Performance Analysis of a Novel LBS Application Using MBMS&TPEG in 3G Mobile Networks

Abstract

Location-based services (LBS) provide content that is dynamically customized according to the user's location. These services are commonly delivered to mobile devices. Due to wireless bandwidth limit, broadcasting based transmission technology is one of main methods to provide dynamic real-time information or public emergency for commercial LBS applications. Multimedia Broadcast and Multicast Services (MBMS) is a broadcasting service offered via existing GSM and UMTS cellular networks. MBMS has the major benefit that the network infrastructure already exists for mobile network operators and the deployment can be cost effective compared with building a new network for the services. However, Location-based services historically were built as stand-alone applications that did not communicate easily with other applications and systems. Therefore, open standards will be a necessary catalyst for LBS growth. This paper proposes a novel LBS application framework based on the TPEG protocol (transport protocol experts group) over MBMS and explains the implement of that using the stream delivery method and download delivery method. For download service, Results of performance tests for FLUTE protocol (File Delivery under unidirectional Transport) and FEC (Forward error correction) are also presented

A Study of 3G Network Performance Using Video Quality Assessment: Bangkok Case Study

“…The Third-Generation International Mobile Telecommunications-2000 (IMT-2000) and the Universal Mobile Telecommunications System (UMTS) were specified by the International Telecommunication Union and the Third Generation Partnership Project (3GPP) respectively [3][4][5]. 3G networks support several services, including mobile internet access, video calls and mobile TV with data rate of ≥ 200 Kbps.…”

Data-Replicas Scheduler for Heterogeneous MapReduce Cluster

“…Studies [3] [7] utilizes data locality in VMs to improve the performance. Study [4] builds a model that defines metrics to analyze the data allocation problem. Study [7] proposes a MapReduce framework on virtual machine which takes full advantage of data locality, virtual machine live migration and checkpoint.…”

“…Hadoop, which is the most popular open-sourced MapReduce framework implementation, is used by Amazon, Adobe, Ebay, Facebook, IBM, Last.fm, Twitter, CMU and ETH etc. almost 150 corporations and research center [4].…”

Extraction and Network Sharing of Forest Vegetation Information based on SVM

“…Support vector machines (SVM) theory is a machine learning method based on the statistical learning theory [11,12,13]. By learning algorithm, SVM can automatically find those support vectors which have greater ability to distinguish classifications, construct classifier, and maximize the interval between classes, thus it has better promotion and higher classification accuracy [2,14,15]. In the classification study of remote sensing image, application of SVM classification do not need data dimensionality reduction, and has high performance in algorithm's convergence, training speed and classification accuracy [16,17].…”