Interleaving and decoding scheme for a product code for a mobile data communication

1997 ◽  
Vol 45 (2) ◽  
pp. 144-147 ◽  
Author(s):  
Chaehag Yi ◽  
Jae Hong Lee
Keyword(s):  
Data Communication ◽  
Mobile Data ◽  
Product Code

scholarly journals Delay-Tolerance-Based Mobile Data Offloading Using Deep Reinforcement Learning

Sensors ◽  
2019 ◽  
Vol 19 (7) ◽  
pp. 1674 ◽  
Author(s):  
Daisuke Mochizuki ◽  
Yu Abiko ◽  
Takato Saito ◽  
Daizo Ikeda ◽  
Hiroshi Mineno
Keyword(s):  
Reinforcement Learning ◽  
Mobile Networks ◽  
Data Communication ◽  
Mobile Network ◽  
Utilization Efficiency ◽  
Bandwidth Utilization ◽  
Delay Tolerance ◽  
Data Offloading ◽  
Mobile Data ◽  
Mobile Data Offloading

The demand for mobile data communication has been increasing owing to the diversification of its purposes and the increase in the number of mobile devices accessing mobile networks. Users are experiencing a degradation in communication quality due to mobile network congestion. Therefore, improving the bandwidth utilization efficiency of cellular infrastructure is crucial. We previously proposed a mobile data offloading protocol (MDOP) for improving the bandwidth utilization efficiency. Although this method balances a load of evolved node B by taking into consideration the content delay tolerance, accurately balancing the load is challenging. In this paper, we apply deep reinforcement learning to MDOP to solve the temporal locality of a traffic. Moreover, we examine and evaluate the concrete processing while considering a delay tolerance. A comparison of the proposed method and bandwidth utilization efficiency of MDOP showed that the proposed method reduced the network traffic in excess of the control target value by 35% as compared with the MDOP. Furthermore, the proposed method improved the data transmission ratio by the delay tolerance range. Consequently, the proposed method improved the bandwidth utilization efficiency by learning how to provide the bandwidth to the user equipment when MDOP cannot be used to appropriately balance a load.

Securing Mobile Data Computing in Healthcare

2011 ◽  
pp. 1930-1939
Author(s):  
Willy Susilo
Keyword(s):  
Mobile Devices ◽  
Point Of Care ◽  
Healthcare Providers ◽  
Data Communication ◽  
Handheld Devices ◽  
Mobile Data ◽  
Case Examples ◽  
Healthcare Environment ◽  
Security Models ◽  
Ward Rounds

Access to mobile data and messages is essential in healthcare environment as patients and healthcare providers are mobile. This is inline with the need of ubiquitous computing in everyday life. Mobile and wireless devices can assist in ensuring patient’s safety by providing easy availability of the data at the point of care. Portability and accessibility of these devices enhances use of them in healthcare environment. However, data integrity and confidentiality of information in them need to be ensured to provide safe, effective and efficient healthcare. Mobile healthcare involves conducting healthcare related activities through using mobile devices such as a smart phone, Personal digital assistant (PDA), wireless enabled computer, iPod and so on. Mobile computing is suitable for healthcare as healthcare providers are mobile. These would be suitable for conducting patient’s healthcare activities in emergencies, ward rounds, homecare, chronic disease management, conducting clinical trials, and so on. There are various projects using mobile devices to enhance patient’s care. With the advancement of medical informatics, telemedicine and information technology, mobile data devices play an enormous role in healthcare system. In this chapter, we outline the need of mobile devices in healthcare, usage of these devices, underlying technology and applications, importance of security of these devices, securing mobile data communication in healthcare through different security models and case examples of applications that we have developed, in particular (1) iPathology tool on iPod, (2) securing healthcare information using Pocket PC 2003, and (3) securing information on handheld devices. There were several incidents in the past due to the insecurity of mobile devices that can leak information to anyone who does not have access to the information. In this chapter, we will illustrate several techniques that we have developed to protect these malicious activities and how these are applicable for securing mobile data computing in healthcare.

scholarly journals Mobile usage of digital geographical data in the Apple iPhone device

2010 ◽  
Vol 58 (4) ◽  
pp. 89-96
Author(s):  
Jiří Kamínek ◽  
Martin Klimánek
Keyword(s):  
User Experience ◽  
Data Communication ◽  
The Internet ◽  
Mobile Data ◽  
Geographical Data ◽  
Web Mapping ◽  
Development Tools ◽  
Current Location ◽  
The Web

This article deals with the possible deployment of the Apple iPhone device as mobile geoinformation technology. An examination of the possibilities of the device, the development tools and the Web Mapping Service (WMS) was followed by developing a prototype of an application called WhateverMap. The application uses maps from the WMS, which are available in the field through mobile data communication. The application interface was designed around the User Experience model so that its control is easy to grasp and intuitive. It also features support to the determination of the current location. To make working with the application more comfortable it integrates a database of WMS ser­vers. The user is thus given an overview of all the maps available and can easily add them as desired. The application can be downloaded free of charge from Apple’s AppStore on the internet.

scholarly journals Energy efficient target set selection and buffer management for D2D mobile data offloading

2021 ◽  
pp. 1-10
Author(s):  
Prince Sharma
Keyword(s):  
Mobile Devices ◽  
Clustering Algorithm ◽  
Data Communication ◽  
D2d Communication ◽  
Significant Feature ◽  
Communication Link ◽  
Community Based ◽  
Data Offloading ◽  
Mobile Data ◽  
Target Set

Data offloading offers a significant solution to the problem of explosive rise in mobile data traffic. A naive approach would be to utilize the infrastructure (cellular tower, WiFi, femtocell) or other mobile devices to offload data. However, increasing the number of a cellular towers, WiFi, or femtocells is costlier deal for data delivery. Recently, device-to-device (D2D) paradigm of data communication has emerged out as one of the most promising solutions to deal with cost effective cellular traffic offloading. D2D communication provides a direct communication link between closely located mobile users. Another significant feature of D2D is its content centric nature, which makes it useful in data offloading. In this paper, we have addressed the issue of data offloading in mobile devices and proposed a hybrid model of D2D communication with ad-hoc nature. The paper also considers the issues like memory constraints of the devices, pruning of replicated messages and energy efficiency to increase the lifetime of the battery. Considering all the constraints and trade off, we have modeled our problem into optimal target selection problem and distributed community detection problem, both of which are NP-hard. We propose a clustering algorithm to optimize the cooperative mobile nodes. The proposed algorithm uses the betweenness centrality and k-means for optimizing target set section. Our proposed algorithm requires less time in terms of computational complexity with limited space. We compare it with the community-based approach in terms of load transferred for varying target set sizes for validation. The simulation results of the suggested algorithm may reduce the energy requirements up to 16.7% and is able to accommodate 80% more traffic as compared to the community-based algorithm.

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