scholarly journals Data Sharing Mechanism of Sensors and Actuators of Industrial IoT Based on Blockchain-Assisted Identity-Based Cryptography

Sensors ◽  
2021 ◽  
Vol 21 (18) ◽  
pp. 6084
Author(s):  
Yifan Meng ◽  
Jingzhao Li
Keyword(s):  
Data Sharing ◽  
Production Efficiency ◽  
Smart Devices ◽  
Test Results ◽  
Sensors And Actuators ◽  
Multiple Production ◽  
Industrial Iot ◽  
Standard Interface ◽  
Identity Based ◽  
Encryption Algorithms

The Industrial IoT is one of the key technologies to improve industrial production efficiency. The entire production process usually involves multiple production regions and numerous smart devices (sensors and actuators). The efficiency of the Industrial IoT is limited by this strong coupling relationship between the subsystem and the sensors and actuators. In this paper, to unleash the potential of Industrial IoT, a safe and reliable data sharing mechanism of sensors and actuators is proposed. We deployed distributed identity authentication and data proxy services in various regions. In the device authentication process, we used identity-based encryption algorithms, and we solved the trust problem between different regions by introducing a private blockchain. In addition, we designed the model of device capability (MDC) to describe the device, enabling it to be shared with a standard interface. Finally, we conducted many performance tests on the proposed mechanism. The test results verified the effectiveness and efficiency of the proposed mechanism.

Cross-Domain Secure Data Sharing using Blockchain for Industrial IoT

2021 ◽  
Author(s):  
Parminder Singh ◽  
Mehedi Masud ◽  
M. Shamim Hossain ◽  
Avinash Kaur
Keyword(s):  
Data Sharing ◽  
Cross Domain ◽  
Industrial Iot ◽  
Secure Data

scholarly journals An Improved Framework for C-V2X Systems with Data Integration and Identity-based Authentication

2021 ◽  
Author(s):  
Rui Huang
Keyword(s):  
Bottom Layer ◽  
Autonomous Driving ◽  
Integrated System ◽  
Smart Devices ◽  
Fusion Model ◽  
Current Trends ◽  
Multiple Data ◽  
Multiple Devices ◽  
Identity Based ◽  
Time Frames

Current trends of autonomous driving apply the hybrid use of on-vehicle and roadside smart devices to perform collaborative data sensing and computing, so as to achieve a comprehensive and stable decision making. The integrated system is usually named as C-V2X. However, several challenges have significantly hindered the development and adoption of such systems. For example, the difficulty of accessing multiple data protocols of multiple devices at the bottom layer, and the centralized deployment of computing arithmetic power. Therefore, this work proposes a novel framework for the design of C-V2X systems. First, a highly aggregated architecture is designed with fully integration with multiple traffic data resources. Then a multilevel information fusion model is designed based on multi-sensors in vehicle-road coordination. The model can fit different detection environments, detection mechanisms, and time frames. Finally, a lightweight and efficient identity-based authentication method is given. The method can realize bidirectional authentication between end devices and edge gateways.

Microsensors, MEMS and NEMS Based Complex Adaptive Smart Devices and Systems

2001 ◽  
Author(s):  
Vijay K. Varadan
Keyword(s):  
Self Assembly ◽  
Communication Systems ◽  
Microelectromechanical Systems ◽  
System Level ◽  
Smart Devices ◽  
Mems Devices ◽  
Wafer Level ◽  
Sensors And Actuators ◽  
Complex Adaptive ◽  
Microelectronics Industry

Abstract The microelectronics industry has seen explosive growth during the last thirty years. Extremely large markets for logic and memory devices have driven the development of new materials, and technologies for the fabrication of even more complex devices with features sizes now down at the sub micron level. Recent interest has arisen in employing these materials, tools and technologies for the fabrication of miniature sensors and actuators and their integration with electronic circuits to produce smart devices and MicroElectroMechanical Systems (MEMS). This effort offers the promise of: 1. Increasing the performance and manufacturability of both sensors and actuators by exploiting new batch fabrication processes developed for the IC and microelectronics industry. Examples include micro stereo lithographic and micro molding techniques. 2. Developing novel classes of materials and mechanical structures not possible previously, such as diamond like carbon, silicon carbide and carbon nanotubes, micro-turbines and micro-engines. 3. Development of technologies for the system level and wafer level integration of micro components at the nanometer precision, such as self-assembly techniques and robotic manipulation. 4. Development of control and communication systems for MEMS devices, such as optical and RF wireless, and power delivery systems.

