67,146 research outputs found
Mobile Agent based Market Basket Analysis on Cloud
This paper describes the design and development of a location-based mobile
shopping application for bakery product shops. Whole application is deployed on
cloud. The three-tier architecture consists of, front-end, middle-ware and
back-end. The front-end level is a location-based mobile shopping application
for android mobile devices, for purchasing bakery products of nearby places.
Front-end level also displays association among the purchased products. The
middle-ware level provides a web service to generate JSON (JavaScript Object
Notation) output from the relational database. It exchanges information and
data between mobile application and servers in cloud. The back-end level
provides the Apache Tomcat Web server and MySQL database. The application also
uses the Google Cloud Messaging for generating and sending notification of
orders to shopkeeper.Comment: 6 pages, 7 figure
Recent advances in industrial wireless sensor networks towards efficient management in IoT
With the accelerated development of Internet-of- Things (IoT), wireless sensor networks (WSN) are gaining importance in the continued advancement of information and communication technologies, and have been connected and integrated with Internet in vast industrial applications. However, given the fact that most wireless sensor devices are resource constrained and operate on batteries, the communication overhead and power consumption are therefore important issues for wireless sensor networks design. In order to efficiently manage these wireless sensor devices in a unified manner, the industrial authorities should be able to provide a network infrastructure supporting various WSN applications and services that facilitate the management of sensor-equipped real-world entities. This paper presents an overview of industrial ecosystem, technical architecture, industrial device management standards and our latest research activity in developing a WSN management system. The key approach to enable efficient and reliable management of WSN within such an infrastructure is a cross layer design of lightweight and cloud-based RESTful web service
Who you gonna call? Analyzing Web Requests in Android Applications
Relying on ubiquitous Internet connectivity, applications on mobile devices
frequently perform web requests during their execution. They fetch data for
users to interact with, invoke remote functionalities, or send user-generated
content or meta-data. These requests collectively reveal common practices of
mobile application development, like what external services are used and how,
and they point to possible negative effects like security and privacy
violations, or impacts on battery life. In this paper, we assess different ways
to analyze what web requests Android applications make. We start by presenting
dynamic data collected from running 20 randomly selected Android applications
and observing their network activity. Next, we present a static analysis tool,
Stringoid, that analyzes string concatenations in Android applications to
estimate constructed URL strings. Using Stringoid, we extract URLs from 30, 000
Android applications, and compare the performance with a simpler constant
extraction analysis. Finally, we present a discussion of the advantages and
limitations of dynamic and static analyses when extracting URLs, as we compare
the data extracted by Stringoid from the same 20 applications with the
dynamically collected data
Mobile content personalisation using intelligent user profile approach
As there are several limitations using mobile internet, mobile content personalisation seems to be an alternative to enhance the experience of using mobile internet. In this paper, we propose the mobile content personalisation framework to facilitate collaboration between the client and the server. This paper investigates clustering and classification techniques using K-means and Artificial Neural Networks (ANN) to predict user's desired content and WAP pages based on device's listed-oriented menu approach. We make use of the user profile and user's information ranking matrix to make prediction of the desired information for the user. Experimental results show that it can generate promising prediction. The results show that it works best when used for predicting 1 matched menu item on the screen
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