A Gamified News Application for Mobile Devices: An Approach that Turns Digital News Readers into Players of a Social Network

2016 ◽  
pp. 480-493
Author(s):  
Catherine Sotirakou ◽  
Constantinos Mourlas
Keyword(s):  
Social Network ◽  
Mobile Devices ◽  
Digital News

scholarly journals A P2P Query Algorithm for Opportunistic Networks Utilizing betweenness Centrality Forwarding

2013 ◽  
Vol 9 (4) ◽  
pp. 331-345
Author(s):  
Jianwei Niu ◽  
Mingzhu Liu ◽  
Han-Chieh Chao
Keyword(s):  
Social Network ◽  
Success Rate ◽  
Mobile Devices ◽  
Betweenness Centrality ◽  
State Of The Art ◽  
Opportunistic Networks ◽  
The State ◽  
Opportunistic Networking ◽  
Wireless Interfaces ◽  
Query Algorithm

With the proliferation of high-end mobile devices that feature wireless interfaces, many promising applications are enabled in opportunistic networks. In contrary to traditional networks, opportunistic networks utilize the mobility of nodes to relay messages in a store-carry-forward paradigm. Thus, the relay process in opportunistic networks faces several practical challenges in terms of delay and delivery rate. In this paper, we propose a novel P2P Query algorithm, namely Betweenness Centrality Forwarding (PQBCF), for opportunistic networking. PQBCF adopts a forwarding metric called Betweenness Centrality (BC), which is borrowed from social network, to quantify the active degree of nodes in the networks. In PQBCF, nodes with a higher BC are preferable to serve as relays, leading to higher query success rate and lower query delay. A comparison with the state-of-the-art algorithms reveals that PQBCF can provide better performance on both the query success Ratio and query delay, and approaches the performance of Epidemic Routing (ER) with much less resource consumption.

scholarly journals Location prediction in location-based social networks

2017 ◽  
Vol 7 (3) ◽  
pp. 149-156
Author(s):  
Mucahit Baydar ◽  
Songul Albayrak
Keyword(s):  
Social Networks ◽  
Wireless Networks ◽  
Social Network ◽  
Mobile Devices ◽  
New Method ◽  
Time Range ◽  
Location Prediction ◽  
The Social ◽  
Location Based Social Network ◽  
Location Based Social Networks

AbstractDevelopments in mobile devices and wireless networks have led to the increasing popularity of location-based social networks. These networks allow users to explore new places, share their location, videos and photos and make friends. They give information about the mobility of users, which can be used to improve the networks. This paper studies the problem of predicting the next check-in of users of location-based social networks. For an accurate prediction, we first analyse the datasets that are obtained from the social networks, Foursquare and Gowalla. Then we obtain some features like place popularity, place popular time range, place distance to user’s home, user’s past visits, category preferences and friendships ,which are used for prediction and deeper understanding of the user behaviours. We use each feature individually, and then in combination, using the new method. Finally, we compare the acquired results and observe the improvement with the new method.Keywords: Location prediction, location-based social network, check-in data.

Application Security for Mobile Devices

2015 ◽  
pp. 562-588
Author(s):  
Gabriele Costa ◽  
Aliaksandr Lazouski ◽  
Fabio Martinelli ◽  
Paolo Mori
Keyword(s):  
Social Network ◽  
Mobile Devices ◽  
Mobile Phones ◽  
Mobile Applications ◽  
Scientific Literature ◽  
Personal Data ◽  
Application Security ◽  
E Mail

In these last years, mobile devices, such as mobile phones and tablets, have become very popular. Moreover, mobile devices have become very powerful and commonly run fairly complex applications such as 3D games, Internet browsers, e-mail clients, social network clients, and many others. Hence, an adequate security support is required on these devices to avoid malicious application damage or unauthorized accesses to personal data (such as personal contacts or business email). This chapter describes the security support of the current commercial mobile devices along with a set of approaches that have been proposed in the scientific literature to enhance the security of mobile applications.

A spontaneous social network based on mobile devices

2014 ◽  
Vol 4 (1) ◽  
Author(s):  
Cristiano André da Costa ◽  
Dante Zaupa ◽  
Jorge Luis Victoria Barbosa ◽  
Rodrigo da Rosa Righi ◽  
João de Camargo ◽  
...  
Keyword(s):  
Social Network ◽  
Mobile Devices

scholarly journals Understanding Social Characteristic from Spatial Proximity in Mobile Social Network

2015 ◽  
Vol 10 (4) ◽  
pp. 539 ◽  
Author(s):  
Duan Hu ◽  
Benxiong Huang ◽  
Lai Tu ◽  
Shu Chen
Keyword(s):  
Social Networks ◽  
Social Network ◽  
Mobile Devices ◽  
Hybrid Approach ◽  
Spatial Proximity ◽  
Mobile Social Networks ◽  
Mobile Social Network ◽  
Data Repositories ◽  
Sensing Applications ◽  
Proximity Matrix

Over the past decades, cities as gathering places of millions of people rapidly evolved in all aspects of population, society, and environments. As one recent trend, location-based social networking applications on mobile devices are becoming increasingly popular. Such mobile devices also become data repositories of massive human activities. Compared with sensing applications in traditional sensor network, Social sensing application in mobile social network, as in which all individuals are regarded as numerous sensors, would result in the fusion of mobile, social and sensor data. In particular, it has been observed that the fusion of these data can be a very powerful tool for series mining purposes. A clear knowledge about the interaction between individual mobility and social networks is essential for improving the existing individual activity model in this paper. We first propose a new measurement called geographic community for clustering spatial proximity in mobile social networks. A novel approach for detecting these geographic communities in mobile social networks has been proposed. Through developing a spatial proximity matrix, an improved symmetric nonnegative matrix factorization method (SNMF) is used to detect geographic communities in mobile social networks. By a real dataset containing thousands of mobile phone users in a provincial capital of China, the correlation between geographic community and common social properties of users have been tested. While exploring shared individual movement patterns, we propose a hybrid approach that utilizes spatial proximity and social proximity of individuals for mining network structure in mobile social networks. Several experimental results have been shown to verify the feasibility of this proposed hybrid approach based on the MIT dataset.

ChronoFindMe

2013 ◽  
pp. 171-187
Author(s):  
Ana Filipa Nogueira ◽  
Catarina Silva
Keyword(s):  
Social Networks ◽  
Social Network ◽  
Mobile Devices ◽  
Mobile Application ◽  
Location Based Services ◽  
User Experiences ◽  
Mobile Interaction ◽  
The Past ◽  
The Social ◽  
Geographical Position

Social networks such as Facebook have grown exponentially over the past decade. This growth led to the exploration of new services that could enhance users’ experiences and constitute a driver for even more followers. With the proliferation of smartphones and the increasing search for applications that enable the sharing of experiences, social networks became eager to integrate into mobile devices, taking advantage of their impressive omnipresence and panoply of sensors. Amongst the sensors, the most notable are the localization sensors (GPS) that allow for the development of location-based services that use the geographical position to enrich user experiences in a variety of contexts, including location-based searching and location-based mobile interaction. ChronoFindMe enhances location-based services by adding a temporal component not present in current approaches. The authors allow information about past and future locations to be considered by defining an architecture that provides location-based services to users of social networks. This information includes data about time and space, which can be accessed through the social network or a specific mobile application, using privacy policies to assure users’ privacy.

Sign in / Sign up

Export Citation Format

Share Document