It sounds like mission impossible to connect everything on the Earth together via Internet, but Internet of Things (IoT) will dramatically change our life in the foreseeable future, by making many "impossibles" possible. To many, the massive data generated or captured by IoT are considered having highly useful and valuable information. Data mining will no doubt play a critical role in making this kind of system smart enough to provide more convenient services and environments. This paper begins with a discussion of the IoT. Then, a brief review of the features of "data from IoT" and "data mining for IoT' is given. Finally, changes, potentials, open issues, and future trends of this field are addressed.

Data Mining for Internet of Things: A Survey

A review of smart home applications based on Internet of Things

Welcome to the CHI 2006 Extended Abstracts. We hope that you will enjoy this year's extended abstracts and the changes that we have made. For the first time this year, we encouraged submissions from six different sub-communities of the human-computer interaction (HCI) field: design; education; engineering; management; research and usability. The intent was to make sure that the technical program included elements that would be of interest to the broad HCI world. We especially sought Experience Reports, Panels, SIGs, and HCI Overviews. Submissions to each of these were carefully reviewed by the Conference Committee and their respective Community Chairs. These Extended Abstracts include these new or modified venues as well as materials from other traditional CHI venues.

CHI '06 Extended Abstracts on Human Factors in Computing Systems

Manufacturing is involved with complex job shop scheduling problems (JSP). In smart factories, edge computing supports computing resources at the edge of production in a distributed way to reduce response time of making production decisions. However, most works on JSP did not consider edge computing. Therefore, this paper proposes a smart manufacturing factory framework based on edge computing, and further investigates the JSP under such a framework. With recent success of some AI applications, the deep Q network (DQN), which combines deep learning and reinforcement learning, has showed its great computing power to solve complex problems. Therefore, we adjust the DQN with an edge computing framework to solve the JSP. Different from the classical DQN with only one decision, this paper extends the DQN to address the decisions of multiple edge devices. Simulation results show that the proposed method performs better than the other methods using only one dispatching rule.

Smart Manufacturing Scheduling With Edge Computing Using Multiclass Deep Q Network

Traditional waste management system operates based on daily schedule which is highly inefficient and costly. The existing recycle bin has also proved its ineffectiveness in the public as people do not recycle their waste properly. With the development of Internet of Things (IoT) and Artificial Intelligence (AI), the traditional waste management system can be replaced with smart sensors embedded into the system to perform real time monitoring and allow for better waste management. The aim of this research is to develop a smart waste management system using LoRa communication protocol and TensorFlow based deep learning model. LoRa sends the sensor data and Tensorflow performs real time object detection and classification. The bin consists of several compartments to segregate the waste including metal, plastic, paper, and general waste compartment which are controlled by the servo motors. Object detection and waste classification is done in TensorFlow framework with pre-trained object detection model. This object detection model is trained with images of waste to generate a frozen inference graph used for object detection which is done through a camera connected to the Raspberry Pi 3 Model B+ as the main processing unit. Ultrasonic sensor is embedded into each waste compartment to monitor the filling level of the waste. GPS module is integrated to monitor the location and real time of the bin. LoRa communication protocol is used to transmit data about the location, real time and filling level of the bin. RFID module is embedded for the purpose of waste management personnel identification.

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An Internet of Things Based Smart Waste Management System Using LoRa and Tensorflow Deep Learning Model

In general, patients who are unwell do not know with which outpatient department they should register, and can only get advice after they are diagnosed by a family doctor. This may cause a waste of time and medical resources. In this paper, we propose an attention-based bidirectional long short-term memory (Att-BiLSTM) model for service robots, which has the ability to classify outpatient categories according to textual content. With the outpatient text classification system, users can talk about their situation to a service robot and the robot can tell them which clinic they should register with. In the implementation of the proposed method, dialog text of users in the Taiwan E Hospital were collected as the training data set. Through natural language processing (NLP), the information in the dialog text was extracted, sorted, and converted to train the long-short term memory (LSTM) deep learning model. Experimental results verify the ability of the robot to respond to questions autonomously through acquired casual knowledge.

https://www.mdpi.com/2078-2489/11/2/106/pdf

Outpatient Text Classification Using Attention-Based Bidirectional LSTM for Robot-Assisted Servicing in Hospital

The smart house is an important issue in the orange technology. In this paper, we proposes a work which applies the Internet of things (IOT) and motion sensing on smart house, denoted by Smart House Monitor & Manager (SHMM), to improve the convenience, safety, and power-saving of the house. It is based on the Zigbee sensor connected all sensors and actuators. And we have implemented the SHMM with a smart house to demonstrate the availability.

An application of Internet of things with motion sensing on smart house

This paper presents an effective and efficient method for speeding up ant colony optimization (ACO) in solving the codebook generation problem. The proposed method is inspired by the fact that many computations during the convergence process of ant-based algorithms are essentially redundant and thus can be eliminated to boost their convergence speed, especially for large and complex problems. To evaluate the performance of the proposed method, we compare it with several state-of-the-art metaheuristic algorithms. Our simulation results indicate that the proposed method can significantly reduce the computation time of ACO-based algorithms evaluated in this paper while at the same time providing results that match or outperform those ACO by itself can provide.

PREACO: A fast ant colony optimization for codebook generation

In this paper, we present an efficient method for speeding up Ant Colony Optimization (ACO), called Pattern Reduction Enhanced Ant Colony Optimization (PREACO). The proposed algorithm is motivated by the observation that many of the computations of ACO on its convergence process are essentially redundant and thus can be eliminated to reduce its computation time. To evaluate the performance of the proposed algorithm, we use it to solve the the traveling salesman problem (TSP). Moreover, we compare the proposed algorithm with several state-of-the-art ACO-based algorithms. Our simulation results indicate that the proposed algorithm can reduce the computation time of ACO algorithms we evaluated up to 99.21% or by a factor of 126.58 while limiting the degradation of the quality of the solution to a very small percentage compared to ACO algorithms themselves.

A fast Ant Colony Optimization for traveling salesman problem

This work briefly introduces a newly emerging discipline called orange technology for humanistic care applications. Meanwhile, an interdisciplinary project “Orange technology: Research and implementation of advanced health, happiness, and care technologies” based on such a discipline is also presented in this paper. The project covers a wide range of studies, including theoretical research, application development, and national policy. The goal is to finally promote the well-being in our modern society.

Shih-Pang Tseng

Papers

Outpatient Text Classification Using Attention-Based Bidirectional LSTM for Robot-Assisted Servicing in Hospital

An application of Internet of things with motion sensing on smart house

PREACO: A fast ant colony optimization for codebook generation

A fast Ant Colony Optimization for traveling salesman problem

Human-centric technology based on orange computing