scholarly journals Boundary Region Detection for Continuous Objects in Wireless Sensor Networks

2018 ◽  
Vol 2018 ◽  
pp. 1-13 ◽  
Author(s):  
Yaqiang Zhang ◽  
Zhenhua Wang ◽  
Lin Meng ◽  
Zhangbing Zhou
Keyword(s):  
Fog Computing ◽  
Boundary Region ◽  
Boundary Line ◽  
Object Boundary ◽  
Sensory Data ◽  
Region Detection ◽  
Industrial Internet ◽  
Continuous Objects ◽  
Continuous Object ◽  
Research Challenge

Industrial Internet of Things has been widely used to facilitate disaster monitoring applications, such as liquid leakage and toxic gas detection. Since disasters are usually harmful to the environment, detecting accurate boundary regions for continuous objects in an energy-efficient and timely fashion is a long-standing research challenge. This article proposes a novel mechanism for continuous object boundary region detection in a fog computing environment, where sensing holes may exist in the deployed network region. Leveraging sensory data that have been gathered, interpolation algorithms have been applied to estimate sensory data at certain geographical locations, in order to estimate a more accurate boundary line. To examine whether estimated sensory data reflect that fact, mobile sensors are adopted to traverse these locations for gathering their sensory data, and the boundary region is calibrated accordingly. Experimental evaluation shows that this technique can generate a precise object boundary region with certain time constraints, and the network lifetime can be prolonged significantly.

scholarly journals Software Architecture of a Fog Computing Node for Industrial Internet of Things

Sensors ◽  
2021 ◽  
Vol 21 (11) ◽  
pp. 3715
Author(s):  
Ioan Ungurean ◽  
Nicoleta Cristina Gaitan
Keyword(s):  
Internet Of Things ◽  
Real Time ◽  
Development Process ◽  
Fog Computing ◽  
Low Latency ◽  
Practical Implementation ◽  
Industrial Internet Of Things ◽  
Industrial Internet ◽  
Starting Point ◽  
Hard Real Time

In the design and development process of fog computing solutions for the Industrial Internet of Things (IIoT), we need to take into consideration the characteristics of the industrial environment that must be met. These include low latency, predictability, response time, and operating with hard real-time compiling. A starting point may be the reference fog architecture released by the OpenFog Consortium (now part of the Industrial Internet Consortium), but it has a high abstraction level and does not define how to integrate the fieldbuses and devices into the fog system. Therefore, the biggest challenges in the design and implementation of fog solutions for IIoT is the diversity of fieldbuses and devices used in the industrial field and ensuring compliance with all constraints in terms of real-time compiling, low latency, and predictability. Thus, this paper proposes a solution for a fog node that addresses these issues and integrates industrial fieldbuses. For practical implementation, there are specialized systems on chips (SoCs) that provides support for real-time communication with the fieldbuses through specialized coprocessors and peripherals. In this paper, we describe the implementation of the fog node on a system based on Xilinx Zynq UltraScale+ MPSoC ZU3EG A484 SoC.

scholarly journals Scalable Fog Computing Orchestration for Reliable Cloud Task Scheduling

2021 ◽  
Vol 11 (22) ◽  
pp. 10996
Author(s):  
Jongbeom Lim
Keyword(s):  
Internet Of Things ◽  
Task Scheduling ◽  
Industrial Revolution ◽  
Virtual Machines ◽  
Fog Computing ◽  
Industrial Internet Of Things ◽  
Industrial Internet ◽  
Virtualization Technology ◽  
Computing Environments ◽  
One Machine

As Internet of Things (IoT) and Industrial Internet of Things (IIoT) devices are becoming increasingly popular in the era of the Fourth Industrial Revolution, the orchestration and management of numerous fog devices encounter a scalability problem. In fog computing environments, to embrace various types of computation, cloud virtualization technology is widely used. With virtualization technology, IoT and IIoT tasks can be run on virtual machines or containers, which are able to migrate from one machine to another. However, efficient and scalable orchestration of migrations for mobile users and devices in fog computing environments is not an easy task. Naïve or unmanaged migrations may impinge on the reliability of cloud tasks. In this paper, we propose a scalable fog computing orchestration mechanism for reliable cloud task scheduling. The proposed scalable orchestration mechanism considers live migrations of virtual machines and containers for the edge servers to reduce both cloud task failures and suspended time when a device is disconnected due to mobility. The performance evaluation shows that our proposed fog computing orchestration is scalable while preserving the reliability of cloud tasks.

