Exploring Significant Motion Sensor for Energy-efficient Continuous Motion and Location Sampling in Mobile Sensing Application

The significant motion sensor is a new sensor that promises motion detection at low power consumption. Despite that promise, no known research has explored the usage of this sensor, especially in mobile sensing research. In this study, we explore the utilization of this significant motion sensor for continuous motion and location sampling in a mobile sensing application. A location sensor is known for its expensive power consumption in retrieving the location data, and continuously sampling from it will quickly deplete a smartphone battery. We experiment with two sampling strategies that utilize this significant motion sensor to achieve low power consumption during continuous sampling. One strategy involves utilizing the sensor naively, while the other involves combining with the duty cycle. Both strategies achieve low energy consumption, but the one that combines with the duty cycle achieves lower energy consumption. By utilizing this sensor, mobile sensing research especially that samples data from location or motion sensors, will be able to achieve lower energy consumption

The Local Innovation Perspective: Development of Mobile-Herbal Service for Indonesia's Mobile Cellular Market

This paper reports the local-innovation perspective for the Indonesian mobile cellular market. Under the local perspective, the innovation opportunity appears when it suit characteristics of the country and the behavior nature of its people such concept is built into realization by making an applications of mobile-herbal (m-herbal) services applications. The service is designed by following the proposed framework, starting from scanning the market demand, defining specific applications, defining the actors, exploration of tacit knowledge of the actors, engineering development, implantation innovation and concurrent innovative development. We used the results of previous market survey emphasizing a need of health-related service for Indonesian market. Herbal remedy was chosen as the focal point of health-related service development since it is well-known indigenous method of treatment by using Indonesian natural ingredients. The service is developed to run on the Android-based smartphone, connects to the database called Indonesian HerbalDB. It consists two main features, i.e. query of herbal remedies and self evaluation assessment. Users of the services may search the names of Indonesian traditional plantation, its local names, and the kind of disease which can be cured. Through the features of self evaluation assessment, users are encouraged to give their personal perception of the herbal remedies, and being the recommendation to other users afterward. Finally, our proposed framework signifies the importance of communications channel among the actors in the mobile cellular facilitating mutual interaction between the multiple actors involved in the mobile herbal development

Concept, design and implementation of sensing as a service framework

Today, personal data is becoming a new economic asset. The data that we generated from our smartphone, our interaction in social media, its like oil in the Internet. An increasing of personal data in the internet cause some issue such as privacy issue, complexity processing, and etc. That will require a highly reliable, available, serviceable, and secure infrastructure at its core and robust innovation. This paper propose a new concept, design and implementation of sensing as a service framework. This framework has three main components: 1) personal data collector application; 2) web portal that has user friendly interface and shows responsive analysis result of personal data that can be accessed by the users based on new web technology; 3) REST API feature and documentation for third party. The goals of this paper are: 1) to provide rich application as a service based on users personal data log; 2) to bridging the user and third party such as developers in term of developing applications and researcher for research based on user personal data; 3) to develop reliable, available, serviceable and secure sensing as a service framework

Fog Computing-Based System for Decentralized Smart Parking System by Using Firebase

The growth of vehicle number is unavoidable whilst the availability of parking is not directly proportional with this condition. Nowadays, many shopping centers do not have sufficient parking spot, causing customers to have difficulty in finding available parking spots. Research has been conducted to tackle the issue of finding available parking spots. Much of this research proposed the narrowband-Internet of things (NB-IoT) as a fog node. For communication purposes, this NB-IoT-based fog node has some shortcomings, such as security and privacy, lower data rate, higher cost in development, dependency with wireless system, and only covers one area. In this research, the fog computing was proposed to decentralize smart parking system by using Firebase to cover several areas or malls in one system and interface. Instead of using NB-IoT, this research employed decentralized local server as a fog node to deliver a fast data exchange. Firestore database (Firebase) was also used to secure, manage, and analyze the data in the cloud. Conjunctively, the Android application was created as a user interface to book and find the availability of parking spots. The Android application was built using Android Studio and implemented authentication to keep the data access secure and private. The testing scenario was done following the design unified modeling language (UML). The research results confirmed that the fog computing system successfully supported the decentralized smart parking system and was able to be implemented for covering several areas or malls in one system

Developing and evaluating mobile sensing for smart home control

Many of researches in controlling smart home system have been proposed. Most of previous approaches in controlling smart home system requires interventions and commands from user. This paper propose a system about smart home based on mobile sensing that does not requires interventions and commands from the user. Mobile Sensing is used to records daily routine activities of the user. Then the system automatically gives a response to user based on his/her daily routine activities. We have implemented our approach to demonstrate the feasibility and effectiveness of using mobile sensing for controlling smart home system. Furthermore, we evaluate our approach and present the details in this paper

A Mixed Malay–English Language COVID-19 Twitter Dataset: A Sentiment Analysis

Social media has evolved into a platform for the dissemination of information, including fake news. There is a lot of false information about the current situation of the Coronavirus Disease 2019 (COVID-19) pandemic, such as false information regarding vaccination. In this paper, we focus on sentiment analysis for Malaysian COVID-19-related news on social media such as Twitter. Tweets in Malaysia are often a combination of Malay, English, and Chinese with plenty of short forms, symbols, emojis, and emoticons within the maximum length of a tweet. The contributions of this paper are twofold. Firstly, we built a multilingual COVID-19 Twitter dataset, comprising tweets written from 1 September 2021 to 12 December 2021. In particular, we collected 108,246 tweets, with over 67% in Malay language, 27% in English, 2% in Chinese, and 4% in other languages. We then manually annotated and assigned the sentiment of 11,568 tweets into three-class sentiments (positive, negative, and neutral) to develop a Malay-language sentiment analysis tool. For this purpose, we applied a data compression method using Byte-Pair Encoding (BPE) on the texts and used two deep learning approaches, i.e., the Multilingual Bidirectional Encoder Representation for Transformer (M-BERT) and convolutional neural network (CNN). BPE tokenization is used to encode rare and unknown words into smaller meaningful subwords. With the CNN, we converted the labeled tweets into image files. Our experiments explored different BPE vocabulary sizes with our BPE-Text-to-Image-CNN and BPE-M-BERT models. The results show that the optimal vocabulary size for BPE is 12,000; any values beyond that would not contribute much to the F1-score. Overall, our results show that BPE-M-BERT slightly outperforms the CNN model, thereby showing that the pre-trained M-BERT network has the advantage for our multilingual dataset