A Survey on Network Methodologies for Real-Time Analytics of Massive IoT Data and Open Research Issues

Verma, Shikhar; Kawamoto, Yuichi; Fadlullah, Zubair Md.; Nishiyama, Hiroki; Kato, Nei

doi:10.1109/comst.2017.2694469

Cited by 361 publications

(161 citation statements)

References 156 publications

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“…is paper uses the increasing request scale to identify the performance limit of the algorithms under the condition of resource constraints. In a real scenario, the number of requests is disorderly and cannot be controlled accurately, making it difficult to measure the bottleneck of algorithms in this way [52]. Namely, it shows a bigger value at a point in time but a smaller value at the next time.…”

Section: Methodsmentioning

confidence: 99%

Multistage Dynamic Packet Access Mechanism of Internet of Things

Meng

Chen

Zhang

2018

Mobile Information Systems

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In the scenario of mass control commands requesting for network access, confined by the best-effort network service mode, it is easy to bring about resource competition and thus a phenomenon of access failure on major and urgent service request at the data access center for the Internet of ings. In this event, the dynamic diversification of control command is unable to access the necessary resources on a comparatively fair basis, causing low efficiency in heterogeneous resource utilization at the access center. is paper defines the problem of group request dynamic resource allocation and further converts it into the problem of 0-1 integer and linear programming and proposes a multistage dynamic packet access strategy. is strategy works first on dynamic group division on the users' mass control requests using the high ability of self-organizing feature maps and then searches for the optimized matching resources based on the frog-leaping algorithm which has a better capacity for global searching for the best resources. is paper analyzes the feasibility of this strategy and its astringency. e experimental results demonstrate that the strategy can effectively improve the success rate of access to the data center for the Internet of ings and reduce network blockage and response delay.

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Section: Methodsmentioning

confidence: 99%

Multistage Dynamic Packet Access Mechanism of Internet of Things

Meng

Chen

Zhang

2018

Mobile Information Systems

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“…The state-of-the art analytics and network practices for real-time IoT analytics are discussed by Verma, Kawamoto, Fadlullah, Nishiyama, and Kato (2017). Authors first designated the fundamentals of the software platforms, IoT analytics and use cases, and then clarify the shortcomings of the network methodologies to provision them.…”

Section: A Survey On Iot Applicationsmentioning

confidence: 99%

Internet of Things: A primer

Paul

Jeyaraj

2019

Hum Behav & Emerg Tech

115

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We admire the emerging technologies that fascinate us, as it has become part of our daily life. Internet of Things (IoT) plays a major role in simplifying human effort. It leaps forward taking the advantages of latest wireless devices and communication technologies. IoT is a combination of technologies such as ubiquitous and pervasive computing, wireless communication devices and sensors, Internet protocol, and others. IoT logically interconnects and interoperates physical objects (sensors, wired/wireless communication devices) and virtual objects (web applications, virtual machines) over existing Internet infrastructure. IoT collects and records heterogeneous data (such as documents, images, videos, audios, and others) from heterogeneous applications (such as CCTV, medical images, barcode reader, and others) with the help of Internet. People, physical objects, and virtual objects are logically connected to the network to observe and analyze for decision‐making. Therefore, IoT has transformed to be an important evolving technology and inevitable in every sectors.

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“…In [19] the authors have surveyed network topologies for real-time application and state that in their current form, data centres are not suitable for realtime processing due to network lag and transfer delays. This was also identified in the Smart Stadium project during an exercise to measure crowd sound on busy match days [20] [21].…”

Section: B Data Processingmentioning

confidence: 99%

Performance of video processing at the edge for crowd-monitoring applications

Bailas

Marsden

Zhang

et al. 2018

2018 IEEE 4th World Forum on Internet of Things (WF-IoT)

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Abstract-Video analytics has a key role to play in smart cities and connected community applications such as crowd counting, activity detection, event classification, traffic counting etc. Using a cloud-centric approach where data is funnelled to a central processor presents a number of key problems such as available bandwidth, real-time responsiveness and personal data privacy issues. With the development of edge computing, a new paradigm for smart data management is emerging. Raw video feeds can be pre-processed at the point of capture while integration and deeper analytics is performed in the cloud. In this paper we explore the capacity of video processing at the edge and shown that basic image processing can be achieved in near real-time on lowpowered gateway devices. We have also investigated deep learning model capabilities for crowd counting in this context showing that its performance is highly dependent on the input size and rescaling video frames can optimise processing and performance. Increased edge processing resolves a number of issues in video analytics for crowd monitoring applications.

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A Survey on Network Methodologies for Real-Time Analytics of Massive IoT Data and Open Research Issues

Cited by 361 publications

References 156 publications

Multistage Dynamic Packet Access Mechanism of Internet of Things

Multistage Dynamic Packet Access Mechanism of Internet of Things

Internet of Things: A primer

Performance of video processing at the edge for crowd-monitoring applications

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