Device-to-device (D2D) offloading has been shown to be a highly effective technique to enhance the performance of wireless networks. Yet, for any two mobile users to share data efficiently and reliably via D2D links, they should be in close proximity for long enough period of time, share similar content interests, and have some level of incentive and trust to cooperate. In this work, we focus on the practical implementation aspects of D2D data sharing taking into account realistic operational conditions. To this end, we design and conduct an experimental study to collect location and neighbor discovery data from 38 mobile users in a university campus over several weeks using our own customized crowdsourcing Android mobile application. The collected data is then processed and utilized to empirically model mobility-related parameters that include contact frequency, contact duration, and inter-contact duration. The participating users did also fill a user interest survey in order to correlate mobility and connectivity patterns with content interests and social network relations. The obtained insights are then used to develop a practical implementation framework for designing effective D2D data sharing strategies. To test the proposed ideas under realistic operational constraints, we design and implement a social-aware D2D data sharing Android mobile application and demonstrate its functionality and effectiveness using an example case study scenario.
The computing and communications capabilities and the user-friendly interfaces of smartphones are facilitating the creation and sharing of content between devices without relying on existing wireless infrastructure, such as WiFi access points or cellular base stations [
Supporting D2D communications in wireless networks can be achieved with different levels of network operator assistance ranging from full to partial to none; for example, see [
The main objective of this work is to address practical aspects of D2D data sharing following an experimental approach using our own developed crowdsourcing Android mobile application in order to capture realistic system and performance constraints. We model empirically key metrics for effective D2D data sharing including the number of contacts, contact duration, and inter-contact duration among a set of mobile users in a bounded geographical environment. We complement these empirical models with data collected via user surveys on content similarity and social networking relations. We then overlay the contact models, content similarity, and social relationship links to generate novel insights that can help in designing efficient and reliable D2D data sharing strategies. Finally, we demonstrate the practical feasibility of the various presented ideas by performing experiments using our own developed social-aware D2D data sharing solution based on an Android mobile application. The key novelty of our work is twofold: (i) holistic approach by jointly capturing within one framework the users’ mobility and inter-contact statistics, content interests, and social relations; (ii) experimental methodology with empirical models in order to obtain practical results that can bridge a gap between theoretical studies and real wireless devices, systems, and technologies.
In the literature, there have been several experimental studies focused on mobility and contact modeling for D2D data sharing. In [
Another line of related work in the literature has focused on content similarity among mobile users. For example, in [
Recently, there has been increased interest in utilizing social networking relations among users to leverage content downloading and sharing for improved quality of experience. In [
The rest of the paper is organized as follows. We describe in Section
Our experimental study design consists of two independent and complementary phases: a mobility-related data collection phase and a content similarity data collection phase.
Mobility-related data is collected using our own crowdsourcing mobile application called
CARMA graphical user interface snapshots: (a) home screen; (b) neighbor discovery screen; (c) settings screen.
This database contains two tables: Data and Neighbors. The information stored in the Data table is as follows: time stamp (msec), device ID, number of bytes sent and received, battery percentage, latitude, longitude, accuracy, and location provider. As for the Neighbors table, the data stored includes the following: time stamp (msec), device ID, discovered device name and MAC address, latitude, longitude, and accuracy.
The aim of this experimental study is to collect data in a confined and controlled environment, with a pool of participants having different pairwise contact frequencies; the scale of the study is similar to [
Due to privacy limitations, it was not possible to activate the tracking of users’ stored content and download activity on their devices using the CARMA mobile application. To deal with this constraint, we designed and deployed an online survey to collect relevant content similarity data from mobile users including a subset who participated in the mobility-related data collection experiment. The survey has 28 questions that focus on users’ interests and content stored on their smartphones, downloaded or shared, as well as on social network activity.
The survey questions were divided into five groups, each centered around a certain aspect that relates to D2D data sharing. The first group of questions focused on the type of files stored on the user devices, as well as on the user interests in the five categories of files mentioned. The second group inquired about the users’ sources of media files. The third group concentrated on the number of files stored and streamed on the users’ devices. The fourth group served to assess the users’ willingness to download and share their files via direct D2D data sharing. The final group captured the users’ social activity regarding four social networks: Facebook, Instagram, Twitter, and LinkedIn. Using the data collected, content similarity level between any two users can be calculated. To correlate the survey’s results with the mobility and contact data analysis, participants were asked to include their names along with their responses. We received 31 responses in total for the survey, including 12 who included their names and participated in the mobility-related data collection experiments.
