This paper presents an Incentive-based Sharing (iShare) protocol that blends cellular and ad hoc networks for content dissemination services. With iShare, mobile users download content from a source via cellular links and at the same time form a mesh ad hoc network for peer-to-peer exchange of content data. The mesh remains robust to network dynamics, minimizes ad hoc communication overhead, and parallelizes the downloading process among mesh members. In order to counter selfish behavior, we apply an efficient and practical “titfor-tat” incentive mechanism, which exploits proximity and mutual content interest of mobile users. This mechanism becomes particularly effective in the case of network dynamics since we utilize promiscuous and broadcast modes of the ad hoc channel. As a result, our protocol effectively helps to free resources in the cellular network and accelerates the content download for its users. Furthermore, it enables users to continuously obtain data via ad hoc connections during cellular handoff periods and provides multi-homing downloads for groups spanning adjacent cells. We evaluate the performance of iShare by means of simulations and compare it to other content dissemination schemes using cellular broadcast channels, cellular unicast channels, and tree-based protocols. The obtained results show that iShare significantly outperforms alternative approaches and creates a win-win situation by improving performance of both iShare and other mobile users. 1

In recent years the popularity of multi-hop wireless networks has been growing. Its flexible topology and abundant routing path enables many types of applications. However, the lack of a centralized controller often makes it difficult to design a reliable service in multi-hop wireless networks. While packet routing has been the center of attention for decades, recent research focuses on data discovery such as file sharing in multi-hop wireless networks. Although there are many peer-to-peer lookup (P2P-lookup) schemes for wired networks, they have inherent limitations for multi-hop wireless networks. First, a wired P2P-lookup builds a search structure on the overlay network and disregards the underlying topology. Second, the performance guarantee often relies on specific topology models such as random graphs, which do not apply to multi-hop wireless networks. Past studies on wireless P2P-lookup either combined existing solutions with known routing algorithms or proposed tree-based routing, which is prone to traffic congestion. In this paper, we present two wireless P2P-lookup schemes that strictly build a topology-dependent structure. We first propose the Ring Interval Graph Search (RIGS) that constructs a DHT only through direct connections between the

Abstract—In this paper, we show that the clustered mobile peer-to-peer (P2P) networks exist in numerous scenarios where mobile users collaborate to improve content distribution services. In order to understand the clustering behavior of nodes in clustered mobile P2P networks, we present a probabilistic model for path selection and use the Mobius Tool to study the clustering behavior of different movement areas. Our study shows that the cluster size distribution follows an exponential function. We then design an adaptive protocol, which blends cellular and P2P (i.e., wifi or Bluetooth) communications of the mobile devices and leverages the exponential-cluster-size function to improve content distribution services. With our protocol, mobile nodes periodically sample the current cluster size and predict the future cluster size using the exponential function. Then, nodes apply the predicted cluster size function to calculate the available data in P2P channel using Online Codes and tune the cellular download timer adaptively to meet the file download deadline. The simulation results show that our adaptive protocol achieves much higher performance than the non-adaptive protocol by reducing the downloading load on the cellular channel by 4 % ∼ 10%, and significantly reducing message overhead. Simulation results also confirm that our protocol adapts well to network dynamics since when the nodes get closer to the destination, the cluster size function is predicted more accurately. I.