Intelligent Authentication Method for Trusted Access of Mobile Nodes in Internet of Things Driven by Cloud Trust

2021 ◽  
pp. 232-241
Author(s):  
Shu Song ◽  
Lixin Jia
Keyword(s):  
Internet Of Things ◽  
Mobile Nodes

Improved Group-Based Signaling Scheme for Mobile Nodes in the Internet of Things Networks

2021 ◽  
pp. 417-434
Author(s):  
Safwan M. Ghaleb ◽  
Mustafa Man ◽  
Baraq Ghaleb ◽  
Faizah Aplop ◽  
Mukhtar Ghaleb ◽  
...  
Keyword(s):  
Internet Of Things ◽  
The Internet ◽  
Mobile Nodes ◽  
The Internet Of Things

scholarly journals Social Relation Predictive Model of Mobile Nodes in Internet of Things

2013 ◽  
Vol 19 (4) ◽  
Author(s):  
An Jian ◽  
Xiaolin Gui ◽  
Wendong Zhang ◽  
Xin He
Keyword(s):  
Internet Of Things ◽  
Predictive Model ◽  
Social Relation ◽  
Mobile Nodes

scholarly journals Survey of Localization for Internet of Things Nodes: Approaches, Challenges and Open Issues

2021 ◽  
Vol 13 (8) ◽  
pp. 210 ◽  
Author(s):  
Sheetal Ghorpade ◽  
Marco Zennaro ◽  
Bharat Chaudhari
Keyword(s):  
Internet Of Things ◽  
Performance Metrics ◽  
Smart Cities ◽  
Low Cost ◽  
Real Life ◽  
High Energy ◽  
Mobile Nodes ◽  
Node Localization ◽  
Iot Devices ◽  
Device Free

With exponential growth in the deployment of Internet of Things (IoT) devices, many new innovative and real-life applications are being developed. IoT supports such applications with the help of resource-constrained fixed as well as mobile nodes. These nodes can be placed in anything from vehicles to the human body to smart homes to smart factories. Mobility of the nodes enhances the network coverage and connectivity. One of the crucial requirements in IoT systems is the accurate and fast localization of its nodes with high energy efficiency and low cost. The localization process has several challenges. These challenges keep changing depending on the location and movement of nodes such as outdoor, indoor, with or without obstacles and so on. The performance of localization techniques greatly depends on the scenarios and conditions from which the nodes are traversing. Precise localization of nodes is very much required in many unique applications. Although several localization techniques and algorithms are available, there are still many challenges for the precise and efficient localization of the nodes. This paper classifies and discusses various state-of-the-art techniques proposed for IoT node localization in detail. It includes the different approaches such as centralized, distributed, iterative, ranged based, range free, device-based, device-free and their subtypes. Furthermore, the different performance metrics that can be used for localization, comparison of the different techniques, some prominent applications in smart cities and future directions are also covered.

scholarly journals A Node Density Control Learning Method for the Internet of Things

Sensors ◽  
2019 ◽  
Vol 19 (15) ◽  
pp. 3428 ◽  
Author(s):  
Shumei Lou ◽  
Gautam Srivastava ◽  
Shuai Liu
Keyword(s):  
Internet Of Things ◽  
Power Consumption ◽  
Sensor Nodes ◽  
Wireless Sensor ◽  
Learning Method ◽  
Mobile Nodes ◽  
Node Density ◽  
Wireless Sensor Nodes ◽  
Density Control ◽  
Control Learning

When examining density control learning methods for wireless sensor nodes, control time is often long and power consumption is usually very high. This paper proposes a node density control learning method for wireless sensor nodes and applies it to an environment based on Internet of Things architectures. Firstly, the characteristics of wireless sensors networks and the structure of mobile nodes are analyzed. Combined with the flexibility of wireless sensor networks and the degree of freedom of real-time processing and configuration of field programmable gate array (FPGA) data, a one-step transition probability matrix is introduced. In addition, the probability of arrival of signals between any pair of mobile nodes is also studied and calculated. Finally, the probability of signal connection between mobile nodes is close to 1, approximating the minimum node density at T. We simulate using a fully connected network identifying a worst-case test environment. Detailed experimental results show that our novel proposed method has shorter completion time and lower power consumption than previous attempts. We achieve high node density control as well at close to 90%.

Social Relation Cognitive Model of Mobile Nodes in the Internet of Things

2012 ◽  
Vol 35 (6) ◽  
pp. 1164 ◽  
Author(s):  
Jian AN ◽  
Xiao-Lin GUI ◽  
Wen-Dong ZHANG ◽  
Jing-Hua JIANG ◽  
Jin ZHANG
Keyword(s):  
Internet Of Things ◽  
Social Relation ◽  
Cognitive Model ◽  
The Internet ◽  
Mobile Nodes ◽  
The Internet Of Things

Research on social relations cognitive model of mobile nodes in Internet of Things

2013 ◽  
Vol 36 (2) ◽  
pp. 799-810 ◽  
Author(s):  
Jian An ◽  
Xiaolin Gui ◽  
Wendong Zhang ◽  
Jinhua Jiang ◽  
Jianwei Yang
Keyword(s):  
Internet Of Things ◽  
Social Relations ◽  
Cognitive Model ◽  
Mobile Nodes

RFID Protocol Design, Optimization, and Security for the Internet of Things

2017 ◽  
Author(s):  
Alex X. Liu ◽  
Muhammad Shahzad ◽  
Xiulong Liu ◽  
Keqiu Li
Keyword(s):  
Internet Of Things ◽  
Design Optimization ◽  
Protocol Design ◽  
The Internet ◽  
The Internet Of Things ◽  
Rfid Protocol
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