Prototype Development Of Distance Detection System Based On The Internet Of Things Using Esp 8266 Wifi Nodemcu Module

Abstract In the previous article about detecting train arrivals [4], we discussed the data transmission process which is the weakness of the system. This article discusses how to overcome these shortcomings. Distance detection systems need to be added to devices that are more powerful and faster in transmitting data. The prototype is built based on the development of the previous prototype. The initial product is a distance detector that has a motion sensor and a data transmission module in the form of Lora 400 MHz and a GSM module. Product development includes the addition of the NodeMCU WIFI module to existing devices. System development aims to: (1) Develop a distance detection system equipped with a WIFI module; and (2) Increase the data transmission speed of the distance detection system equipped with the WIFI NodeMCU module. The implementation method uses research and development techniques [3]. System development takes 6 months, with the stages of completion being system requirements analysis, hardware, and software design, system prototyping, and tool testing. Checklist data and delivery time are used as test data. Furthermore, the data is analyzed quantitatively to determine the achievement of results according to predetermined indicators. The device is equipped with a GPS module, a Lora-R02 receiver and transmitter module, and a Nodemcu ESP8266 module as a replacement for the 900A SIM module which transmits data faster. When the device is being tested, all the sensors of the tool work well at range of 125 meters (previously less than 100 meters). While the Lora module can react when the object has reached a distance of 300 meters. Data transmission previously using GSM modules took 10-13 seconds. After using the WIFI module, data transmission only takes 1-3 seconds.

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Design of multi-indicator integrated testing system for tobacco intelligent silk production line

Journal of Intelligent & Fuzzy Systems ◽

10.3233/jifs-211936 ◽

2021 ◽

pp. 1-13

Author(s):

Dayong Guo ◽

Qing Hu

Keyword(s):

Response Time ◽

Data Transmission ◽

Production Line ◽

Network Architecture ◽

Production Management ◽

Detection System ◽

System Response ◽

Testing System ◽

Ring Network ◽

Transmission Speed

Aiming at the problems of low precision, slow data transmission speed and long response time of silk quality and temperature control in tobacco intelligent production line, a multi-index testing system is designed. According to the characteristics of PROFIBUS fieldbus technology, combined with PROFIBUS transmission technology, a factory level information network is formed with PROFIBUS-DP as the exchange mode. Based on the PROFIBUS technology, the dual redundancy structure of control ring network and management information ring network is adopted, and the whole network architecture is constructed by logic layering. From the point of view of building enterprise MES system, it locates real-time production monitoring, production task receiving and production line related data collection, integrates equipment control layer, centralized monitoring layer and production management layer, and designs system function structure. The functional structure of the system, and the establishment of a number of data tables, to achieve a tobacco intelligent production line silk quality detection system design. Experimental results show that this method can effectively speed up the data transmission speed and shorten the system response time.

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Stray Light Analysis of Solar Photoelectric Detection System on Space Target

Advanced Materials Research ◽

10.4028/www.scientific.net/amr.466-467.196 ◽

2012 ◽

Vol 466-467 ◽

pp. 196-200

Author(s):

Ling Yin Li ◽

Yi Fan Wang ◽

Xiao Gang San

Keyword(s):

Mechanical Model ◽

Detection System ◽

Stray Light ◽

Coating Technology ◽

Detection Systems ◽

Geometry Model ◽

Photoelectric Detection ◽

System Requirements ◽

System Geometry ◽

Telescope System

Most conventional spacious solar photoelectric detection systems suffer from unexpected stray light. This paper demonstrates the effects on photoelectric detection system bring out by stray light. On this foundation, build up evaluation index of stray light system, to search the reasonable programme to eliminating stray light. A detailed telescope system geometry model was created, and scatter models were create for telescope and enclosure components. By the means of designing the structure of main baffle, baffle vane and the coating technology. In order to verify the prosperity of structure that designed, we utilizes software to analysis opto-mechanical model. we utilizes software to analysis opto-mechanical model. The simulations show that point source transmittance of the system is thus reduced by up to two orders of magnitude between 10-8 and 10-10. Meanwhile, the experiment obtains clean detection data which satisfies the system requirements. Stray light has been suppressed effectually, and so measurement precision was improved from detection graphics.

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Intelligent Botnet Detection Approach in Modern Applications

International Journal of Interactive Mobile Technologies (iJIM) ◽

10.3991/ijim.v15i16.24199 ◽

2021 ◽

Vol 15 (16) ◽

pp. 113

Author(s):

Khattab M. Ali Alheeti ◽

Ibrahim Alsukayti ◽

Mohammed Alreshoodi

Keyword(s):

Detection System ◽

Security And Privacy ◽

Sensitive Information ◽

Learning Networks ◽

Detection Systems ◽

Testing Dataset ◽

Detection Approach ◽

Secure Environment ◽

Iot Devices ◽

The Internet Of Things

Innovative applications are employed to enhance human-style life. The Internet of Things (IoT) is recently utilized in designing these environments. Therefore, security and privacy are considered essential parts to deploy and successful intelligent environments. In addition, most of the protection systems of IoT are vulnerable to various types of attacks. Hence, intrusion detection systems (IDS) have become crucial requirements for any modern design. In this paper, a new detection system is proposed to secure sensitive information of IoT devices. However, it is heavily based on deep learning networks. The protection system can provide a secure environment for IoT. To prove the efficiency of the proposed approach, the system was tested by using two datasets; normal and fuzzification datasets. The accuracy rate in the case of the normal testing dataset was 99.30%, while was 99.42% for the fuzzification testing dataset. The experimental results of the proposed system reflect its robustness, reliability, and efficiency.

