Opportunistic D2D Research Articles

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

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.

Collaborative Mobile Crowdsensing in Opportunistic D2D Networks

With the remarkable proliferation of smart mobile devices, mobile crowdsensing has emerged as a compelling paradigm to collect and share sensor data from surrounding environment. In many application scenarios, due to unavailable wireless network or expensive data transfer cost, it is desirable to offload crowdsensing data traffic on opportunistic device-to-device (D2D) networks. However, coupling between mobile crowdsensing and D2D networks, it raises new technical challenges caused by intermittent routing and indeterminate settings. Considering the operations of data sensing, relaying, aggregating, and uploading simultaneously, in this article, we study collaborative mobile crowdsensing in opportunistic D2D networks. Toward the concerns of sensing data quality, network performance and incentive budget, Minimum-Delay-Maximum-Coverage (MDMC) problem and Minimum-Overhead-Maximum-Coverage (MOMC) problem are formalized to optimally search a complete set of crowdsensing task execution schemes over user, temporal, and spatial three dimensions. By exploiting mobility traces of users, we propose an unified graph-based problem representation framework and transform MDMC and MOMC problems to a connection routing searching problem on weighted directed graphs. Greedy-based recursive optimization approaches are proposed to address the two problems with a divide-and-conquer mode. Empirical evaluation on both real-world and synthetic datasets validates the effectiveness and efficiency of our proposed approaches.

Cellular Traffic Offloading via Link Prediction in Opportunistic Networks

With the emergence of affordable smart mobile devices (such as smartphones and tablets) running innovative applications have severely overloaded the cellular network. To cope with this issue, there have been many efforts to offload the traffic from the cellular network to other complement networks, for instance, Wi-Fi and device-to-device (D2D) communications. In this paper, we formulate the traffic offloading issue as a link prediction problem in opportunistic D2D network, which is targeted to alleviate the overburdened cellular network traffic and reduce the delay time. Considering the complexity of realistic networks, we employ three indexes of link prediction: common neighbors, Katz, and LRW index. To measure the performance of our proposed algorithm, we analyze it is offloading traffic capacity along with delay minimization among users in different networks. It is demonstrated that our proposed link prediction solution can efficiently offload up to 80% of the cellular traffic.

Social‐aware content dissemination through opportunistic D2D communications

AbstractLast decade has witnessed rapid development in wireless network. The traffic demand is driven by enormous application data that have high‐bandwidth requirement. From the operators perspective, device‐to‐device (D2D) communications are expected to provide an efficient way of offloading cellular traffic. Note that, in the high‐density gathering cases, the redundant traffic in the cellular infrastructure can be drastically offloaded by disseminating the duplicated content through D2D communications. According to this observation, we propose a novel social‐aware content dissemination scheme through D2D communications to effectively offload cellular traffic. We base our proposition on the inherent social aspects of user mobility, exploit three social structural metrics, namely, centrality, contact rate, and intercontact time. The contributions of this paper are two‐fold. First, we integrate the carry‐forward mechanism and multihop forwarding into our D2D content dissemination scheme to fully utilize the potential of D2D networks. Second, based on social pattern, we design a two‐stage social‐aware delay prediction scheme (SADP) to improve the D2D offloading utility gain. The two stages are decision‐making stage and content dissemination stage. Based on real trace data set, we evaluate the proposed method through opportunistic D2D communications, which utilizes the aforementioned metrics to enhance offloading performance and D2D transmission utility gain. By comparing with other two well‐known schemes, SA‐Epidemic and SA‐Prophet, it has been shown that our method is superior to them in terms of offloading ratio and message delay and can substantially enhance the content dissemination performance. Since SADP is designed based on a distributed insight, it ensures that it can be adaptive to mobile network. To that end, we also look forward to the future that SADP is a promising approach applied in the application scenario of 5G network.

Delay-Constrained Utility Maximization for Video Ads Push in Mobile Opportunistic D2D Networks

It is a significant challenge for device-to-device (D2D) networks to deliver mobile videos among mobile users due to the highly nondeterministic and intermittent connectivity. In this paper, we propose to integrate the random mobility of users in mobile opportunistic D2D networks with crowdsourcing to push mobile video Ads. Incentives are key to the success of video Ads push as it heavily depends on how actively mobile users participate in it. To stimulate users to perform mobile video Ads push tasks, we model the interaction between depositories and the Ad provider as a reverse auction. More specifically, we try to maximize the utility of Ad provider by selecting a subset of depositories before a specified deadline. We first propose an online auction (OA) algorithm, which runs efficiently in polynomial time, guarantees individual rationality, profitability. However, it does not guarantee truthfulness and thus limits its practicality. We then introduce two truthful OA algorithms, i.e., TOA and TOA-MM. We carry out trace-driven simulation to verify the three OA algorithms. The simulation results corroborate that the proposed algorithms have superior performance and efficiently stimulate mobile users to make contribution to video Ads push.

D2D Opportunistic Local Content Dissemination Sans Location Sharing

In this paper, we consider the problem of local content dissemination among mobile users via decentralized device-to-device (D2D) communication, where the concerned contents have relevance only within a geographic span centered at their respective location of creation. This has several potential applications, for example, advertising and sharing contents specific to an event. To this end, we propose a scheme for locality-bounded content and information dissemination (LUCID) leveraging opportunistic D2D communications. Unlike other existing works, LUCID innovates by not requiring users to share locations of the centers of localities and, thereby, provides a natural defense against possible location privacy attacks. In this paper, we theoretically characterize the behavior of LUCID for different locality sizes. Moreover, we investigate the extent to which an individual user can attempt to infer the location of a center of a locality, and discuss why that is difficult to achieve. Finally, we also consider an alternative version of LUCID where locations are partially shared. Results of simulation-based performance evaluation indicate that, when compared to an optimal scheme, LUCID can deliver contents to about two-third of the relevant users without requiring any location sharing.

