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"Mobile communications networks"
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Vehicular networking
\"With this essential guide to vehicular networking, you will learn about everything from conceptual approaches and state-of-the-art protocols, to system designs and their evaluation. Covering both in- and inter-vehicle communication, this comprehensive work outlines the foundations of vehicular networking as well as demonstrating its commercial applications, from improved vehicle performance, to entertainment, and traffic information systems. All of this is supported by in-depth case studies and detailed information on proposed protocols and solutions for access technologies and information dissemination, as well as topics on rulemaking, regulations, and standardization. Importantly, for a field which is attracting increasing commercial interest, you will learn about the future trends of this technology, its problems, and solutions to overcome them. Whether you are a student, a communications professional or a researcher, this is an invaluable resource\"-- Provided by publisher.
Book
Modeling the UE-perceived cellular network performance following a controller-based approach
During the last few years, mobile communication networks have experienced a huge evolution. This evolution culminates with the arrival of the fifth generation (5G) of mobile communication networks. As a result, the complexity of network management tasks has been increasing and the need to use automatic management algorithms has been demonstrated. However, many mobile network operators (MNOs) are reluctant to evaluate these algorithms in their networks. To address this issue, in this paper, a modeling approach is proposed. In this sense, the behavior of a commercial network, as it is perceived by user equipments (UEs), has been replicated in a research testbed using a three-step modeling process. The first step consists on performing a measurement campaign in several external networks. The second step is composed of the measurement campaign result analysis and the classification of the results in different types of scenarios. Finally, the third step is related to the application of a modeling algorithm in a research testbed. In order to perform the last step, the use of a method based on a controller is proposed. The modeling process presented in this paper allows to replicate the network behavior from users located in different areas and with different conditions point of view. Moreover, the use of a testbed environment can help to avoid downtime in commercial networks caused by possible algorithm bugs.
Journal Article
The handbook of personal area networking technologies and protocols
\"This handbook offers an unparalleled view of wireless personal area networking technologies and their associated protocols. It lifts the lid on their growing adoption within the consumer electronics, home automation, sports, and health and well-being markets. Bluetooth low energy, ZigBee, EnOcean, and ANT+ are comprehensively covered, along with other WPAN technologies including NFC, Wi-Fi, Bluetooth classic and high speed, and WHDI. It also features 802.11ac, the Internet of Things, Wireless USB, WiGig, and WirelessHD. The handbook shows how white space radio, cellular, and femtocells have inadvertently blurred the boundaries between personal and wide area communications, creating disruptive topologies through technology convergence. It explores how pervasive WAN technologies have spawned a new generation of consumers through the Lawnmower Man Effect and explains how our personal space has become integral to social media streams, including Twitter, Facebook, and Pinterest. An essential read for students, software engineers and developers, product planners, technical marketers, and analysts\"-- Provided by publisher.
Book
SUN-569 The Efficacy of Mobile Application on Diabetes Prevention Compared with Standard of Care in Adults with Prediabetes: Randomized Controlled Trial
Abstract
Disclosure: N. Wonghakaeo: None.
Aims: To investigate the efficacy of a mobile application compared with a control group receiving standard care in prediabetic patients. Methods: Healthcare personnel at King Chulalongkorn Memorial Hospital with prediabetes (N=131) were randomized into either the mobile application group or the standard care group. The primary outcome was the change in fasting plasma glucose (FPG) at 6 months. The secondary outcomes were changes in anthropometric, metabolic parameters, and quality of life. Results: The changes in bodyweight, waist circumference, and HbA1C between groups were significantly different at 6 months between the groups, but there is no difference in FPG (mean difference -2.60 mg/dL [95% CI -5.73, 0.52], p=0.102). At 6 months, 11 participants (17.2%) in the application group reversed to normoglycemia, compared to, 4 participants (6%) in the standard care group. Conclusion: Mobile applications is an innovative and effective way to improve health outcomes by reducing body weight, waist circumference, HbA1c at 6 months, increasing muscle mass at 6 months, and increasing a rate of transition from prediabetes to normoglycemia than standard care.
Presentation: Sunday, July 13, 2025
Journal Article
Human Monkeypox Classification from Skin Lesion Images with Deep Pre-trained Network using Mobile Application
Recently, human monkeypox outbreaks have been reported in many countries. According to the reports and studies, quick determination and isolation of infected people are essential to reduce the spread rate. This study presents an Android mobile application that uses deep learning to assist this situation. The application has been developed with Android Studio using Java programming language and Android SDK 12. Video images gathered through the mobile device’s camera are dispatched to a deep convolutional neural network that runs on the same device. Camera2 API of the Android platform has been used for camera access and operations. The network then classifies images as positive or negative for monkeypox detection. The network’s training has been carried out using skin lesion images of monkeypox-infected people and other skin lesion images. For this purpose, a publicly available dataset and a deep transfer learning approach have been used. All training and testing steps have been applied on Matlab using different pre-trained networks. Then, the network that has the best accuracy has been recreated and trained using TensorFlow. The TensorFlow model has been adapted to mobile devices by converting to the TensorFlow Lite model. The TensorFlow Lite model has been then embedded into the mobile application together with the TensorFlow Lite library for monkeypox detection. The application has been run on three devices successfully. During the run-time, the inference times have been gathered. 197 ms, 91 ms, and 138 ms average inference times have been observed. The presented system allows people with body lesions to quickly make a preliminary diagnosis. Thus, monkeypox-infected people can be encouraged to act rapidly to see an expert for a definitive diagnosis. According to the test results, the system can classify the images with 91.11% accuracy. In addition, the proposed mobile application can be trained for the preliminary diagnosis of other skin diseases.