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Academic Information System Islamic State University (UIN) Sunan Kalijaga Yogyakarta Android Based

IJID (International Journal on Informatics for Development) ◽

10.14421/ijid.2015.04101 ◽

2015 ◽

Vol 4 (1) ◽

pp. 1

Author(s):

Suryana Wijaya ◽

Muhammad Dzulfikar Fauzi ◽

Agung Fatwanto

Keyword(s):

Information System ◽

System Development ◽

Design System ◽

Islamic State ◽

State University ◽

Development Methodology ◽

Development Life Cycle ◽

History Of ◽

System Requirements ◽

Academic Information

The development of information systems is done so that the system gets better performance and is more in line with the wishes of users. However, until now there is no academic information system for UIN Sunan Kalijaga specifically developed based on mobile. In fact, mobile technology is currently growing very rapidly in Indonesia. Based on the Nielsen survey as of May 2011, the number of mobile device users in Indonesia reached 125 million out of the 238 million population. For this reason, it is necessary to develop the Academic Information System of UIN Sunan Kalijaga based on mobile, especially Android.The system development methodology used in this study is SDLC (Software Development Life Cycle). The steps are Analysis of system requirements, system design, and design, system implementation, testing, and evaluation.This research results an application that can be used to access academic information, such as schedule to attend the lecture, schedule of examination, card of yield study, presence and history of performance index. The special thing of this application is user still can see the academic information although offline with some condition.

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Intrusion Detection into Cloud-Fog-Based IoT Networks Using Game Theory

Wireless Communications and Mobile Computing ◽

10.1155/2020/8819545 ◽

2020 ◽

Vol 2020 ◽

pp. 1-9

Author(s):

Poria Pirozmand ◽

Mohsen Angoraj Ghafary ◽

Safieh Siadat ◽

Jiankang Ren

Keyword(s):

Game Theory ◽

Nash Equilibrium ◽

Internet Of Things ◽

Intrusion Detection ◽

Detection System ◽

Human Life ◽

Intrusion Detection Systems ◽

The Internet ◽

Detection Systems ◽

The Internet Of Things

The Internet of Things is an emerging technology that integrates the Internet and physical smart objects. This technology currently is used in many areas of human life, including education, agriculture, medicine, military and industrial processes, and trade. Integrating real-world objects with the Internet can pose security threats to many of our day-to-day activities. Intrusion detection systems (IDS) can be used in this technology as one of the security methods. In intrusion detection systems, early and correct detection (with high accuracy) of intrusions is considered very important. In this research, game theory is used to develop the performance of intrusion detection systems. In the proposed method, the attacker infiltration mode and the behavior of the intrusion detection system as a two-player and nonparticipatory dynamic game are completely analyzed and Nash equilibrium solution is used to create specific subgames. During the simulation performed using MATLAB software, various parameters were examined using the definitions of game theory and Nash equilibrium to extract the parameters that had the most accurate detection results. The results obtained from the simulation of the proposed method showed that the use of intrusion detection systems in the Internet of Things based on cloud-fog can be very effective in identifying attacks with the least amount of errors in this network.

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A Survey on SDN-based Intrusion Detection Systems on the Internet of Thing: Concepts, Issues, and Blockchain Applications

10.21203/rs.3.rs-694000/v1 ◽

2021 ◽

Author(s):

Heba A. Hassan ◽

Ezz E. Hemdan ◽

Walid El-Shafai ◽

Mona Shokair ◽

Fathi E. Abd El-Samie

Keyword(s):

Intrusion Detection ◽

Computer Networks ◽

Detection System ◽

Intrusion Detection Systems ◽

Survey Study ◽

The Internet ◽

Detection Systems ◽

Systems Software ◽

The Internet Of Things ◽

Sdn Controller

Abstract With the accelerated development of computer networks utilization and the enormous growth of the number of applications running on top of it, network security becomes more significant. Intrusion Detection Systems (IDS) is considered as one of the essential tools utilized to protect computer networks and information systems. Software-defined network (SDN) architecture is used to provide network monitoring and analysis mechanism due to the programming environment of the SDN controller. On the other hand intrusion detection system is developed to monitor incoming traffic to the SDN network; hence it enables SDN to adjust security service insertion. This paper presents a survey study for SDN with the Internet of Things (IoT) and its improved versions like SDN-based IDS and SDN-based IoT. Likewise, discussing the IoT and its problems, especially the security aspects and solutions to overcome these problems. Finally, a brief description of the Blockchain concept and how it can be merged with an SDN-based IoT system to further enhance its security aspects is provided.

