8 research outputs found
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
Lambda-Based Data Processing Architecture for Two-Level Load Forecasting in Residential Buildings
Building energy management systems (BEMS) have been intensively used to manage the electricity consumption of residential buildings more efficiently. However, the dynamic behavior of the occupants introduces uncertainty problems that affect the performance of the BEMS. To address this uncertainty problem, the BEMS may implement load forecasting as one of the BEMS modules. Load forecasting utilizes historical load data to compute model predictions for a specific time in the future. Recently, smart meters have been introduced to collect electricity consumption data. Smart meters not only capture aggregation data, but also individual data that is more frequently close to real-time. The processing of both smart meter data types for load forecasting can enhance the performance of the BEMS when confronted with uncertainty problems. The collection of smart meter data can be processed using a batch approach for short-term load forecasting, while the real-time smart meter data can be processed for very short-term load forecasting, which adjusts the short-term load forecasting to adapt to the dynamic behavior of the occupants. This approach requires different data processing techniques for aggregation and individual of smart meter data. In this paper, we propose Lambda-based data processing architecture to process the different types of smart meter data and implement the two-level load forecasting approach, which combines short-term and very short-term load forecasting techniques on top of our proposed data processing architecture. The proposed approach is expected to enhance the BEMS to address the uncertainty problem in order to process data in less time. Our experiment showed that the proposed approaches improved the accuracy by 7% compared to a typical BEMS with only one load forecasting technique, and had the lowest computation time when processing the smart meter data
Lambda-Based Data Processing Architecture for Two-Level Load Forecasting in Residential Buildings
Building energy management systems (BEMS) have been intensively used to manage the electricity consumption of residential buildings more efficiently. However, the dynamic behavior of the occupants introduces uncertainty problems that affect the performance of the BEMS. To address this uncertainty problem, the BEMS may implement load forecasting as one of the BEMS modules. Load forecasting utilizes historical load data to compute model predictions for a specific time in the future. Recently, smart meters have been introduced to collect electricity consumption data. Smart meters not only capture aggregation data, but also individual data that is more frequently close to real-time. The processing of both smart meter data types for load forecasting can enhance the performance of the BEMS when confronted with uncertainty problems. The collection of smart meter data can be processed using a batch approach for short-term load forecasting, while the real-time smart meter data can be processed for very short-term load forecasting, which adjusts the short-term load forecasting to adapt to the dynamic behavior of the occupants. This approach requires different data processing techniques for aggregation and individual of smart meter data. In this paper, we propose Lambda-based data processing architecture to process the different types of smart meter data and implement the two-level load forecasting approach, which combines short-term and very short-term load forecasting techniques on top of our proposed data processing architecture. The proposed approach is expected to enhance the BEMS to address the uncertainty problem in order to process data in less time. Our experiment showed that the proposed approaches improved the accuracy by 7% compared to a typical BEMS with only one load forecasting technique, and had the lowest computation time when processing the smart meter data
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