Mobile social networks (MSNs) enable people with similar interests to interact without Internet access. By forming a temporary group, users can disseminate their data to other interested users in proximity with short-range communication technologies. However, due to user mobility, airtime available for users in the same group to disseminate data is limited. In addition, for practical consideration, a star network topology among users in the group is expected. For the former, unfair airtime allocation among the users will undermine their willingness to participate in MSNs. For the latter, a group head is required to connect other users. These two problems have to be properly addressed to enable real implementation and adoption of MSNs. To this aim, we propose a Nash bargaining-based joint head selection and airtime allocation scheme for data dissemination within the group. Specifically, the bargaining game of joint head selection and airtime allocation is first formulated. Then, Nash bargaining solution (NBS) based optimization problems are proposed for a homogeneous case and a more general heterogeneous case. For both cases, the existence of solution to the optimization problem is proved, which guarantees Pareto optimality and proportional fairness. Next, an algorithm, allowing distributed implementation, for join head selection and airtime allocation is introduced. Finally, numerical results are presented to evaluate the performance, validate intuitions and derive insights of the proposed scheme.
Existing protocols on data dissemination in MSNs (e.g. @cite_11 @cite_25 @cite_24 @cite_15 ) mostly focus on selecting proper data carriers, i.e. whether users should exchange data when they meet, while how data is exchanged after they decide to exchange data is often neglected or simplified. The major reason is that previous studies on MSNs predominantly assume that nodes contact with each other in a pairwise manner. And this assumption makes problem arising in data exchange, such as airtime allocation, seemingly trivial and therefore overlooked by previous studies. However, simultaneous multiple contact is quite common in many cases such as conference, underground, and tourist sites. This viewpoint is supported by a recent study on real-world contact traces @cite_7 . Clearly, group communication among multiple contacting nodes can be more efficient than pairwise communication for content dissemination if multiple users are in contact. And the problem of airtime allocation for content dissemination within a group is nontrivial.
@cite_7 @cite_24 @cite_15 @cite_25 @cite_11
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With the rapid growth of smartphone usage, mobile social networks (MSNs) are becoming increasingly popular. MSN can be considered as a type of delay tolerant network (DTN) which lacks continuous end-to-end connections between nodes, due to the node mobility and limited transmission range. Inspired by the homophily of social networks that friends are usually similar in characteristics, we present a novel concept - cloud, where the nodes in frequent contact with the destinations will form destination clouds. Neighbors in the destination cloud have a special status that can forward the message to the destination directly. We propose a cloud-based multicast scheme with feedback in MSNs with two phases: pre-cloud and inside-cloud. In the pre-cloud process, the message holder will forward the copy of the multicast message to the encountered node, based on a given forwarding metric. The forwarding metric can be iteratively refined from a feedback control mechanism. In the inside-cloud process, the message holder will wait until it meets with the destinations. We analytically formulate the multicast problem into a continuous Markov chain problem, and formally analyze the latency in this model. Extensive trace-driven simulations show that our scheme significantly improves the performance compared to existing schemes. The emerging of mobile social networks opens opportunities for viral marketing. However, before fully utilizing mobile social networks as a platform for viral marketing, many challenges have to be addressed. In this paper, we address the problem of identifying a small number of individuals through whom the information can be diffused to the network as soon as possible, referred to as the diffusion minimization problem. Diffusion minimization under the probabilistic diffusion model can be formulated as an asymmetric @math -center problem which is NP-hard, and the best known approximation algorithm for the asymmetric @math -center problem has approximation ratio of @math and time complexity @math . Clearly, the performance and the time complexity of the approximation algorithm are not satisfiable in large-scale mobile social networks. To deal with this problem, we propose a community based algorithm and a distributed set-cover algorithm. The performance of the proposed algorithms is evaluated by extensive experiments on both synthetic networks and a real trace. The results show that the community based algorithm has the best performance in both synthetic networks and the real trace compared to existing algorithms, and the distributed set-cover algorithm outperforms the approximation algorithm in the real trace in terms of diffusion time. The increasing penetration of smart devices with networking capability form novel networks. Such networks, also referred as pocket switched networks (PSNs), are intermittently connected and represent a paradigm shift of forwarding data in an ad hoc manner. The social structure and interaction of users of such devices dictate the performance of routing protocols in PSNs. To that end, social information is an essential metric for designing forwarding algorithms for such types of networks. Previous methods relied on building and updating routing tables to cope with dynamic network conditions. On the downside, it has been shown that such approaches end up being cost ineffective due to the partial capture of the transient network behavior. A more promising approach would be to capture the intrinsic characteristics of such networks and utilize them in the design of routing algorithms. In this paper, we exploit two social and structural metrics, namely centrality and community, using real human mobility traces. The contributions of this paper are two-fold. First, we design and evaluate BUBBLE, a novel social-based forwarding algorithm, that utilizes the aforementioned metrics to enhance delivery performance. Second, we empirically show that BUBBLE can substantially improve forwarding performance compared to a number of previously proposed algorithms including the benchmarking history-based PROPHET algorithm, and social-based forwarding SimBet algorithm. We study the dissemination of dynamic content, such as news or traffic information, over a mobile social network. In this application, mobile users subscribe to a dynamic-content distribution service, offered by their service provider. To improve coverage and increase capacity, we assume that users share any content updates they receive with other users they meet. We make two contributions. First, we determine how the service provider can allocate its bandwidth optimally to make the content at users as "fresh" as possible. More precisely, we define a global fairness objective (namely, maximizing the aggregate utility over all users) and prove that the corresponding optimization problem can be solved by gradient descent. Second, we specify a condition under which the system is highly scalable: even if the total bandwidth dedicated by the service provider remains fixed, the expected content age at each user grows slowly (as log(n)) with the number of users n. To the best of our knowledge, our work is the first to address these two aspects (optimality and scalability) of the distribution of dynamic content over a mobile social network.