One key component for efficient opportunistic device-to-device (D2D) deployment is cache management. It determines which content to store opportunistic D2D communications. Existing solutions focus on the nature of content or mobility attributes, but most of them neglect their joint influence. Moreover, most solutions rely on a preloading phase, filling caches with content that the respective users may not consume, but that may be of interest to other nodes, and increasing traffic overhead in the core network. Further, a popular file may be a lousy candidate for opportunistic D2D because contact opportunities may not provide enough transfer capacity. To solve this issue, we propose a model that computes priority values based on both content and mobility attributes. Our approach considers only files that users have consumed, therefore eliminating a preloading phase. Using real-world and synthetic mobility traces, we compare our solution with Least Recently Stored replacement, as well as a state-of-the-art approach that also considers content and mobility attributes. Results show an increase in the global cache hit rate of almost 80% in scenarios that offer many files, and of around 420% in scenarios with a few users. The priority model generates 90% lower overhead in terms of the control bytes. We also apply our solution in a chunk-based adaptive video streaming application. We observe that our solution leads to higher video delivery ratios when compared to the baselines.

Joint Content-Mobility Priority Modeling for Cached Content Selection in D2D Networks

Abstract In Opportunistic Networks (OppNets), mobility of and contact between nodes are explored to create communication opportunities, exchange messages, and information. In the era of data sharing, the data traffic from messages may be higher with nodes exchanging text messages, media or streaming. The use of social aspects to find better relay nodes has become popular in forwarding strategies to increase network performance. However, due to resource constraints such as energy level and buffer space, social aspects applied to forwarding strategies may lead to unexpected behaviors or even harvest more energy from the chosen relay nodes. This work designed a set of experiments to investigate the relationship between energy consumption and social aspects. It is evaluated three trace-based mobility models with increasing contact densities. In addition, it is implemented and collected some social aspects from the investigated scenarios: centrality, betweenness, and local clustering coefficient (LCC). The results presented that the mean contact time of node has more correlation to energy consumption than social aspects (Pearson correlation= 0.99792) for the mobility traces evaluated. Further, when the mean contact time is extensive, the energy consumption increases from 210% to the more sparse to the denser scenario. We also verified that when the conditions for message exchanges are better, i.e., when contact time is enough to exchange all messages, the correlation between centrality and energy consumption is higher. Finally, with the extensive contact time, the energy of popular nodes was entirely harvested in our densest scenario. 

Investigating Energy Consumption in Social Aware Opportunistic Networks with High Data Traffic

Device-to-Device (D2D) communication has gained attention due to its potential to reduce data traffic in mobile operator networks. Several models and algorithms for efficient D2D communication have been proposed recently. However, despite the availability of D2D communication technologies in most current devices, there are few real-world performance evaluations using such communication model. Aiming to fill this gap, this paper presents an experimental evaluation of D2D communication using off-the-shelf devices. To achieve this goal, we propose ShareFile, a tool to measure the performance of content sharing through Wi-Fi Direct in Android devices. We deployed a testbed to compare the performance of D2D communication against centralized or non direct solutions. The results demonstrate that even small distances degrade D2D communication performance. The time to find nearby devices ranges from 400ms to 1.5s, while time to establish a connection can reach 6s and the flow rate reaches up to 6.5MBps, which is close to the 7.9MBps achieved by cloud solutions.

ShareFile: Sharing Content Through Device-to-Device Communication

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ShareFile: Sharing Content Through Device-to-Device Communication