Toward Reliable Contention-aware Data Dissemination in Multi-hop Cognitive Radio Ad Hoc Networks

This paper introduces a new channel selection strategy for reliable contentionaware data dissemination in multi-hop cognitive radio network. The key challenge here is to select channels providing a good tradeoff between connectivity and contention. In other words, channels with good opportunities for communication due to (1) low primary radio nodes (PRs) activities, and (2) limited contention of cognitive ratio nodes (CRs) acceding that channel, have to be selected. Thus, by dynamically exploring residual resources on channels and by monitoring the number of CRs on a particular channel, SURF allows building a connected network with limited contention where reliable communication can take place. Through simulations, we study the performance of SURF when compared with three other related approaches. Simulation results confirm that our approach is effective in selecting the best channels for efficient and reliable multi-hop data dissemination

Mobile Social Networking aided content dissemination in heterogeneous networks

Since more and more mobile applications are based on the proliferation of social information, the study of Mobile Social Net-works (MSNs) combines social sciences and wireless communications. Operating wireless networks more efficiently by exploiting social relationships between MSN users is an appealing but challenging option for network operators. An MSN-aided content dissemination technique is presented as a potential ex-tension of conventional cellular wireless net-works in order to satisfy growing data traffic. By allowing the MSN users to create a self-organized ad hoc network for spontaneously disseminating contents, the network operator may be able to reduce the operational costs and simultaneously achieve an improved network performance. In this paper, we first summarize the basic features of the MSN architecture, followed by a survey of the factors which may affect MSN-aided content dissemination. Using a case study, we demonstrate that one can save resources of the Base Station (BS) while substantially lowering content dissemination delay. Finally, other potential applications of MSN-aided content dissemination are introduced, and a range of future challenges are summarized

SURF: A Distributed Channel Selection Strategy for Data Dissemination in Multi-Hop Cognitive Radio Networks

In this paper, we propose an intelligent and distributed channel selection strategy for efficient data dissemination in multi-hop cognitive radio network. Our strategy, SURF, classifies the available channels and uses them efficiently to increase data dissemination reliability in multi-hop cognitive radio networks. The classification is done on the basis of primary radio unoccupancy and of the number of cognitive radio neighbors using the channels. Through extensive NS-2 simulations, we study the performance of SURF compared to three related approaches. Simulation results confirm that our approach is effective in selecting the best channels for efficient communication (in terms of less primary radio interference) and for highest dissemination reachability in multi-hop cognitive radio networks