Table
CARMA data collection settings during spring and summer stages.
Settings | Spring | Summer |
---|---|---|
Area | AUB campus | AUB campus |
Trace duration (days) | 77 | 25 |
Location data collection frequency | 10 minutes | 2 minutes |
Neighbor discovery frequency | 10 minutes | 1 minute |
Data points upload frequency | 1 hour | 1 hour |
Number of participants recruited | 37 | 13 |
Number of points in data table | 21,289 | 34,920 |
Number of points in neighbors table | 354,528 | 326,539 |
Number of participants correlated with survey | 8 | 4 |
As in [
In this section, we present a summary of the obtained results from the two stages (spring semester experiment and summer semester experiment) of data collection using CARMA; we use this data to derive several empirical models for mobility-related metrics that would be useful for future studies and enhancements related to D2D communications. Moreover, we analyze the user survey results and social network relations to correlate content similarity and social links with mobility and contact data in the context of D2D data sharing between mobile users in a given geographical area.
The number of contacts between two devices is a key measure to determine whether D2D data sharing is likely between them. The CDF of the normalized number of contacts per day and that of the normalized number of contacts per time interval are derived empirically using the collected data. The empirical aggregated CDF for the normalized number of contacts per day, as well as per time interval, are shown in Figures
Gamma distribution parameters for the number of contacts CDF.
Spring | Summer | |||
---|---|---|---|---|
per day | per time interval | per day | per time interval | |
Shape ( | 0.45 | 0.52 | 0.32 | 0.41 |
Scale ( | 103.12 | 21.57 | 2889.52 | 144.68 |
% fit | 90% | 90% | 76% | 88% |
Selected empirical aggregated CDF for the normalized number of contacts per day for (a) Monday in spring (b) Tuesday in summer.
Selected empirical aggregated CDF for the normalized number of contacts per time interval for (a) 10:00 to 14:00 in spring and (b) 6:00 to 10:00 in summer.
Referring to Figure
Additionally, we investigate how the number of users in a study affects the statistics of the total number of contacts. Thus, we arbitrarily select different sets of users of size ranging from 5 to 25. We start with a set of 5 users and then incrementally increase by 5 additional users up to 25. The total and average number of contacts with respect to day and time interval are plotted in Figures
(a) Total and (b) average number of contacts in function of different sets of users with respect to day.
(a) Total and (b) average number of contacts in function of different sets of users with respect to time interval.
We also calculate the pairwise number of contacts, defined as the number of contacts between a specific user pair, for both semesters (see Figure
Pairwise number of contacts in log-scale for (a) spring and (b) summer.
The pairwise contact duration CDF aggregated over all users is studied with respect to day and time (see Figures
Lognormal distribution parameters for pair-wise contact duration CDF.
Spring | Summer | |||
---|---|---|---|---|
per day | per time interval | per day | per time interval | |
| 3.91 | 1.88 | 1.21 | 1.16 |
| 0.77 | 1.37 | 0.93 | 1.17 |
% fit | 95% | 95% | 80% | 90% |
Pairwise aggregate CDF of contact duration with respect to day for (a) spring and (b) summer.
Pairwise aggregate CDF of contact duration with respect to time interval for (a) spring and (b) summer.
For example, results show that 80% of the pairwise contact duration values recorded are less than 125 minutes per the selected day during the spring and less than 20 minutes during the summer. On the other hand, 80% of the pairwise contact duration values recorded are less than eight minutes per the selected four-hour interval during the spring and less than 12 minutes during the summer. The contact duration is a critical factor in D2D data sharing applications as it determines the time needed to download a given content (or parts of it) as a function of the communications bit rate quality between the cooperating devices.
We have also analyzed the inter-contact duration statistics per day and time interval. The inter-contact duration is defined as the time period between two contact events for given a pair of users; that is, it captures the time during which the users are not in proximity with respect to each other. For example, results show that, during the spring, 479 inter-contacts occurred during the whole trace, with a minimum duration of 10 minutes, a maximum duration of approximately 58 days, and an average duration of approximately 26 hours. However, during the summer, 1111 inter-contacts occurred during the whole trace, with a minimum duration of 1 minute, a maximum duration of approximately 5 days, and an average duration of around 46 minutes. Note that the minimum durations are determined by the neighbor discovery frequency parameter which was set to 10 minutes and 1 minute during the spring and summer data collection stages, respectively (see Table
The content similarity analysis is solely based on the survey responses. Based on the 31 responses received, statistics show that the majority of the responders store about four to six media files per day on their hand-held devices. The files they store are distributed to include all seven categories considered (personal documents, public documents, personal images, public images, personal videos, public videos, and music) with high percentages. Even though personal files occupy the majority of files stored by the participants, public media files also occupy a high percentage of user storage with the most popular file type being images, followed by music. Table
User interest survey results.