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Toward a deep learning-based intrusion detection system for IoT against botnet attacks

IAES International Journal of Artificial Intelligence (IJ-AI) ◽

10.11591/ijai.v10.i1.pp110-120 ◽

2021 ◽

Vol 10 (1) ◽

pp. 110

Author(s):

Idriss Idrissi ◽

Mohammed Boukabous ◽

Mostafa Azizi ◽

Omar Moussaoui ◽

Hakim El Fadili

Keyword(s):

Deep Learning ◽

Intrusion Detection ◽

Intrusion Detection System ◽

Detection System ◽

Traffic Data ◽

Security Attacks ◽

Security Breaches ◽

Detection Systems ◽

Learning Techniques ◽

The Internet Of Things

The massive network traffic data between connected devices in the internet of things have taken a big challenge to many traditional intrusion detection systems (IDS) to find probable security breaches. However, security attacks lean towards unpredictability. There are numerous difficulties to build up adaptable and powerful IDS for IoT in order to avoid false alerts and ensure a high recognition precision against attacks, especially with the rising of Botnet attacks. These attacks can even make harmless devices becoming zombies that send malicious traffic and disturb the network. In this paper, we propose a new IDS solution, baptized BotIDS, based on deep learning convolutional neural networks (CNN). The main interest of this work is to design, implement and test our IDS against some well-known Botnet attacks using a specific Bot-IoT dataset. Compared to other deep learning techniques, such as simple RNN, LSTM and GRU, the obtained results of our BotIDS are promising with 99.94% in validation accuracy, 0.58% in validation loss, and the prediction execution time is less than 0.34 ms.

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Sustainable Security for the Internet of Things Using Artificial Intelligence Architectures

ACM Transactions on Internet Technology ◽

10.1145/3448614 ◽

2021 ◽

Vol 21 (3) ◽

pp. 1-22

Author(s):

Celestine Iwendi ◽

Saif Ur Rehman ◽

Abdul Rehman Javed ◽

Suleman Khan ◽

Gautam Srivastava

Keyword(s):

Internet Of Things ◽

Detection System ◽

Experimental Testing ◽

Cyber Attacks ◽

Internet Technology ◽

The Internet ◽

Business Operations ◽

Private Data ◽

The Internet Of Things ◽

Better Than

In this digital age, human dependency on technology in various fields has been increasing tremendously. Torrential amounts of different electronic products are being manufactured daily for everyday use. With this advancement in the world of Internet technology, cybersecurity of software and hardware systems are now prerequisites for major business’ operations. Every technology on the market has multiple vulnerabilities that are exploited by hackers and cyber-criminals daily to manipulate data sometimes for malicious purposes. In any system, the Intrusion Detection System (IDS) is a fundamental component for ensuring the security of devices from digital attacks. Recognition of new developing digital threats is getting harder for existing IDS. Furthermore, advanced frameworks are required for IDS to function both efficiently and effectively. The commonly observed cyber-attacks in the business domain include minor attacks used for stealing private data. This article presents a deep learning methodology for detecting cyber-attacks on the Internet of Things using a Long Short Term Networks classifier. Our extensive experimental testing show an Accuracy of 99.09%, F1-score of 99.46%, and Recall of 99.51%, respectively. A detailed metric representing our results in tabular form was used to compare how our model was better than other state-of-the-art models in detecting cyber-attacks with proficiency.

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Design of a Microwave Power Detection System in the 5G-Communication Frequency Band

Sensors ◽

10.3390/s21082674 ◽

2021 ◽

Vol 21 (8) ◽

pp. 2674

Author(s):

Qingying Ren ◽

Wen Zuo ◽

Jie Xu ◽

Leisheng Jin ◽

Wei Li ◽

...

Keyword(s):

Frequency Band ◽

Microwave Power ◽

Communication Systems ◽

Detection System ◽

Measurement Sensitivity ◽

Detection Range ◽

Detection Systems ◽

Detection Frequency ◽

Communication Frequency ◽

5G Communication

At present, the proposed microwave power detection systems cannot provide a high dynamic detection range and measurement sensitivity at the same time. Additionally, the frequency band of these detection systems cannot cover the 5G-communication frequency band. In this work, a novel microwave power detection system is proposed to measure the power of the 5G-communication frequency band. The detection system is composed of a signal receiving module, a power detection module and a data processing module. Experiments show that the detection frequency band of this system ranges from 1.4 GHz to 5.3 GHz, the dynamic measurement range is 70 dB, the minimum detection power is −68 dBm, and the sensitivity is 22.3 mV/dBm. Compared with other detection systems, the performance of this detection system in the 5G-communication frequency band is significantly improved. Therefore, this microwave power detection system has certain reference significance and application value in the microwave signal detection of 5G communication systems.

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