Mobile Data Offloading through Opportunistic Communications and Social Participation

University of Maryland, College Park
IEEE Transactions on Mobile Computing (Impact Factor: 2.91). 06/2012; 11(5):821 - 834. DOI: 10.1109/TMC.2011.101
Source: IEEE Xplore

ABSTRACT 3G networks are currently overloaded, due to the increasing popularity of various applications for smartphones. Offloading mobile data traffic through opportunistic communications is a promising solution to partially solve this problem, because there is almost no monetary cost for it. We propose to exploit opportunistic communications to facilitate information dissemination in the emerging Mobile Social Networks (MoSoNets) and thus reduce the amount of mobile data traffic. As a case study, we investigate the target-set selection problem for information delivery. In particular, we study how to select the target set with only k users, such that we can minimize the mobile data traffic over cellular networks. We propose three algorithms, called Greedy, Heuristic, and Random, for this problem and evaluate their performance through an extensive trace-driven simulation study. Our simulation results verify the efficiency of these algorithms for both synthetic and real-world mobility traces. For example, the Heuristic algorithm can offload mobile data traffic by up to 73.66 percent for a real-world mobility trace. Moreover, to investigate the feasibility of opportunistic communications for mobile phones, we implement a proof-of-concept prototype, called Opp-off, on Nokia N900 smartphones, which utilizes their Bluetooth interface for device/service discovery and content transfer.

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Available from: Madhav Marathe, Aug 15, 2015
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    • "The majority of advantage of such a traffic offloading approach is that there is very little or no monetary cost associated with opportunistic communications. In [38], Han et al. exploited opportunistic communications to enable traffic offloading in the mobile social networks. As a special case, the authors studied the target-set selection problem for data delivery. "
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    ABSTRACT: This paper first provides a brief survey on existing traffic offloading techniques in wireless networks. Particularly as a case study, we put forwards an on-line reinforcement learning framework for the problem of traffic offloading in a stochastic heterogeneous cellular network (HCN), where the time-varying traffic in the network can be offloaded to nearby small cells. Our aim is to minimize the total discounted energy consumption of the HCN while maintain the Quality-of-Service (QoS) experienced by mobile users. For each cell (i.e., a macro cell or a small cell), the energy consumption is determined by its system load which is coupled with system loads in other cells due to the sharing over a common frequency band. We model the energy-aware traffic offloading problem in such HCNs as a discrete-time Markov decision process (DTMDP). Based on the traffic observations and the traffic offloading operations, the network controller gradually optimizes the traffic offloading strategy with no prior knowledge of the DTMDP statistics. Such a model-free learning framework is important especially when the state space is huge. In order to solve the curse of dimensionality, we design a centralized Q-learning with compact state representation algorithm, which is named as QC-learning. Moreover, a decentralized version of the QC-learning is developed based on the fact the macro base stations (BSs) can independently manage the operations of local small-cell BSs through making use of the global network state information obtained from the network controller. Simulations are conducted to show the effectiveness of the derived centralized and decentralized QC-learning algorithms in balancing the tradeoff between energy saving and QoS satisfaction.
    IEEE Journal on Selected Areas in Communications 04/2015; 33(4):627 - 640. DOI:10.1109/JSAC.2015.2393496 · 4.14 Impact Factor
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    • "The work in [20] investigates how the joint association of nodes with interest-and locality-induced social groups can be exploited to enhance content dissemination . But [20] and [25] do not account for the variations of the content distribution in OnSN over time. "
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    ABSTRACT: Device-to-device (D2D) communication is seen as a major technology to overcome the imminent wireless capacity crunch and to enable new application services. In this paper, a novel social-aware approach for optimizing D2D communication by exploiting two layers, namely the social network layer and the physical wireless network layer, is proposed. In particular, the physical layer D2D network is captured via the users' encounter histories. Subsequently, an approach, based on the so-called Indian Buffet Process, is proposed to model the distribution of contents in the users' online social networks. Given the social relations collected by the base station, a new algorithm for optimizing the traffic offloading process in D2D communications is developed. In addition, the Chernoff bound and approximated cumulative distribution function (cdf) of the offloaded traffic are derived and the validity of the bound and cdf is proven. Simulation results based on real traces demonstrate the effectiveness of our model and show that the proposed approach can offload the network's traffic successfully.
    IEEE Transactions on Wireless Communications 01/2015; 14(1):177-190. DOI:10.1109/TWC.2014.2334661 · 2.76 Impact Factor
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    • "[6] proposes a content sharing mechanism based on Delay Tolerant Networks (DTNs). [7] exploits opportunistic communications to facilitate information dissemination in the emerging Mobile Social Networks (MoSoNets). [8] uses the smartphone as a driving aid, giving the driver useful information in order to reduce fuel consumption and trip duration. "
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    ABSTRACT: Recently, people have been interested in sharing what they are watching on TV, allowing the development of Social TV Applications often based on mobile devices. In this context, this paper proposes IRTR (Improved Real-Time TV-channel Recognition): a new method aimed at recognizing in real time (live) what people are watching on TV without any active user interaction. IRTR uses the audio signal of the TV program recorded by smartphones and is performed through two steps: i) fingerprint extraction and ii) TV channel real-time identification. Step i) is based on the computation of the Audio Fingerprint (AF). The AF computation has been taken from the literature and has been improved in terms of power consumption and computation speed to make the smartphone implementation feasible by using an ad hoc cost function aimed at selecting the best set of AF parameters. Step ii) is aimed at deciding the TV channel the user is watching. This step is performed using a likelihood estimation algorithm proposed in this paper. The consumed power, computation and response time, and correct decision rate of IRTR, evaluated through experimental measures, show very satisfying results such as a correct decision rate of about 95%, about $2 s$ of computation time, and above 90% power saving with respect to the literature.
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