Mobile Data Offloading through Opportunistic Communications and Social Participation

University of Maryland, College Park
IEEE Transactions on Mobile Computing (Impact Factor: 2.54). 06/2012; 11(5):821 - 834. DOI: 10.1109/TMC.2011.101
Source: IEEE Xplore


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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    • "A first class of offloading techniques based on opportunistic networks is based on approaches in which the dissemination controller has to collect information about nodes' contact rates in order to come up with the best set of nodes to trigger the dissemination of contents in the opportunistic network . To the best of our knowledge, Han et al. [39] (then subsequently extended in [40]) were the first to exploit opportunistic communications to alleviate data traffic in the cellular network. In their pioneering work they propose and evaluate three algorithms, i.e. "
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    ABSTRACT: The widespread diffusion of mobile phones is triggering an exponential growth of mobile data traffic that is likely to cause, in the near future, considerable traffic overload issues even in last-generation cellular networks. Offloading part of the traffic to other networks is considered a very promising approach and, in particular, in this paper we consider offloading through opportunistic networks of users’ devices. However, the performance of this solution strongly depends on the pattern of encounters between mobile nodes, which should therefore be taken into account when designing offloading control algorithms. In this paper we propose an adaptive offloading solution based on the Reinforcement Learning framework and we evaluate and compare the performance of two well known learning algorithms: Actor Critic and Q-Learning. More precisely, in our solution the controller of the dissemination process, once trained, is able to select a proper number of content replicas to be injected in the opportunistic network to guarantee the timely delivery of contents to all interested users. We show that our system based on Reinforcement Learning is able to automatically learn a very efficient strategy to reduce the traffic on the cellular network, without relying on any additional context information about the opportunistic network. Our solution achieves higher level of offloading with respect to other state-of-the-art approaches, in a range of different mobility settings. Moreover, we show that a more refined learning solution, based on the Actor-Critic algorithm, is significantly more efficient than a simpler solution based on Q-learning.
    Computer Communications 09/2015; DOI:10.1016/j.comcom.2015.09.004 · 1.70 Impact Factor
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    • "However, a fixed cloudlet does not necessary need to communicate with all the mobile cloudlets present. Instead, longer periods of connection can be maintained with a subset by solving the target-set selection problem suggested for the emerging Mobile Social Networks (MoSoNets) [10]. Thus, popular content can be transmitted to the target-set and redistributed over multi-hop links, leading to much larger gains. "
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    ABSTRACT: The next generation mobile networks (NGMN) are over-taxed by the increasing demand of on-the-go content access. Standards bodies and researchers are building solutions that offload the traffic demands from the NGMN infrastructure to small cells. We motivate a taxicab cloud as a mobile ISP that offloads traffic demands from NGMN to under-utilized licensed bands such as TV whitespaces. This cloud consists of mobile (taxicabs) and fixed cloudlets. Fixed cloudlets are placed around major transit hubs in New York City. Cloudlets communicate with each other using cognitive radio (CR) technology for opportunistic spectrum access in the licensed band. Mobile cloudlets feature a multi-radio design to allow various short-range connection options (Bluetooth, Wi-Fi, mmWave etc.) to user equipment (UE). Cloudlets maintain distributed caches of popular content while UEs use name based content retrieval to access content. We use this scenario as a backdrop to study the taxicab mobility pattern. This is a bottom-up approach to designing suitable network and link layer technologies as well as estimate the benefits i.e., volume of traffic offloaded from the NGMN.
    IEEE Smart Vehicles Workshop 2015; 06/2015
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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 · 3.45 Impact Factor
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