Low-cost IoT-Based sensors dashboard for monitoring the state of health of mobile harbor cranes: Hardware and software description.

A cost-effective IoT-based real-time data acquisition and analysis hardware system was developed to enhance the performance of the mobile harbor cranes using a combination of a cost-effective quality control monitoring sensor dashboard (proximity sensors, angle position sensor, weight sensor, vibration sensor, and wind sensor), embedded microcontroller (Arduino), and embedded computer (Raspberry Pi). Hardware was operated using a specially developed novel Quality Control and Data Acquisition Multiprocessing software (QC-DAS). The QC-DAS can automatically collect and save real-time data of the sensors in a large-capacity SD card, monitor the state of health of the hardware, and transmit the real-time data of the sensors and the working state of the crane to an IoT server.

The proposed hybrid deep learning intrusion prediction IoT (HDLIP-IoT) framework.

Throughout the past few years, the Internet of Things (IoT) has grown in popularity because of its ease of use and flexibility. Cyber criminals are interested in IoT because it offers a variety of benefits for users, but it still poses many types of threats. The most common form of attack against IoT is Distributed Denial of Service (DDoS).

A hybrid trust computing approach for IoT using social similarity and machine learning.

Every year, millions of new devices are added to the Internet of things, which has both great benefits and serious security risks for user data privacy. It is the device owners' responsibility to ensure that the ownership settings of Internet of things devices are maintained, allowing them to communicate with other user devices autonomously. The ultimate goal of the future Internet of Things is for it to be able to make decisions on its own, without the need for human intervention.