Towards the Protection and Security in Fog Computing for Industrial Internet of Things

2021 ◽  
pp. 17-32
Author(s):  
G. Rama Subba Reddy ◽  
K. Rangaswamy ◽  
Malla Sudhakara ◽  
Pole Anjaiah ◽  
K. Reddy Madhavi
Keyword(s):  
Internet Of Things ◽  
Distributed Computing ◽  
Fog Computing ◽  
Security And Privacy ◽  
Safety Measures ◽  
Iot Applications ◽  
Industrial Internet ◽  
Sensor Devices ◽  
Key Enabling Technologies ◽  
Privacy Issues

Internet of things (IoT) has given a promising chance to construct amazing industrial frameworks and applications by utilizing wireless and sensor devices. To support IIoT benefits efficiently, fog computing is typically considered as one of the potential solutions. Be that as it may, IIoT services still experience issues such as high-latency and unreliable connections between cloud and terminals of IIoT. In addition to this, numerous security and privacy issues are raised and affect the users of the distributed computing environment. With an end goal to understand the improvement of IoT in industries, this chapter presents the current research of IoT along with the key enabling technologies. Further, the architecture and features of fog computing towards the fog-assisted IoT applications are presented. In addition to this, security and protection threats along with safety measures towards the IIoT applications are discussed.

scholarly journals Industrial Internet of Things and Fog Computing to Reduce Energy Consumption in Drinking Water Facilities

Processes ◽  
2020 ◽  
Vol 8 (3) ◽  
pp. 282 ◽  
Author(s):  
Adrian Korodi ◽  
Ruben Crisan ◽  
Andrei Nicolae ◽  
Ioan Silea
Keyword(s):  
Drinking Water ◽  
Energy Consumption ◽  
Internet Of Things ◽  
Fog Computing ◽  
Industrial Internet Of Things ◽  
Non Invasive ◽  
Data Accumulation ◽  
Industrial Internet ◽  
Reduce Energy Consumption ◽  
And Control

The industry is generally preoccupied with the evolution towards Industry 4.0 principles and the associated advantages as cost reduction, respectively safety, availability, and productivity increase. So far, it is not completely clear how to reach these advantages and what their exact representation or impact is. It is necessary for industrial systems, even legacy ones, to assure interoperability in the context of chronologically dispersed and currently functional solutions, respectively; the Open Platform Communications Unified Architecture (OPC UA) protocol is an essential requirement. Then, following data accumulation, the resulting process-aware strategies have to present learning capabilities, pattern identification, and conclusions to increase efficiency or safety. Finally, model-based analysis and decision and control procedures applied in a non-invasive manner over functioning systems close the optimizing loop. Drinking water facilities, as generally the entire water sector, are confronted with several issues in their functioning, with a high variety of implemented technologies. The solution to these problems is expected to create a more extensive connection between the physical and the digital worlds. Following previous research focused on data accumulation and data dependency analysis, the current paper aims to provide the next step in obtaining a proactive historian application and proposes a non-invasive decision and control solution in the context of the Industrial Internet of Things, meant to reduce energy consumption in a water treatment and distribution process. The solution is conceived for the fog computing concept to be close to local automation, and it is automatically adaptable to changes in the process’s main characteristics caused by various factors. The developments were applied to a water facility model realized for this purpose and on a real system. The results prove the efficiency of the concept.

scholarly journals A Twofold Sink Based Data Collection for Continuous Object Tracking in Wireless Sensor Network

2017 ◽  
Author(s):  
Sajjad Hussain Chauhdary ◽  
Ali Hassan ◽  
Mohammed A Alqarni ◽  
Abdullah Alamri
Keyword(s):  
Object Tracking ◽  
Forest Fires ◽  
Life Time ◽  
Mobile Sink ◽  
Residual Energy ◽  
Sensor Nodes ◽  
Sink Node ◽  
Network Nodes ◽  
Continuous Objects ◽  
Continuous Object

Continuous object tracking in WSNs, such as monitoring of mud-rock flows, forest fires etc., is a challenging task due to characteristic nature of continuous objects. They can appear randomly in the network field, move continuously, and can change in size and shape. Monitoring such objects in real-time generally require tremendous amount of messaging between sensor nodes to synergistically estimate object’s movement and track its location. In this paper, we propose a novel twofold-sink mechanism, comprising of a mobile and a static sink node. Both sink nodes gather information about boundary sensor nodes, which is then used to uniformly distribute energy consumption across all network nodes, thus helping in saving residual energy of network nodes. Numerous object tracking schemes, using mobile sink, have been proposed in the literature. However, existing schemes employing mobile sink cannot be applied to track continuous objects, because of momentous variation of network topology. Therefore, we present in this paper a mechanism, transformed from K-means algorithm, to find the best sensing location of the mobile sink node. It helps to reduce transmission load on the intermediate network nodes situated between static sink node and the ordinary network sensing nodes. The simulation results show that the proposed scheme can distinctly improve life time of the network, compared to one-sink protocol employed in continuous object tracking.

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