RIS: A Reciprocal Incentive Scheme in Selfish Opportunistic Networks

Abstract

Data dissemination in opportunistic networks has been extensively studied in recent years. In the resource-constrained opportunistic networks (e.g., the buffer size, the communication bandwidth and the energy of nodes are scarce), the nodes usually behave selfishly. In other words, the nodes only store data objects they are interested in and are not willing to contribute their own resource to store and forward data objects for other nodes. Their selfish behaviors significantly decrease the data dissemination performance of opportunistic networks. In this paper, a Reciprocal Incentive Scheme (RIS) is put forward, which can create a win-win situation for the Internet Service Provider, A-type nodes (the nodes which can freely download a large amount of content from the Internet) and B-type nodes (the nodes which must pay much money to the ISP for downloading data objects from the Internet by direct connection). When RIS is deployed in a selfish opportunistic network, we analyze in detail how these selfish nodes select data objects for their limited buffer to maximize their revenue. Furthermore, this paper investigates the relationship between the decisions made by the nodes and the scope of network information they maintain. Extensive trace-driven simulations based on MIT trace are conducted to evaluate the data dissemination performance of the whole system when all nodes try to maximize their revenue during the process of data dissemination. The results demonstrate that RIS could increase the average delivery ratio of each channel by 18 % and reduce the average receiving delay of each channel by 40 %. Moreover, B-type nodes only need to pay much less money for each subscribed data object than the situation where no incentive scheme is adopted in the system. The simulation results also show that the larger scope of network information the nodes maintain, the better performance RIS can achieve.