Analysis of Bluetooth RSSI for Proximity Detection of Ship Passengers

Concern about the health of people who traveled onboard was raised during the COVID-19 outbreak on the Diamond Princess cruise ship. The ship’s narrow space offers an environment conducive to the virus’s spread. Close contact isolation remains one of the most critical current measures to stop the virus’s rapid spread. Contacts can be identified efficiently by detecting intelligent devices nearby. The smartphone’s Bluetooth RSSI signal is essential data for proximity detection. This paper analyzes Bluetooth RSSI signals available to the public and compares RSSI signals in two distinct poses: standing and sitting. These features can improve accuracy and provide an essential basis for creating algorithms for proximity detection. This allows for improved accuracy in identifying close contacts and can help ships sustainably manage persons onboard in the post-epidemic era.

Internet of Robotic Things in Surveillance

These days a new field has emerged known as IoRT which is a combination of IOT and Robotics and known as Internet of Robotic Things. Through IORT, intelligent devices can monitor events, fuse sensor data from a variety of sources, use local and distributed intelligence to determine a best course of action, and then act to control or manipulate objects in the physical world and physically moving through that world. This paper mainly focuses on application of IoRT as a surveillance robot with audio and video features in the domain of security. Keywords: IOT, Robotics, Surveillance Robot, Ardino, Sensors, Raspberry Pi, Robotic control.

Optimal Compression of Remote Sensing Images Using Deep Learning during Transmission of Data

Industrial internet of things has grown quite popular in recent years and involves a large number of intelligent devices linked together to build a system that can investigate, communicate, gather and observe information. Due to this requirement, there is more demand for compression techniques which compresses data, leading to less usage of resources and low complexity. This is where Convolutional Neural Networks (CNN) play a large role in the field of computer vision, especially in places where high applications such as interpretation coupled with detection is required. Similarly, low-level applications such as image compression cannot be resolved using this methodology. In this paper, a compression technique for remote sensing images using CNN is proposed. This methodology incorporates CNN in a compact learning environment wherein the actual image that consists of structural data is coded using Lempel Ziv Markov chain algorithm. This process is followed by image reconstruction in order to obtain the actual image in high quality. Other methodologies such as optimized trunctiona, JPEG2000, JPEC and binary tree were compared using a large number of experiments in terms of space saving, reconstructed image quality and efficiency. The output obtained indicates that the proposed methodology shows effective improvement, attaining a 50 dB signal to noise ratio and space saving of 90%.

Prognostics-aware multi-robot route planning to extend the lifetime

Transformation to Industry 4.0, manufacturing systems need more intelligent devices with capable of self-awareness. Prognostic-aware robotic systems are one of key components for the self-awareness in manufacturing. The prognostics-aware route planning is one of the key components for the success of the multi-robot team during the long-term and uninterrupted operations with also extending lifetime and reducing maintenances costs. In this study, a Prognostics-aware Multi-Robot Route Planning (P-MRRP) algorithm is proposed for extending lifetime of the robot team. In the P-MRRP algorithm, firstly routes are obtained from route set construction algorithm and most reliable route set is selected by calculating Probability of Route Completion (PoRC) according to reliability of the robot team. The proposed algorithm also considers effect of load during the route of robots. In this study, the reliability of the robot is updated considering both the travelled distances with route of robot and the load of robot between pickup and/or delivery nodes. The results of P-MRRP algorithm are compared with the results of classical MRRP. The performance of the algorithm shows that the lifetime of mobile robot team can be extended by using the P-MRRP algorithm.

Design of Central Air Conditioning Control Acquisition Device Based on IoT Technology

With the rapid development of Internet and electronic technology, the Internet of things (hereinafter referred to as IOT) has become an important way of many intelligent devices, which can improve people’s application of intelligent devices. Through the IOT, we can carry out the research on the centralized monitoring system of central air conditioning [1]. Through the IOT, we can realize the IOT of indoor and outdoor units of central air conditioning, which can realize the important application of practical engineering projects. Through fuzzy algorithm, this paper can improve the overall improvement of the project. Through the cooperation of MCU and GPRS module, we can complete the wireless transmission of data., This can realize computer terminal monitoring and mobile terminal monitoring. Through engineering application, we can realize the automation and information integration of system monitoring, which will greatly improve the monitoring efficiency. Through the development of acquisition device, we can improve the comfort and security of central air conditioning, which will enhance the communication between the physical equipment of the system and the upper server network. Firstly, this paper analyzes the flow of fuzzy PID algorithm. Then, this paper specifies the system diagram of the central air conditioning control acquisition system [2]. Finally, this paper puts forward the important work of system acquisition.

Ontological base models machine-to-machine M2M applied to the internet of things IOT

Machine-to-Machine M2M technology being a specific discourse universe of the Internet of Things IoT for the connectivity of intelligent devices, the support of said environment requires a basic conceptual scheme; for which the present article, proposes an evaluation about the different ontological models that consider the M2M and the IoT in simultaneous, recognizing the syntactic and semantic capacity of the interoperability of such devices, from the study of the basic schemes in mention, and identifying its most outstanding properties according to the Quality of Service QoS metric, obtaining the oneM2M ontology as the most appropriate.

MyMaster : A Multifactor Authentication Scheme for Smart Home Device

Smart homes are one of the Internet of Things (IoT) applications most significant to enable people to operate intelligent devices on the Internet in their homes. However, when users can access an intelligent home system remotely, they have major privacy and confidentiality difficulties to overcome. Nothing has been done to improve the safety characteristics of an intelligent home with current research on authentication approaches. For example, to address these issues and to develop a reciprocal tracking authentication system with a critical aspect of a deal, we recommend an Internet based Smart Home System (IFTTT) model. As a controller and a safety guard, an IFTTT-Home Gateway provides a user with remote access to a Smart Home System within their company. The system is designed for mutual authentication with security features such as anonymity and full advance security by using Elliptical Curve Encryption, Nonces, XOR or cryptographic Hash functions. We also incorporate multi factor authentication (MFA) into the model to ensure more security and preventing privacy leakage.

An Intelligent Path Selection Algorithm Based on Deep Reinforcement Learning

Path selection is the most important algorithm in intelligent devices such as robots. At present, the traditional path-planning algorithm has achieved some results, but it lacks the ability of environmental perception and continuous learning. In order to solve the above problems, this paper proposes an intelligent path selection algorithm based on deep reinforcement learning, which uses the learning ability of deep learning and the decision-making ability of reinforcement learning to realize the autonomous path planning of robots and other equipment. Simulation results show that the proposed algorithm has faster convergence, efficiency and accuracy.

Mata: An Android Eye-Tracking Based User Interface Control Application

The advent of smartphone technology has provided us with intelligent devices for communication as well as playing game. Unfortunately, applications that exploit available sensors in the smartphone are mostly designed for people with no physical handicap. This paper presents Mata, a game user interface using eye-tracking to operate and control games running on Android smartphone. This system is designed to enhance user experiences and help motoric impaired peoples in using smartphone for playing games. Development and testing of the Mata system has proven the concepts of eye-tracking and eyegazing usage as unimodal input for game user interface.