Smart streetlight is an outdoor infrastructure that uses technologies, such as sensors and actuators, to provide intelligent outdoor lighting and replace the power-consuming traditional streetlights. Although the current smart streetlight systems are employing these technologies for the maintenance, they simply gather data wirelessly on a periodical basis and still have drawbacks to provide real-time monitoring operation. To address these issues, the implementation of an IoT-based low-delay smart streetlight monitoring system is proposed in this article. In addition, a data filtering algorithm is also proposed in this article where redundant data are ignored to avoid overloading and excessive data storage consumption. Implementation results show that the proposed monitoring system and data filtering algorithm are able to provide real-time monitoring of smart streetlights with minimal time execution up to 0.11 ms and greatly reduce data storage usage up to 88.57%, respectively. 

Implementation of IoT-Based Low-Delay Smart Streetlight Monitoring System

Smart streetlight is an outdoor infrastructure that uses technologies, such as sensors and actuators, to provide intelligent outdoor lighting and replace the power-consuming traditional streetlights. Although the current smart streetlight systems are employing these technologies for the maintenance, they simply gather data wirelessly on a periodical basis and still have drawbacks to provide real-time monitoring operation. To address these issues, the implementation of an IoT-based low-delay smart streetlight monitoring system is proposed in this article. In addition, a data filtering algorithm is also proposed in this article where redundant data are ignored to avoid overloading and excessive data storage consumption. Implementation results show that the proposed monitoring system and data filtering algorithm are able to provide real-time monitoring of smart streetlights with minimal time execution up to 0.11 ms and greatly reduce data storage usage up to 88.57%, respectively.

The Internet of Things (IoT) is the interconnection of things around us to make our daily process more efficient by providing more comfort and productivity. However, these connections also reveal a lot of sensitive data. Therefore, thinking about the methods of information security and coding are important as the security approaches that rely heavily on coding are not a strong match for these restricted devices. Consequently, this research aims to contribute to filling this gap, which adopts machine learning techniques to enhance network-level security in the low-power devices that use the lightweight MQTT protocol for their work. This study used a set of tools tools and, through various techniques, trained the proposed system ranging from Ensemble methods to deep learning models. The system has come to know what type of attack has occurred, which helps protect IoT devices. The log loss of the Ensemble methods is 0.44, and the accuracy of multi-class classification is 98.72% after converting the table data into an image set. The work also uses a Convolution Neural Network, which has a log loss of 0.019 and an accuracy of 99.3%. It also aims to implement these functions in IDS.

https://www.beei.org/index.php/EEI/article/download/2766/2137

Automated diagnosis of attacks in internet of things using machine learning and frequency distribution techniques

Due to the tremendous progress in the automotive industry, the growth of the urban population, the number of vehicles is increasing and this creates parking challenges. Intelligent parking management systems offer an optimal solution for finding empty parking space so that drivers can quickly find their car parking space. To solve these problems, it is necessary to design an intelligent parking system, in addition to providing comfort to drivers, which is also economically viable. This paper proposes an intelligent multi-storey car parking system with the help of RFID technology and examining user preferences that can effectively solve car parking problems. The proposed method is a multi-objective decision-making method to reduce the problem of car parking, which is called MODMRPCP. Therefore, the proposed MODM-RPCP method can allocate the best space for their stopping place by using the decision-making system and based on the priorities considered by the users. The simulation results show that the MODM-RPCP reduces the average booking time more than 19.2% and 27.1%, and decreases the response time of central parking management server more than 20.1% and 29.78% compared to MOGWOLA and ODPP approaches.

https://www.preprints.org/manuscript/202011.0738/v1/download

An Intelligent Parking Management System using RFID Technology based on User Preferences

An intelligent parking management system using RFID technology based on user preferences

Smart parking system as a part of smart city development has been widely proposed with several research. In this research, proposed a system of parking management application that functions to monitor and control the location of parking slot that can be used by the parking management and parking users. The web application connected to ultrasonic sensor and GPS using MQTT protocol and real-time database. The research used modify algorithm of the SSGA, to optimize the allocation of empty parking slot and MQTT protocol to obtain the faster response time of the system when many users are accessing the website application. The results obtain a variation of sending delays from the client publish to firebase at 4 seconds. Meanwhile, for the sending delay from the broker to firebase the variation was at 2 seconds for each time of data sending.

Radya Fadhil

Papers

Smart parking management system using SSGA MQTT and real-time database