Smart Edge Broker for Location-Based Transfer between Services and Distributed Data in IoT Smart Services

Ahn, Junguk; Lee, Byung Mun

doi:10.1155/2020/8896252

Cited by 3 publications

(3 citation statements)

References 26 publications

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“…In the smart home [22], the gateway collects data from home sensors and transfers it to the sleep care service for the IoTs where the sensor is compatible with the smart home gateway to improve the quality of service. Thus, the collected results from the service are sent to the cloud for storage and analysis.…”

Section: Related Workmentioning

confidence: 99%

Smart Controller Integrated with MQTT Broker Based on Machina Learning Techniques

Hanon,

Salman

2024

JESA

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Massive amounts of heterogeneous data are produced by Internet of Things (IoT) devices utilized in daily life and numerous fields, and these data streams need to be stored, processed, analyzed, and transmitted to the cloud. It usually suffers from missing values and anomalies; system services also suffer from congestion due to slow processors, resulting in low throughput, a high response time, slow decision-making, and data loss, resulting in low quality of service and the deterioration of the system's performance. In this study, propose to integrate the smart controller (SC) with the Message Queuing Telemetry Transport (MQTT) broker and services in the fog node to make decisions automatically to prevent congestion in the system's services and speed up the processing. The IoT stream is inspected in the services for anomalies using one-class support vector machines (OCSVM). Then, using the integrating technique of principal component analysis (PCA) and the k-nearest neighbors (KNN) algorithm in the SC, obtain the best prediction of the efficient number of services that must be deployed in the system. The operating model proposed showed significantly stable system performance in terms of throughput, latency, response time, the amount of data loss, and preventing congestion.

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Section: Related Workmentioning

confidence: 99%

Smart Controller Integrated with MQTT Broker Based on Machina Learning Techniques

Hanon,

Salman

2024

JESA

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“…VerneMQ is an open-source showing similar qualities as commercial brokers. A similar distributed broker environment has been proposed in [10] , [11] . However, in all these approaches the broker is considered to be static.…”

Section: Related Workmentioning

confidence: 99%

Preparedness for managing pandemic using distributed mobile brokers – Using COVID 19 use case

Janani

Jebadurai

Paulraj

et al. 2022

Materials Today: Proceedings

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The human community has experienced the worst disaster during this pandemic spread of COVID-19. Getting essential services viz., medical, food, water, and oxygen support have been challenging during the pandemic. People suffer inadequate essential services during these lockdowns and quarantines. This paper proposes a distributed mobile broker service using the MQTT protocol for managing and accessing essential services during a pandemic. The proposed method splits the city into areas. The essential service providers register their services with the publishers and the essential service seekers seek their services from the publishers using mobile brokers. The mobile broker is also equipped with data aware trajectory planning technique that considers collecting data from available nodes with less delay. The proposed technique has been tested under two scenarios viz., mobile brokers, fixed subscribers, and mobile brokers, mobile subscribers. The performance analysis has been carried out in terms of delay and packet delivery ratio with fixed broker conditions.

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“…In the past, efforts have been made to attempt to work from home or remotely, but it has not been able to bring about a major policy and social direction due to the efforts to implement it in terms of technology. However, if you look at the background of the rise of Smart work in the industry today, it can be said that it is stronger than before [4]. One of the backgrounds is the development of IT technology.…”

Section: Introductionmentioning

confidence: 99%

Study on the Storage Virtualization Technology for Smart Work Service in Mobile Environment

Seo¹,

Kim²

2020

Ijasc

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Background/Objectives: As the paradigm changes with technological advancement and information society, next-generation products that can access information anytime, anywhere through mobile Internet devices through Smart work are being released. In particular, in the coronavirus era, there is a lot of interest in the introduction of Smart work. Methods/Statistical analysis: Companies that only pursued work in the office tend to support a free and creative work environment that goes beyond temporal and spatial restrictions. In addition, we are hoping for collaboration, efficiency, and organizational innovation through smart work communication. The introduction of Smart work by companies can be expected to have a positive impact on companies. Findings: Therefore, in this paper, we intend to provide a safe Smart work service through research and application of storage virtualization technology. In other words, a Smart work service was designed by securing the technology for operating the latest operating system based on mobile virtualization technology and storage virtualization technology, which is the core of mobile security. Improvements/Applications: The storage virtualization-based mobile work service proposed in this paper can be used for mobile terminals and special security-enhanced terminals that require high security in mobile environments. In addition, it can be used for mobile financial services for safe payment/payment such as electronic payment and internet banking, and prevents corporate information leakage and illegal access by unauthorized users in work environments such as Smart work using mobile terminals.

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Smart Edge Broker for Location-Based Transfer between Services and Distributed Data in IoT Smart Services

Cited by 3 publications

References 26 publications

Smart Controller Integrated with MQTT Broker Based on Machina Learning Techniques

Smart Controller Integrated with MQTT Broker Based on Machina Learning Techniques

Preparedness for managing pandemic using distributed mobile brokers – Using COVID 19 use case

Study on the Storage Virtualization Technology for Smart Work Service in Mobile Environment

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