scholarly journals An Efficient SDN Multicast Architecture for Dynamic Industrial IoT Environments

2018 ◽  
Vol 2018 ◽  
pp. 1-11 ◽  
Author(s):  
Hyeong-Su Kim ◽  
Seongjin Yun ◽  
Hanjin Kim ◽  
Heonyeop Shin ◽  
Won-Tae Kim
Keyword(s):  
Large Scale ◽  
Packet Delay ◽  
Smart Factory ◽  
Point Selection ◽  
Virtual Factory ◽  
Industrial Iot ◽  
Rendezvous Point ◽  
Point To Point ◽  
Switching Method ◽  
Shared Tree

Large-scale industrial IoT services appear in smart factory domains such as factory clouds which integrate distributed small factories into a large virtual factory with dynamic combination based on orders of consumers. A smart factory has so many industrial elements including various sensors/actuators, gateways, controllers, application servers, and IoT clouds. Since there are complex connections and relations, it is hard to handle them in point-to-point manner. In addition, many duplicated traffics are exchanged between them through the Internet. Multicast is believed as an effective many-to-many communication mechanism by establishing multicast trees between sources and receivers. There are, however, some issues for adopting multicast to large-scale industrial IoT services in terms of QoS. In this paper, we propose a novel software-defined network multicast based on group shared tree which includes near-receiver rendezvous point selection algorithm and group shared tree switching mechanism. As a result, the proposed multicast mechanism can reduce the packet loss by 90% compared to the legacy methods under severe congestion condition. GST switching method obtains to decreased packet delay effect, respectively, 2%, 20% better than the previously studied multicast and the legacy SDN multicast.

Interference mitigation in point to point wireless sensor networks using LSP protocol and time division multiplexing approach

2020 ◽  
Vol 39 (4) ◽  
pp. 5449-5458
Author(s):  
A. Arokiaraj Jovith ◽  
S.V. Kasmir Raja ◽  
A. Razia Sulthana
Keyword(s):  
Energy Consumption ◽  
Large Scale ◽  
Control Parameter ◽  
Interference Mitigation ◽  
Wireless Sensor ◽  
Point To Point ◽  
A Minor ◽  
Two Stages ◽  
Time Division ◽  
And Control

Interference in Wireless Sensor Network (WSN) predominantly affects the performance of the WSN. Energy consumption in WSN is one of the greatest concerns in the current generation. This work presents an approach for interference measurement and interference mitigation in point to point network. The nodes are distributed in the network and interference is measured by grouping the nodes in the region of a specific diameter. Hence this approach is scalable and isextended to large scale WSN. Interference is measured in two stages. In the first stage, interference is overcome by allocating time slots to the node stations in Time Division Multiple Access (TDMA) fashion. The node area is split into larger regions and smaller regions. The time slots are allocated to smaller regions in TDMA fashion. A TDMA based time slot allocation algorithm is proposed in this paper to enable reuse of timeslots with minimal interference between smaller regions. In the second stage, the network density and control parameter is introduced to reduce interference in a minor level within smaller node regions. The algorithm issimulated and the system is tested with varying control parameter. The node-level interference and the energy dissipation at nodes are captured by varying the node density of the network. The results indicate that the proposed approach measures the interference and mitigates with minimal energy consumption at nodes and with less overhead transmission.

Implementation of MQTT protocol based network architecture for smart factory

2021 ◽  
pp. 095440542110144
Author(s):  
Chia-Shin Yeh ◽  
Shang-Liang Chen ◽  
I-Ching Li
Keyword(s):  
Machine Tool ◽  
Gesture Recognition ◽  
Manufacturing Process ◽  
Network Architecture ◽  
Information Management System ◽  
Edge Computing ◽  
Smart Factory ◽  
Smart Manufacturing ◽  
Industrial Iot ◽  
Computing Module

The core concept of smart manufacturing is based on digitization to construct intelligent production and management in the manufacturing process. By digitizing the production process and connecting all levels from product design to service, the purpose of improving manufacturing efficiency, reducing production cost, enhancing product quality, and optimizing user experience can be achieved. To digitize the manufacturing process, IoT technology will have to be introduced into the manufacturing process to collect and analyze process information. However, one of the most important problems in building the industrial IoT (IIoT) environment is that different industrial network protocols are used for different equipment in factories. Therefore, the information in the manufacturing process may not be easily exchanged and obtained. To solve the above problem, a smart factory network architecture based on MQTT (MQ Telemetry Transport), IoT communication protocol, is proposed in this study, to construct a heterogeneous interface communication bridge between the machine tool, embedded device Raspberry Pi, and website. Finally, the system architecture is implemented and imported into the factory, and a smart manufacturing information management system is developed. The edge computing module is set up beside a three-axis machine tool, and a human-machine interface is built for the user controlling and monitoring. Users can also monitor the system through the dynamically updating website at any time and any place. The function of real-time gesture recognition based on image technology is developed and built on the edge computing module. The gesture recognition results can be transmitted to the machine controller through MQTT, and the machine will execute the corresponding action according to different gestures to achieve human-robot collaboration. The MQTT transmission architecture developed here is validated by the given edge computing application. It can serve as the basis for the construction of the IIoT environment, assist the traditional manufacturing industry to prepare for digitization, and accelerate the practice of smart manufacturing.

scholarly journals Joint optimization of network selection and task offloading for vehicular edge computing