Category | Popular Genres | Percentage |
---|---|---|
Documents | Lectures and course material | 58.06% |
Articles of interest | 51.61% | |
Technical documents | 45.16% | |
| ||
Images | Photos from the internet (e.g., 9 gag, reddit) | 67.74% |
| ||
Videos | Short humor clips/stand-up comedy | 58.06% |
Music Videos | 25.81% | |
| ||
Music | Blues and Jazz/Classical | 48.39% |
Rock/R&B/Rap/Hip-Hop | 45.16% | |
Arabic music | 41.94% | |
| ||
Movies | Comedy | 80.65% |
Action | 54.84% | |
Science Fiction/Fantasy/Paranormal | 48.39% |
Furthermore, when asked about D2D data sharing, responders indicated that they are willing to share and/or download all file categories over direct D2D links, that is, documents, images, videos, and music, with music (90.32%) and videos (87.1%) being the most popular choices. As for incentives to share files with neighboring devices using D2D links, 61.3% were favorable without any incentives and 96.8% were favorable when incentives are offered. The most popular incentives were extra 3G/4G cellular data quota (90.3%), followed by free call minutes for each file exchanged (58.1%) and credits for online shopping (54.9%).
For the content similarity analysis, two parts of the survey are relevant: the one related to user interest and the one related to the device content. A weight is calculated for each part by taking the number of common answers between any two participants divided by the corresponding total number of questions as follows:
The similarity matrix
The social relationship strength is analyzed using the survey participants’ Facebook friends lists. In order to correlate mobility and content similarity results with participants’ social network relationships, the number of mutual friends is considered to assess the strength level between any two participants. The number of mutual Facebook friends between participants is presented in the matrix
In this section, the social relationship strength, the content similarity, and the number of pairwise contacts are correlated to identify favorable quantifiable conditions for successful D2D file sharing. The correlation between mobility, content, and social networks is done only for the spring semester, with results being summarized using graphs in Figure
Multilayered graph representing the pairwise social network relationship strength (a), content similarity (b), and number of contacts (c) during the spring semester.
The aggregation of the three layers is visualized in Figure
Aggregation of social network relationship strength, content similarity, and number of contacts during the spring semester based on the level of correlation.
In this section, we use the insights derived from the mobility and content similarity experimental measurements and models from Section
The following is a summary of key factors to design practical D2D data sharing strategies: Social network relationship between users is a prerequisite enabler for D2D data sharing, as its strength is an indicator of the users’ level of trustworthiness with respect to each other; for example, see [ For a user to download a data file over a direct D2D connection, the content of interest should be available in one of its neighboring devices. This requires running service and peer discovery procedures to identify devices that are willing to cooperate and within radio range from the requesting device; this will be followed by content discovery phase to check if any of the trusted devices has the content locally cached. The user survey results presented in Section In addition to trust, incentives, and content availability, D2D data sharing success depends on the number and duration of contacts between a pair of users over time and space. Let
Conditions for designing efficient and reliable D2D data sharing mobile solutions.
Case | Result |
---|---|
Low | Users are unreliable pair for D2D cooperation |
High | D2D cooperation likely to be successful, with large content size and low delay |
Low | D2D cooperation likely to be successful, with medium content size and low delay |
High | D2D cooperation can be successful, but with limited content size and high delay |
High | D2D cooperation can be successful, but with limited content size and low delay |
The experimental measurements and derived empirical models in Section
We have developed and implemented a social-aware D2D data sharing mobile solution for Android smartphones based on the architecture in Figure
D2D data sharing mobile solution architecture.
This module is responsible for authenticating the device by connecting the mobile application to the user’s social networking profile which includes list of friends or contacts; we used Facebook in our implementation, without loss of generality. This process starts by the user providing her/his Facebook credentials which are then authenticated with a Facebook server. If valid, the Facebook server will return a unique identifier for the user’s profile along with the user’s friends list. The user’s friends list is then saved in a local database while the user’s ID and D2D wireless link MAC address are saved in a remote global database; the wireless link can be either WiFi-Direct or Bluetooth as both provide the needed discovery and connectivity features to facilitate stable direct D2D connections. In our implementation, we used Bluetooth.
