This paper presents a novel Q-learning based auction (QL-BA) algorithm for dynamic spectrum access in a one primary user multiple secondary users (OPMS) scenario. In the auction market, the secondary user provides a bidding price dynamically and intelligently using a Q-learning based bidding strategy to compete for current access opportunity; meanwhile primary user decides to whom to release the unused spectrum according to the maximal bidding principle. To obtain the limited and time-varying spectrum opportunities, each bidder presents a preference utility through Q-learning, considering the current packet transmission and future expectation. Simulation results show that the proposed QL-BA can significantly improve secondary users' bidding strategies and, hence, the performance in terms of packet loss, bidding efficiency and transmission rate is improved progressively.
Routing is one of the most challenging problems in the opportunistic network owing to the occasion-connected mobile wireless environment. To overcome this weakness, many routing protocols have been put forward to solve it by exploiting the nodes' mobility history. Meanwhile, to parallel the current trend of the social network, some of them design the solution by utilizing the social relationship characteristics from the real world. Nevertheless, few of these works could close the gap between the two totally different meanings and improve the efficiency of the whole network based on both. In this paper, we propose social relationship enhanced predicable routing (SREP) in the opportunistic network. The whole algorithm depends on this truth-the nodes in the opportunistic network only visits some defined place because of its necessary relationship with other people, thus we could adapt the semi-deterministic Markov process to model the behavior of the node. And we also introduce PageRank algorithm to quantify social degree of node. The simulation shows that SREP is an effective routing protocol in a specific scenario based on the human motion.
Opportunistic networking-forwarding messages in a disconnected mobile ad hoc network via any encountered nodes-offers a new mechanism for exploiting the mobile devices that many users already carry. However, forwarding messages in such a network is trapped by many particular challenges, and some protocols have contributed to solve them partly. In this paper, we propose heterogeneous content-aware routing protocol (HCR) for opportunistic network, an approach containing basic cognitive thoughts, that focuses on optimal efficiency, based on the distinct significance of content renders distinguish priorities, including the quantity of copy, TTL and so on, in the process of transmission. Simulation results reflect that compared with the Spray-and-wait and Prophet, the HCR improves the performance of the delivery ratio and delay of the network.
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