Sangwhan Cha

Toward a Unified Framework for Mobile Applications

Mobile application developers and content providers usually need to develop mobile applications with concerns for mobility for specific wireless networks and device platforms which are used by network carriers. In order to provide standard mobile applications with interoperability and mobility support, in this paper we propose a comprehensive mobile application framework to support interoperability and mobility of mobile application development and operation. Such framework supports developing mobile device applications, mobile server applications, as well as mobile client-server communications and peer-to-peer communications.

Middleware Framework for Disconnection Tolerant Mobile Application Services

Mobile services are prone to failures caused by the disruption of an active wireless access network connection due to the user’s movement to other networks or signal blocking (shadowing). Thus, proper mechanisms for disconnection tolerant mobile application services are needed. In this paper, we propose a middleware framework that transparently performs required functionality for users in order to provide continuous mobile services in case of network disruption. Such middleware framework provides an effective disconnection tolerant mobile application service.

Developing a Real-Time Data Analytics Framework Using Hadoop

Currently, the majority of existing workflows are based on meta-heuristics that produce good heuristics that are dynamic in nature, and map the workflow tasks to services on-the-fly, but unfortunately, they lack the ability of supporting analytical tasks considering data types and real-time processing. This paper aims to address this problem by developing a real-time data analytics framework capable of handling real-time processing of structured and unstructured data needed for performing different analytical tasks, ranging from data ingestion and processing to data exploration, and visualization. We propose architecture based on the Storm/YARN projects for data ingestion, processing exploration and visualization of streaming structured and unstructured data. We have implemented the proposed architecture using Apache Storm related APIs for both of a local mode and a distributed mode. We describe our experiments for the architecture prototype implementation and evaluate the functional requirements for each component and non-functional tests such as real time update performance and time taken for data flow among components. All components were able to handle their own functionalities properly. Also, we provide the main results for a non-functional test in order to discuss our system efficiency.

The role of an IoT platform in the design of real-time recommender systems

The growth of Internet of Things (IoT) brings the promise of a wide range of new recommender systems due to the expected 57 billion smart connected devices by 2025. In this paper, we propose a new IoT platform for supporting a real-time recommender system. To illustrate the effectiveness of our proposed IoT platform, we present a prototype implementation and a tourism application to demonstrate the entire process from user event data collection to notification/recommendations provision. We conducted several experiments including notification and system performance tests to illustrate the use and performance of our real-time recommender system.

Optimizing pressure sensor array data for a smart-shoe fall monitoring system

Micro-sensors are now integral part of many technologically advanced healthcare systems used in monitoring elderly people who have high risk of fall and other mobility problems. Some theories have been established that relate inconsistency in the gait phases of a person to the possibility of an imminent fall. Using these theories, a number of monitoring systems have been developed to detect and predict falls. Smart shoe is one such solution presented here. It consists of an array of pressure sensors in the shape of a foot. Available sensors in the market do not have pressure sensors customizable to specific requirements such as a fall study. In this research, an optimized layout of pressure sensors is developed for a smart-shoe fall monitoring application. The data from the foot pressure arrays for different activities such as walking, falling forward etc. are collected. Data mining algorithms are then used to classify the fall types accurately and their performances are reported here.

Context Aware Middleware Services for Disconnection Tolerant Mobile Applications

To provide effective mobile services in spite of network disruption, context aware middleware services on mobile devices and application servers can provide services for continuation of applications, by managing mobile services, network connection, limited resources, and context information. In this paper, we propose context aware middleware services for disconnection tolerant mobile application services. Such middleware services manage runtime application and networking contexts for decision making of the middleware.

Developing a streaming data processing workflow for querying space–time activities from geotagged tweets

Abstract The critical dimensions in describing space–time activities are “what“, “where”, “when”, and “who”, which are frequently applied to collect data about basic functions people perform in space in the course of a day. Collecting data about these dimensions using activity-based surveys has presented researchers with a number of technical and social limitations, ranging from the restricted period of time participants have to record their activities to the level of accuracy with which participants complete a survey. This paper proposes a new streaming data processing workflow for querying space–time activities (STA) as a by-product of microblogging communication. It allows exploring a large volume of geotagged tweets to discover STA patterns of daily life in a systematic manner. A sequence of tasks have been implemented using different cloud-based computing resources for handling over one million of daily geotagged tweets from Canada for a period of six months. The STA patterns have revealed activity choices that might be attributable to personal motivations for communicating an activity in social networks.