Fog Intelligence for Real-Time IoT Sensor Data Analytics

Raafat, Hazem M.; Hossain, M. Shamim; Essa, Ehab; Elmougy, Samir; Tolba, A. S.; Muhammad, Ghulam; Ghoneim, Ahmed

doi:10.1109/access.2017.2754538

Cited by 52 publications

(13 citation statements)

References 14 publications

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“…Fog-Based Data Analysis: The authors in [78][79][80] presented fog-level-based IoT sensor data processing. Here, the senor data features are extracted and processed to classify different signal patterns using a neural network.…”

Section: Discussionmentioning

confidence: 99%

An Overview of IoT Sensor Data Processing, Fusion, and Analysis Techniques

Krishnamurthi

Kumar

Gopinathan

et al. 2020

Sensors

227

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In the recent era of the Internet of Things, the dominant role of sensors and the Internet provides a solution to a wide variety of real-life problems. Such applications include smart city, smart healthcare systems, smart building, smart transport and smart environment. However, the real-time IoT sensor data include several challenges, such as a deluge of unclean sensor data and a high resource-consumption cost. As such, this paper addresses how to process IoT sensor data, fusion with other data sources, and analyses to produce knowledgeable insight into hidden data patterns for rapid decision-making. This paper addresses the data processing techniques such as data denoising, data outlier detection, missing data imputation and data aggregation. Further, it elaborates on the necessity of data fusion and various data fusion methods such as direct fusion, associated feature extraction, and identity declaration data fusion. This paper also aims to address data analysis integration with emerging technologies, such as cloud computing, fog computing and edge computing, towards various challenges in IoT sensor network and sensor data analysis. In summary, this paper is the first of its kind to present a complete overview of IoT sensor data processing, fusion and analysis techniques.

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Section: Discussionmentioning

confidence: 99%

An Overview of IoT Sensor Data Processing, Fusion, and Analysis Techniques

Krishnamurthi

Kumar

Gopinathan

et al. 2020

Sensors

227

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“…These appliances are equipped with IoT sensor nodes [33][34] and controlled using Low-Power Wireless Personal Area Networks (LoWPAN) mobile communication protocol. Figure 12, shows an example of smart home connected through 5G network.…”

Section: Fig 12 Smart Homes Connected Through 5g Networkmentioning

confidence: 99%

Role of 5G Networks: Issues, Challenges and Applications

Tripathi¹,

Rajak²,

Shrivastava³

2019

IJEAT

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 Abstract: Due to revolutionary development in electronic and communication, mobile and handheld devices become the part of our daily life. As a result, volume of data traffic on Internet is increasing day by day. To provide unlimited, uninterrupted and content-rich services to these devices, the 5th Generation (5G) of network technology is emerged. 5G network can provide better Quality of Service (QoS) along with higher data rates than 4G network and have least latency. The paper appraisals various generations of wireless networks. Furthermore, it explores various challenges in implementation of 5G network and application areas of 5G network.