2021 ◽  
Vol 10 (1) ◽  
Author(s):  
Lujie Tang ◽  
Bing Tang ◽  
Li Zhang ◽  
Feiyan Guo ◽  
Haiwu He
Keyword(s):  
Large Scale ◽  
Vehicular Networks ◽  
Access Point ◽  
Edge Computing ◽  
Network Selection ◽  
Network Access ◽  
Task Execution ◽  
Point Selection ◽  
Vehicle Movement ◽  
Task Offloading

AbstractTaking the mobile edge computing paradigm as an effective supplement to the vehicular networks can enable vehicles to obtain network resources and computing capability nearby, and meet the current large-scale increase in vehicular service requirements. However, the congestion of wireless networks and insufficient computing resources of edge servers caused by the strong mobility of vehicles and the offloading of a large number of tasks make it difficult to provide users with good quality of service. In existing work, the influence of network access point selection on task execution latency was often not considered. In this paper, a pre-allocation algorithm for vehicle tasks is proposed to solve the problem of service interruption caused by vehicle movement and the limited edge coverage. Then, a system model is utilized to comprehensively consider the vehicle movement characteristics, access point resource utilization, and edge server workloads, so as to characterize the overall latency of vehicle task offloading execution. Furthermore, an adaptive task offloading strategy for automatic and efficient network selection, task offloading decisions in vehicular edge computing is implemented. Experimental results show that the proposed method significantly improves the overall task execution performance and reduces the time overhead of task offloading.

Data-Driven Lightweight Interest Point Selection for Large-Scale Visual Search

2018 ◽  
Vol 20 (10) ◽  
pp. 2774-2787 ◽  
Author(s):  
Feng Gao ◽  
Xinfeng Zhang ◽  
Yicheng Huang ◽  
Yong Luo ◽  
Xiaoming Li ◽  
...  
Keyword(s):  
Visual Search ◽  
Large Scale ◽  
Data Driven ◽  
Interest Point ◽  
Point Selection ◽  
Selection For

Dazzling the Multitude Original Media Spectacles

1990 ◽  
Author(s):  
Carolyn Marvin
Keyword(s):  
Nineteenth Century ◽  
Large Scale ◽  
New Technologies ◽  
Late Nineteenth Century ◽  
Social Worlds ◽  
Media Technologies ◽  
The Social ◽  
Late Nineteenth ◽  
Words And Images ◽  
Point To Point

Anthropologists and literary theorists are fond of emphasizing the particularistic and dramatic dimensions of lived communication. The particularistic dimension of communication is constituted in whatever of its aspects have the most individually intimate meaning for us. The dramatic dimension is the shared emotional character of a communicated message, displayed and sometimes exaggerated for consumption by a public. Its dramatic appeal and excitement depend partly on the knowledge that others are also watching with interest. Such dimensions have little in common with abstractions about information and efficiency that characterize contemporary discussion about new communications technologies, but may be closer to the real standards by which we judge media and the social worlds they invade, survey, and create. Media, of course, are devices that mediate experience by re-presenting messages originally in a different mode. In the late nineteenth century, experts convinced of the power of new technologies to repackage human experience and to multiply it for many presentations labored to enhance the largest, most dramatically public of messages, and the smallest, most intimately personal ones, by applying new media technologies to a range of modes from private conversation to public spectacle, that special large-scale display event intended for performance before spectators. In the late nineteenth century, intimate communication at a distance was achieved, or at least approximated, by the fledgling telephone. The telephone of this era was not a democratic medium. Spectacles, by contrast, were easily accessible and enthusiastically relished by their nineteenth-century audiences. Their drama was frequently embellished by illuminated effects that inspired popular fantasies about message systems of the future, perhaps with giant beams of electric light projecting words and images on the clouds. Mass distribution of electric messages in this fashion was indeed one pole of the range of imaginative possibilities dreamt by our ancestors for twentieth-century communication. Equally absorbing was the fantasy of effortless point-to-point communication without wires, where no physical obstacle divided the sympathy of minds desiring mutual communion.

scholarly journals A Grid Feature-Point Selection Method for Large-Scale Street View Image Retrieval Based on Deep Local Features

2020 ◽  
Vol 12 (23) ◽  
pp. 3978
Author(s):  
Tianyou Chu ◽  
Yumin Chen ◽  
Liheng Huang ◽  
Zhiqiang Xu ◽  
Huangyuan Tan
Keyword(s):  
Image Retrieval ◽  
Large Scale ◽  
Local Features ◽  
Selection Method ◽  
Image Features ◽  
Feature Point ◽  
Feature Points ◽  
Point Selection ◽  
Retrieval Task ◽  
Street View

Street view image retrieval aims to estimate the image locations by querying the nearest neighbor images with the same scene from a large-scale reference dataset. Query images usually have no location information and are represented by features to search for similar results. The deep local features (DELF) method shows great performance in the landmark retrieval task, but the method extracts many features so that the feature file is too large to load into memory when training the features index. The memory size is limited, and removing the part of features simply causes a great retrieval precision loss. Therefore, this paper proposes a grid feature-point selection method (GFS) to reduce the number of feature points in each image and minimize the precision loss. Convolutional Neural Networks (CNNs) are constructed to extract dense features, and an attention module is embedded into the network to score features. GFS divides the image into a grid and selects features with local region high scores. Product quantization and an inverted index are used to index the image features to improve retrieval efficiency. The retrieval performance of the method is tested on a large-scale Hong Kong street view dataset, and the results show that the GFS reduces feature points by 32.27–77.09% compared with the raw feature. In addition, GFS has a 5.27–23.59% higher precision than other methods.

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