surveillance capitalism. Predictions on consumer behaviour or the so-called behavioural surpluses are extracted from the set of collected (big) data of users/consumers from the so-called digital footprints, which become intelligence data, commodities on the data market. In addition to predicting user behaviour, various behavioural techniques push, or nudge users in a particular desired consumer or political direction or action, or dark nudge techniques when it comes to unauthorized data collection on users in the digital sphere. Surveiling and nudging users is done in the range from caring for their health, well-being and benefits, as well as general and public well-being, to encouraging expenditure, desired behaviour or voting in the desired direction of subjects who create such incentives (corporations, political parties, governments, etc.). The subject of the paper is based on behavioural economics which has introduced behavioural techniques in the field of public policy. The author proposes conceptual model of protective and active approach in the era of surveillance capitalism in the private and public spheres. An overview of the current digital regulation in the EU is given, and the need for further development of the legislative framework that will regulate the issues of supervision and protection of privacy and user data is pointed out.

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“…In addition, the development of mobile Internet technology has greatly promoted artificial intelligence so that human society has entered the intelligence era [8]. Machine Learning (ML) helps process the huge amounts of data created by IoT devices and gives IoT time to think so that it can use its technologies and ideas to deliver the desired results [9]. However, traditional ML framework mainly focus on centralized data processing, which poses great challenges to data privacy protection and network throughput.…”

Section: Introductionmentioning

confidence: 99%

Research on Resource Allocation for Efficient Federated Learning

Ren¹,

Ni²,

Nie³

et al. 2021

Preprint

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As a promising solution to achieve efficient learning among isolated data owners and solve data privacy issues, federated learning is receiving wide attention. Using the edge server as an intermediary can effectively collect sensor data, perform local model training, and upload model parameters for global aggregation. So this paper proposes a new framework for resource allocation in a hierarchical network supported by edge computing. In this framework, we minimize the weighted sum of system cost and learning cost by optimizing bandwidth, computing frequency, power allocation and subcarrier assignment. To solve this challenging mixed-integer non-linear problem, we first decouple the bandwidth optimization problem(P1) from the whole problem and obtain a closed-form solution. The remaining computational frequency, power, and subcarrier joint optimization problem(P2) can be further decomposed into two sub-problems: latency and computational frequency optimization problem(P3) and transmission power and subcarrier optimization problem(P4). P3 is a convex optimization problem that is easy to solve. In the joint optimization problem(P4), the optimal power under each subcarrier selection can be obtained first through the successive convex approximation(SCA) algorithm. Substituting the optimal power value obtained back to P4, the subproblem can be regarded as an assignment problem, so the Hungarian algorithm can be effectively used to solve it. The solution of problem P2 is accomplished by solving P3 and P4 iteratively. To verify the performance of the algorithm, we compare the proposed algorithm with five algorithms; namely Equal bandwidth allocation, Learning cost guaranteed, Greedy subcarrier allocation, System cost guaranteed and Time-biased algorithm. Numerical results show the significant performance gain and the robustness of the proposed algorithm in the face of parameter changes.

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Survey On Applications Of Internet Of Things Using Machine Learning

Cited by 14 publications

References 30 publications

IoT Equipment Monitoring System Based on C5.0 Decision Tree and Time-Series Analysis

IoT Equipment Monitoring System Based on C5.0 Decision Tree and Time-Series Analysis

Privatno i javno u nadzirućem kapitalizmu

Research on Resource Allocation for Efficient Federated Learning

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