Narrowband Internet of Things Systems With Opportunistic D2D Communication

Narrowband Internet of Things (NB-IoT) is a new cellular technology introduced by the third generation partnership (3GPP) providing low-power and wide-area coverage for IoT. In this paper, we consider the scenario that NB-IoT is deployed in an heterogeneous network and the quality of the direct link from the NB-IoT user equipment (UE) to the serving base station (BS) cannot satisfy the quality of service requirement for transmission of vital sensing data. Thereupon, device-to-device (D2D) communication is adopted as a routing extension to NB-IoT systems, and thus, enables two-hop routes between NB-IoT UE and the serving BS via a set of D2D relays. As the candidate UE relays work in duty cycle to save energy, we derive a model to select a set of UE relays and perform opportunistic D2D communication according to a working schedule. Two optimization problems are formulated aiming at achieving optimal expected delivery ratio (EDR) and expected two-hop delay, respectively. Dynamic programming-based algorithms are proposed to solve the optimization problems and obtain the optimal working schedule of the relays. Simulation results demonstrate that our proposed maxEDR and minEED algorithms improves the system performance compared with other state-of-the-art algorithms.

On the Capacity Gain of Multi-Hop Cellular Networks With Opportunistic Networking and D2D: A Space-Time Graph-Based Evaluation

Previous studies have shown that device-centric wireless technologies, including multi-hop cellular networks (MCNs) and device-to-device communications (D2D), can increase the cellular network capacity. This letter progresses the current state of the art by deriving (using space-time graphs) the upper-bound capacity gains that can be further achieved when integrating efficiency-driven opportunistic networking into device-centric wireless networks. The increase is obtained without modifying the radio interface, and is not constrained to any particular radio interface.

DORE: An Experimental Framework to Enable Outband D2D Relay in Cellular Networks

Device-to-Device (D2D) communications represent a paradigm shift in cellular networks. In particular, analytical results on D2D performance for offloading and relay are very promising, but no experimental evidence validates these results to date. This paper is the first to provide an experimental analysis of outband D2D relay schemes. Moreover, we design D2D opportunistic relay with QoS enforcement (DORE), a complete framework for handling channel opportunities offered by outband D2D relay nodes. DORE consists of resource allocation optimization tools and protocols suitable to integrate QoS-aware opportunistic D2D communications within the architecture of 3GPP Proximity-based Services . We implement DORE using an SDR framework to profile cellular network dynamics in the presence of opportunistic outband D2D communication schemes. Our experiments reveal that outband D2D communications are suitable for relaying in a large variety of delay-sensitive cellular applications, and that DORE enables notable gains even with a few active D2D relay nodes.

Minimum-Cost Crowdsourcing with Coverage Guarantee in Mobile Opportunistic D2D Networks

With the remarkable proliferation of intelligent wireless devices and active mobile users, we have witnessed a rapid increasing trend of mobile crowdsourcing applications. While crowdsourcing does not depend on any specific underlying network, the device-to-device (D2D)-based crowdsourcing is highly desired when the originator of a crowdsourcing task cannot directly reach out to the participants or the conventional approaches for data transportation are costly. The marriage of crowdsourcing and D2D creates new, interesting research problems, mainly due to the unique non-deterministic setting in D2D. In this work, we focus on the problem of how to efficiently distribute a crowdsourcing task and recruit participants based on D2D communications. We formally formulate the minimum-cost crowdsourcing (MCC) problem in D2D networks, which explores a multi-dimensional design space to seek an optimal solution that minimizes the total crowdsourcing cost while satisfying the coverage probability over the field of interest. We introduce an approximation algorithm based on a reduced solution space and formally prove that its cost is bounded by a desired approximation ratio in comparison with the optimal solution. We further propose a lightweight online heuristic that inherits the same design philosophy but implements it in a distributed manner. We prototype the proposed online scheme in Android and carry out experiments in a campus environment, involving 21 Dell Streak tablets carried by students for a period of 15 days. We also extract the algorithm codes from our prototype and perform extensive simulations based on Haggle trace. The results demonstrate the efficiency of the proposed heuristics and reveal empirical insights into the design tradeoffs and practical considerations in D2D-based crowdsourcing.

Offloading Multiple Mobile Data Contents Through Opportunistic Device-to-Device Communications

Opportunistic device-to-device (D2D) communication is the approach proposed to offload mobile data traffic for cellular networks. In opportunistic D2D communication, the network has to appoint relaying users to distribute content(s) to normal subscribers under a given delay-tolerance threshold. In general, the total number of relaying users is fixed. Identifying proper number of relaying users is one of the key challenges in opportunistic D2D communication. The network has to select proper number of relaying users for each content to minimize the amount of mobile data traffic. This paper presents a popularity-based relaying user selection algorithm to determine the number of relaying users for distributing multiple contents with different popularity. An analytical model is then presented to estimate the amount of reduced mobile data traffic under single-hop and multi-hop opportunistic forwarding scenarios. Results obtained by simulations as well as by our proposed analytical model show that the proposed popularity-based algorithm can find the total number of relaying users to the amount of reduced mobile data traffic. For services which have longer delay-tolerance threshold, the proposed popularity-based algorithm requires less relaying users and can achieve similar amount of reduced mobile data traffic as the state-of-the-art random fully-allocation algorithm does. For services which have shorter delay-tolerance threshold, the proposed popularity-based algorithm provide significant gain comparing with the random fully-allocation algorithm.