Improving the Security of Data in the Internet of Things by Performing Data Aggregation Using Neural Network-Based Autoencoders

2020 ◽  
pp. 171-185
Author(s):  
Ab Rouf Khan ◽  
Mohammad Khalid Pandit ◽  
Shoaib Amin Banday
Keyword(s):  
Neural Network ◽  
Internet Of Things ◽  
Data Aggregation ◽  
The Internet ◽  
The Internet Of Things

scholarly journals Lifetime balanced data aggregation for the internet of things

2017 ◽  
Vol 58 ◽  
pp. 244-264 ◽  
Author(s):  
Zi Li ◽  
Wensheng Zhang ◽  
Daji Qiao ◽  
Yang Peng
Keyword(s):  
Internet Of Things ◽  
Data Aggregation ◽  
The Internet ◽  
The Internet Of Things

scholarly journals Energy-Optimal Data Aggregation and Dissemination for the Internet of Things

2018 ◽  
Vol 5 (2) ◽  
pp. 955-969 ◽  
Author(s):  
Emma Fitzgerald ◽  
Michal Pioro ◽  
Artur Tomaszwski
Keyword(s):  
Internet Of Things ◽  
Data Aggregation ◽  
The Internet ◽  
The Internet Of Things

scholarly journals Security-Aware Routing Protocol Based on Artificial Neural Network Algorithm and 6LoWPAN in the Internet of Things

2022 ◽  
Vol 2022 ◽  
pp. 1-8
Author(s):  
Jiangdong Lu ◽  
Dongfang Li ◽  
Penglong Wang ◽  
Fen Zheng ◽  
Meng Wang
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Internet Of Things ◽  
Routing Protocols ◽  
Intelligent Systems ◽  
Smart Devices ◽  
The Internet ◽  
Routing Mechanism ◽  
Artificial Neural ◽  
The Internet Of Things

Today, with increasing information technology such as the Internet of Things (IoT) in human life, interconnection and routing protocols need to find optimal solution for safe data transformation with various smart devices. Therefore, it is necessary to provide an enhanced solution to address routing issues with respect to new interconnection methodologies such as the 6LoWPAN protocol. The artificial neural network (ANN) is based on the structure of intelligent systems as a branch of machine interference, has shown magnificent results in previous studies to optimize security-aware routing protocols. In addition, IoT devices generate large amounts of data with variety and accuracy. Therefore, higher performance and better data handling can be achieved when this technology incorporates data for sending and receiving nodes in the environment. Therefore, this study presents a security-aware routing mechanism for IoT technologies. In addition, a comparative analysis of the relationship between previous approaches discusses with quality of service (QoS) factors such as throughput and accuracy for improving routing mechanism. Experimental results show that the use of time-division multiple access (TDMA) method to schedule the sending and receiving of data and the use of the 6LoWPAN protocol when routing the sending and receiving of data can carry out attacks with high accuracy.

scholarly journals The Model of Data Aggregation from Clustered Devices in the Internet of Things

2020 ◽  
Vol 17 (4) ◽  
pp. 156
Author(s):  
R. V. Faizullin ◽  
S. Hering
Keyword(s):  
Internet Of Things ◽  
Data Aggregation ◽  
The Internet ◽  
The Internet Of Things

Современные производственные системы могут включать множество элементов и связей между ними. Для эффективной работы системы необходимо оперативно собирать и обрабатывать информацию ее элементов. Целью статьи стало описание модели сбора данных с устройств интернета вещей на основе кластеризации. Количество устройств влияет на сложность организации работы системы. В основе схемы сбора данных должна быть выработана стратегия формирования маршрутов передачи информации. В статье описана возможность использования мобильных агентов для сбора данных. Оптимизация маршрутов агентов между узлами интернета вещей сводится к построению гамильтонова цикла (замкнутого со стартом в конкретной точке) с минимальными затратами на его прохождение (время, расстояние или др.). Для упрощения задачи предложено кластеризовать узлы внутри системы. В случае необходимости оптимизации маршрутов мобильных агентов определена функция, в которой учтены такие параметры, как расстояния между узлами системы, важность собираемой информации, энергия узлов и т. п., при этом за счет коэффициентов уравнения, которые задаются экспертно, можно влиять на процесс планирования маршрута для мобильных агентов в системах интернета вещей с учетом необходимого баланса между показателями производительности системы. Предложенная формулировка задачи позволяет использовать марковский процесс принятия решений для ее решения.

Security Model of Internet of Things Based on Binary Wavelet and Sparse Neural Network

2019 ◽  
Vol 10 (1) ◽  
pp. 1-17
Author(s):  
Zhihui Wang ◽  
Jingjing Yang ◽  
Benzhen Guo ◽  
Xiao Zhang
Keyword(s):  
Neural Network ◽  
Internet Of Things ◽  
Network Architecture ◽  
Safety Management ◽  
The Internet ◽  
Security Model ◽  
The Novel ◽  
Simulated Environment ◽  
Proposed Model ◽  
The Internet Of Things

At present, the internet of things has no standard system architecture. According to the requirements of universal sensing, reliable transmission, intelligent processing and the realization of human, human and the material, real-time communication between objects and things, the internet needs the open, hierarchical, extensible network architecture as the framework. The sensation equipment safe examination platform supports the platform through the open style scene examination to measure the equipment and provides the movement simulated environment, including each kind of movement and network environment and safety management center, turning on application gateway supports. It examines the knowledge library. Under this inspiration, this article proposes the novel security model based on the sparse neural network and wavelet analysis. The experiment indicates that the proposed model performs better compared with the other state-of-the-art algorithms.

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