08-05-2013, 02:13 PM
Social group formation with mobile cloud services
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Introduction
Social networks [7] have become very popular these days, with millions of users, from several countries and with different backgrounds and interests. Facebook [14] and Twitter [49] are the most popular social networks of them, with more than 800 million and 200 million active users, respectively. This number of people present in the network is a good opportunity to create professional and business connections around the world. People with common interests in the social network can take advantage of the social network benefits like public recognition, creation of relationships, referrals, and manage the social capital. Social groups [36] also result in sharing and collaborative relationships between the members. For example, a group of people working in the same research topic can collaborate with each other sharing resources, ideas, and knowledge, etc. This helps in achieving their common goals faster.
CroudSTag: application description and screenshots
CroudSTag is a mobile application developed for Android devices, with the aim of aiding in the social group formation by means of facial recognition technologies and MapReduce [11]video processing. CroudSTag recognizes the people who appear in media content such as pictures or videos and joins them together into a social group. For example, consider a researcher who attends conferences around the world and has a set of media content (pictures and video files) of the people with whom he/she had interacted at the event. The media files are probably taken from his/her mobile itself and are stored on the cloud. The researcher later wants to create and keep connections with his acquaintances on the social network. He/she also wants to group them according to specific interests and would like to follow the groups directly from his/her mobile phone. The scenario can also be envisioned with any other type of the event or community that wants to keep its members in contact, something like alumni.
CroudSTag’s application flow
Figure 1 shows the screenshots of the CroudSTag usage scenario, which is an extension of the original CroudSTag application presented in our previous work [41]. With the help of CroudSTag application, the user can upload pictures and videos to the cloud and store them in Amazon Simple Storage Services (S3) [1], as shown in the top cycle of the figure. When the user starts the application, three buttons are shown: facebook Login, Take Picture, and Take Video, as shown in screenshot 1. The facebook login goes directly to facebook and authenticates the user getting an authorization token, which is used for accessing the facebook graph and also for accessing the user’s tags during the facial recognition process. This authentication token is also needed to send the invitation to people, to join the social group. The Take Picture and Take Video buttons are used to take a picture/video, respectively, and upload the file to S3.
Generic middleware for mobile cloud services invocation
While the services employed for the CroudSTag application are interesting, the direct invocation of a cloud service (such as face.com) is resource consuming for a mobile phone. Most often, the operating system of the device tends to get stuck, if the computation offloading requires long waiting time for getting a response back. Moreover, while the handset is wait- ing for an answer, it cannot make another application call from the user. Hence, the mobile is not able to execute con- current tasks. Also, such waiting time is not tolerable for the user and mobile application usability perspective. Consequently, it is necessary to rely on a middleware solution for getting the results back asynchronously.
Related work
Extensive research has been conducted in the computer vision domain during the past two decades, focusing in face detection and facial recognition. These studies have achieved huge improvements in the generation of algorithms and techniques for facial detection and recognition.
However, they happen to be resource and time demanding and thus are not suitable for resource constrained devices like mobile phones. For instance, Turk and Pentland [48] developed a framework for the detection and identification of human faces, which claims to work in near-real-time.
Conclusions and future research directions
Cloud computing and mobile computing domains have advanced rapidly and are the promising technologies for the near future. Joining the technologies together, one can envision several applications, which are suitable for the people in the social networks. The applications may also take advantage of huge user bases of the social networks, increasing their feature richness. CroudSTag is one such application built for the Android devices. The application takes a set of media files (actually collected by the phone itself) from the cloud and uses the cloud services of Amazon and SaaS of face.com to recognize and identified people out of those files. The application then enables the user to send, to each recognized person, an invitation to be part of a social group and it uses facebook for the social group creation. The social groups thus formed with people of common interests result in sharing and collaboration relationships between the members. The paper explained the application with detailed architectural and technological choices. The performance analysis of the application shows that the social groups can be formed with significant ease and reasonable performance latencies on the devices.