Machine Learning for Industrial IoT Systems

2021 ◽  
pp. 336-358
Author(s):  
Mona Bakri Hassan ◽  
Elmustafa Sayed Ali Ahmed ◽  
Rashid A. Saeed
Keyword(s):  
Machine Learning ◽  
Decision Making ◽  
Big Data ◽  
Operations Management ◽  
Industrial Applications ◽  
Smart Devices ◽  
Iot Applications ◽  
Industrial Iot ◽  
Effective Decision ◽  
Effective Decision Making

The use of AI algorithms in the IoT enhances the ability to analyse big data and various platforms for a number of IoT applications, including industrial applications. AI provides unique solutions in support of managing each of the different types of data for the IoT in terms of identification, classification, and decision making. In industrial IoT (IIoT), sensors, and other intelligence can be added to new or existing plants in order to monitor exterior parameters like energy consumption and other industrial parameters levels. In addition, smart devices designed as factory robots, specialized decision-making systems, and other online auxiliary systems are used in the industries IoT. Industrial IoT systems need smart operations management methods. The use of machine learning achieves methods that analyse big data developed for decision-making purposes. Machine learning drives efficient and effective decision making, particularly in the field of data flow and real-time analytics associated with advanced industrial computing networks.

scholarly journals Smart Airport Cybersecurity: Threat Mitigation and Cyber Resilience Controls

Sensors ◽  
2018 ◽  
Vol 19 (1) ◽  
pp. 19 ◽  
Author(s):  
Georgia Lykou ◽  
Argiro Anagnostopoulou ◽  
Dimitris Gritzalis
Keyword(s):  
Best Practices ◽  
Growth Efficiency ◽  
Smart Devices ◽  
Malicious Attacks ◽  
Travel Experience ◽  
Implementation Rate ◽  
Industrial Iot ◽  
Threat Mitigation ◽  
Operational Practices ◽  
Risk Scenario

Airports are at the forefront of technological innovation, mainly due to the fact that the number of air travel passengers is exponentially increasing every year. As a result, airports enhance their infrastructure intelligence and evolve as smart facilities to support growth, by offering an enjoyable travel experience. New challenges are coming up, which aviation has to deal with and adapt to, such as the integration of Industrial IoT (Internet of Things) in airport facilities and the increased use of smart devices from travelers and employees. Cybersecurity is becoming a key enabler for safety, which is paramount in the aviation context. Smart airports strive to provide optimal services in a reliable and sustainable manner, by working around the domains of growth, efficiency, safety and security. This article researches: (a) the implementation rate of cybersecurity measures in commercial airports; (b) malicious threats that evolve due to IoT and smart devices installed; (c) risk scenario analysis for IoT malicious attacks with threat mitigation actions. With the aim to enhance operational practices and develop robust cybersecurity governance in smart airports, we present a systematic and comprehensive analysis of malicious attacks in smart airports, to facilitate airport community comprehend risks and proactively act, by implementing cybersecurity best practices and resilience measures.

Structural design and kinematic analysis of high-speed automatic casing system for lithium-ion battery

2019 ◽  
Vol 16 (2) ◽  
pp. 225-237
Author(s):  
Qinxiang Xia ◽  
Xinjian Ming ◽  
Jinchuan Long ◽  
Xiujuan Liu
Keyword(s):  
Lithium Ion Battery ◽  
Structural Design ◽  
Kinematic Analysis ◽  
High Speed ◽  
Production Efficiency ◽  
Lithium Ion ◽  
Virtual Prototype ◽  
Test Results ◽  
Content Type ◽  
Working Mechanisms

Purpose The purpose of this paper is to design the whole structure of high-speed automatic casing system (HSACS) for lithium-ion battery (LIB), and verify its rationality and reliability by kinematic simulation and casing test. Design/methodology/approach Based on the software of SolidWorks and ADAMS, the structure of working mechanisms for HSACS was designed, and virtual prototype models of HSACS and main turntable were established to realize the kinematic analysis. The HSACS casing test was also carried out and compared with simulation. Findings Simulation results for the designed HSACS were presented graphically and analyzed. The graphical results indicate that the coordination motions among the working mechanisms of HSACS are reasonable and no interference occurs. Casing test results show that the casing quality and production efficiency of HSACS equipment are satisfying. Originality/value In the present work, the developed virtual prototype models of HSACS provide reliable kinematic analysis results for the structural design of HSACS, and reasonable motion relations are realized in the designed structure of HSACS. Furthermore, not only the casing quality requirements can be met, but also the production efficiency of high-speed automatic casing machine for LIB is greatly improved via this kind of new HSACS structure.

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