Journal Article
Validation of the Mobile Application Rating Scale (MARS)
Mobile health apps (MHA) have the potential to improve health care. The commercial MHA market is rapidly growing, but the content and quality of available MHA are unknown. Instruments for the assessment of the quality and content of MHA are highly needed. The Mobile Application Rating Scale (MARS) is one of the most widely used tools to evaluate the quality of MHA. Only few validation studies investigated its metric quality. No study has evaluated the construct validity and concurrent validity.
This study evaluates the construct validity, concurrent validity, reliability, and objectivity, of the MARS.
Data was pooled from 15 international app quality reviews to evaluate the metric properties of the MARS. The MARS measures app quality across four dimensions: engagement, functionality, aesthetics and information quality. Construct validity was evaluated by assessing related competing confirmatory models by confirmatory factor analysis (CFA). Non-centrality (RMSEA), incremental (CFI, TLI) and residual (SRMR) fit indices were used to evaluate the goodness of fit. As a measure of concurrent validity, the correlations to another quality assessment tool (ENLIGHT) were investigated. Reliability was determined using Omega. Objectivity was assessed by intra-class correlation.
In total, MARS ratings from 1,299 MHA covering 15 different health domains were included. Confirmatory factor analysis confirmed a bifactor model with a general factor and a factor for each dimension (RMSEA = 0.074, TLI = 0.922, CFI = 0.940, SRMR = 0.059). Reliability was good to excellent (Omega 0.79 to 0.93). Objectivity was high (ICC = 0.82). MARS correlated with ENLIGHT (ps<.05).
The metric evaluation of the MARS demonstrated its suitability for the quality assessment. As such, the MARS could be used to make the quality of MHA transparent to health care stakeholders and patients. Future studies could extend the present findings by investigating the re-test reliability and predictive validity of the MARS.
Journal Article
Mobile Application Usability
This paper presents a mobile application usability conceptualization and survey instrument following the 10-step procedure recommended by MacKenzie et al. (2011). Specifically, we adapted Apple’s user experience guidelines to develop our conceptualization of mobile application usability that we then developed into 19 first-order constructs that formed 6 second-order constructs. To achieve our objective, we collected four datasets: content validity (n = 318), pretest (n = 440), validation (n = 408), and cross-validation (n = 412). The nomological validity of this instrument was established by examining its impact on two outcomes: continued intention to use and mobile application loyalty. We found that the constructs that represented our mobile application usability conceptualization were good predictors of both outcomes and compared favorably to an existing instrument based on Microsoft’s usability guidelines. In addition to being an exemplar of the recent procedure of MacKenzie et al. to validate an instrument, this work provides a rich conceptualization of an instrument for mobile application usability that can serve as a springboard for future work to understand the impacts of mobile application usability and can be used as a guide to design effective mobile applications.
Journal Article
Security of the Internet of Things: perspectives and challenges
Internet of Things (IoT) is playing a more and more important role after its showing up, it covers from traditional equipment to general household objects such as WSNs and RFID. With the great potential of IoT, there come all kinds of challenges. This paper focuses on the security problems among all other challenges. As IoT is built on the basis of the Internet, security problems of the Internet will also show up in IoT. And as IoT contains three layers: perception layer, transportation layer and application layer, this paper will analyze the security problems of each layer separately and try to find new problems and solutions. This paper also analyzes the cross-layer heterogeneous integration issues and security issues in detail and discusses the security issues of IoT as a whole and tries to find solutions to them. In the end, this paper compares security issues between IoT and traditional network, and discusses opening security issues of IoT.
Journal Article
Weight Pick: an efficient packet selection algorithm for network coding based multicast retransmission in mobile communication networks
This paper proposes an efficient packet selection algorithm, called
Weight Pick
, for improving the efficiency of a network coding based multicast retransmission protocol in mobile communication networks. Unlike existing packet selection algorithms,
Weight Pick
introduces the concept of a dynamic combination number in performing network coding. Based on this concept, a base station dynamically determines the number of packets combined or encoded in a retransmission packet based on the current packet receiving status of users and the combination number for each retransmission packet can be different. In packet selection,
Weight Pick
attempts to find an encoding combination whose weight is not less than (
C
− 1) for every user, where
C
is the combination number of that retransmission packet. Simulation results show that
Weight Pick
can significantly improve the retransmission performance as compared with existing packet selection algorithms in terms of both packet loss ratio and packet transmission delay.
Journal Article