Experimentation is an invaluable part of learning in all sciences. However, building and maintaining laboratories is expensive, time and space consuming. Moreover, in computer science advances in technology can quickly make the infrastructure obsolete. In this paper we advocate the use of recently deployed public testbeds as remote labs for computer science education. As an example we describe the successful use of the GENI testbed in graduate and undergraduate courses and present a specific case study of GENI being used in an undergraduate class on Network Management and Intelligent Networks.

In this paper, we address the problem of distributed resource management for secondary users in Cognitive Radio Networks (CRNs), through a topology aware and frequency agile cross-layer approach. We exploit the theory of spatial processes and propose a Markov Random Field (MRF) based framework, which enables secondary CRN users to achieve efficient and viable mechanisms in the lower protocol stack layers by exchanging local only information. Specifically, through Gibbs sampling secondary users can optimize in a distributed and parallel manner their channel allocation, medium access and routing without resolving to otherwise computationally demanding optimization approaches. Through analysis and simulation we exhibit the efficacy of the proposed framework and show that a semi-parallel implementation can significantly reduce the required overhead cost compared to the sequential Gibbs sampling approach, while retaining a very close performance to the latter. We also study the emerging trade-offs by demonstrating the performance benefits in terms of channel assignment, medium access and data flow.

Secondary ad hoc users of Cognitive Radio networks experience constant fluctuations of their link quality due to dynamic traffic behavior of their primary network counterparts. Several cognitive-aware MAC and routing protocols have been proposed in order to counterfeit properly such spectrum variations. In this paper, we introduce a vertical time-based control mechanism in the traditional protocol stack, aiming at properly balancing the inherent trade-off between instant local adaptations at MAC layer, against slower, but more globally aware rerouting reaction mechanisms. Based on localized and independent node decisions, an optimal feedback policy is proposed in order to exploit the most appropriate protocol layer in each case and provide per link channel assignments, such that flow rate requirements are satisfied with the least cost. We use Markov theory to examine the coexistence of primary and secondary networks, while the optimal decision policy is derived via a Markov Decision Process (MDP) and linear programming. Analysis and simulations are used for performance evaluation and together they confirm that the proposed time-based cross-layer feedback strategy contributes to higher network performance regarding flow rate requirements, sensing and rerouting costs.