This module is responsible for a background task whose job is to synchronize a global database of files and devices whenever feasible; for instance, whenever the device connects to the Internet. The database contains only the meta-data about the files that are being shared by the devices. More precisely, for each file that is shared by some mobile device, we maintain the following information: the file’s unique signature (md5 hash), file size, user-friendly file name, and a list of device identifiers that have the file available for sharing; we assume the device identifier is the MAC address of its WiFi-Direct or Bluetooth interface. In addition, the database is used to map between Facebook user identifiers and device MAC addresses. The database contains a tuple (MAC_ADD, FB_UID) for each device with a WiFi-Direct/Bluetooth interface having MAC_ADD as a MAC address and authenticated with a Facebook profile having FB_UID as identifier.
This module is intermittently launched in the background, at predetermined intervals, to discover nearby devices. The device discovery mechanism is implemented as a standard Bluetooth discovery procedure. A list containing the MAC addresses of discovered devices is sent to the File Server Service module.
This module runs periodically in the background and can operate in two modes: either as a file server or as a fetcher. By default, this module is in the file server mode waiting for incoming requests from other devices. This is implemented using a standard TCP file transfer procedure; the device listens on a specific TCP port for incoming file transfer requests. A request is composed of two parameters: the unique signature of the requested file along with a byte offset; the byte offset is needed to allow a device to download different parts of the content from multiple devices. Whenever such request is received, the device file server will begin sending the requested file after skipping to the specified offset.
In the file fetcher mode, this module keeps a lookout for files that are requested by the user. Whenever a new list of nearby devices is received from the device discovery module and the device is not actively transmitting a file, the file fetcher module checks whether it can obtain one of the files requested by the user by the following: Filtering the list of discovered devices and keeping only those that meet a set of conditions, for example, as discussed in Section Fetching the list of requested files and cross-matching it to the list of filtered discovered devices to check whether any of the selected devices has a file of interest.
If such a device is found, a Bluetooth connection request is sent to the device. If the connection is successfully established, the file fetcher service connects to the file server running on the other device and the file transfer process begins (see Figure
Device-to-device interaction flowchart including both discovery and file fetching services.
Figures
(a) List of files; (b) list of Facebook friends.
(a) List of discovered devices; (b) file transfer in progress.
In order to validate the functionality of the developed mobile solution, we present in this section an example scenario using six Android smartphones to download a 5000 KB file; all devices are running the mobile application based on the architecture explained in Section
Example social-aware D2D data sharing case study using Android smartphones.
Figure
In this work, we addressed several aspects related to the practical design and implementation of direct D2D data sharing mobile solutions without relying on intelligence or coordination from any existing radio access network. We performed an experimental study using our own developed crowdsourcing Android application CARMA in order to collect data and derive empirical models for several user mobility and contact parameters. To this end, we managed two data collection stages, the first including 37 mobile user participants over a period of 11 weeks and the second including 13 mobile user participants over a period of 4 weeks. Our focus was on use cases with bounded geographical location (university campus) and homogenous community (university students). We also designed and managed a user survey after obtaining the needed IRB (Institutional Research Board) approval in order to collect data related to content stored on users’ mobile devices and their willingness to participate in D2D data sharing applications. Then, we collected data on the social network relationships among the users and presented a set of conditions necessary for successful D2D file sharing based on joint consideration and correlation of mobility parameters (e.g., number of contacts and contact duration per time period), content similarity level, and social network relationship strength. Finally, we presented an architecture, implementation, and validation of a mobile solution for social-aware D2D data sharing using Android mobile application with several back-end module and a friendly user interface.
Even though the conclusions derived in the course of our work are specific to a university environment, they provide valuable insights that can guide the development of D2D data sharing solutions. The research outcomes illustrate clearly the dynamic nature of inter-user interactions whether in terms of contact and mobility, social connectivity, or mobile content. They also shed light on the temporal variability of all these factors (e.g., by comparing summer versus spring or comparing different days within the week) even by a homogenous group of users in a bounded geographical area, which somehow mimics a best case scenario when compared to dynamic variations over the scale of a city or country. Regarding the number of participants in our crowdsourcing experiments, it is similar to other interesting studies reported in the literature, for example, [
The statements made herein are solely the responsibility of the authors.
The authors declare that there are no conflicts of interest regarding the publication of this paper.
This work was made possible by Qatar National Research Fund (a member of the Qatar Foundation), NPRP Grant 7-1529-2-555.