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“…Synthesizing from the vast amounts of works being pursued in IoT, the challenges immediately pertaining to networking issues in order to enable smart IoT systems include Node discovery, Resource discovery, Service discovery, Data discovery, (Mukherjee, Neupane, & Calyam, 2017), (Zhou, Cao, Dong, & Vasilakos, 2017), Da (Ranjan, Thakker, Haller, & Buyya, 2017), An (Patel, Ali, & Sheth, 2017), Da, Re (Bhandari, Sharma, & Wang, 2017), Re, Co, An (Petrolo, Morabito, Loscrì, & Mitton, 2017), Ca (Mingozzi et al, 2016) Se, Sn, Re (Yan, Hua, Zhang, & Wan, 2017), Re (Farris et al, 2017), (Zhang et al, 2015), Da, Co (Vukobratovic et al, 2016), Se, Da, Co (Ma, Li, Guan, Xia, & Liu, 2015) Gateway Re, Sn, An, Co (Billet & Issarny, 2017), (Petrolo et al, 2017) Re, Sn, An, Co (Billet & Issarny, 2017) Fog/ edge An (Patel et al, 2017), Da, An, Co, Re, Se (Sahni, Cao, Zhang, & Yang, 2017), (Verhelst & Moons, 2017), Se (Mukherjee, Neupane, & Calyam, 2017), Ro (Vinueza Naranjo, Shojafar, Mostafaei, Pooranian, & Baccarelli, 2017, An, Da (Raafat et al, 2017) Re (Farris et al, 2017), Da, Re, Co, An (Sun & Ansari, 2016), Ca, Co (Roca, Milito, Nemirovsky, & Valero, 2018), Re (Farris et al, 2017) End nodes Da (Raafat et al, 2017), Ro (Vinueza Naranjo et al, 2017, An, Re, Se (Verhelst & Moons, 2017), Ca, An (Chaochaisit, Bessho, Koshizuka, & Sakamura, 2016 An, Re, Se (Verhelst & Moons, 2017) Ca = Context Awareness; Co = Communication; Se = Security; Ay = Analytics; Da = Data; Ro = Routing, Sn = Sensing, Re = Resource. and Information discovery.…”

Section: Synthesismentioning

confidence: 99%

“…In simpler terms, this layer is tasked with providing connectivity between the topmost and bottom-most layer of the IoT ecosystem (Figure 2), as well as enabling communication between various distributed local networks without forwarding data and the topmost layer. However, more recent allied technologies such as the fog (Raafat et al, 2017) are becoming hugely popular owing to their uniformly sized data packets, having precisely the same position of bits in the transmitted packets for all applications. This makes it easier to extract, organize, and interpretation of the data.…”

Section: Networking Components In Iotmentioning

confidence: 99%

“…In simpler terms, this layer is tasked with providing connectivity between the topmost and bottom‐most layer of the IoT ecosystem (Figure ), as well as enabling communication between various distributed local networks without forwarding data and the topmost layer. However, more recent allied technologies such as the fog (Raafat et al, ) are becoming hugely popular owing to their added capability of processing and analyzing of locally gathered data besides proving the regular unification and forwarding services and having the intelligence of selectively forwarding packets to the remote infrastructure in the topmost layer of the IoT ecosystem. This ensures speed‐up of decision‐making and reduction in unnecessary congestion in the IP‐based backbone network, which still relies on legacy IP‐based protocols and infrastructure for all operations.…”

Section: Introductionmentioning

confidence: 99%

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Knowledge discovery for enabling smart Internet of Things: A survey

Misra¹,

Mukherjee²,

Roy

2018

WIREs Data Min & Knowl

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The use of knowledge discovery on the Internet of Things (IoT) and its allied domains is undeniably one of the most indispensable ones, which results in optimized placement architectures, efficient routing protocols, device energy savings, and enhanced security measures for the implementation. The absence of knowledge discovery in IoT results in just an implementation of large‐scale sensor networks, which generates a huge amount of data, and which needs, an often under‐optimized, processing for actionable outputs. In this survey, we explore various domains of IoT for which knowledge discovery is inseparable from the application, and show how it benefits the overall implementation of the IoT architecture. This article is categorized under: Fundamental Concepts of Data and Knowledge > Knowledge Representation Technologies > Machine Learning Application Areas > Internet and Web‐Based Applications Algorithmic Development > Spatial and Temporal Data Mining

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Fog Intelligence for Real-Time IoT Sensor Data Analytics

Cited by 52 publications

References 14 publications

An Overview of IoT Sensor Data Processing, Fusion, and Analysis Techniques

An Overview of IoT Sensor Data Processing, Fusion, and Analysis Techniques

Role of 5G Networks: Issues, Challenges and Applications

Knowledge discovery for enabling smart Internet of Things: A survey

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