In recent years there has been a growing interest in Opportunistic Routing as a way to increase the capacity of wireless networks by exploiting its broadcast nature. In contrast to traditional uni-path routing, in opportunistic routing the nodes overhearing neighbor's transmissions can become candidates to forward the packets towards the destination.In this paper we address the question: What is the maximum performance that can be obtained using opportunistic routing? To answer this question we use an analytical model that allows to compute the optimal position of the nodes, such that the progress towards the destination is maximized. We use this model to compute bounds to the minimum expected number of transmissions that can be achieved in a network using opportunistic routing.

Channel assignment has been extensively researched for multi-radio wireless mesh networks, but it is still very challenging when it comes to its implementation. In this paper we propose a semi-dynamic and distributed channel assignment mechanism called SICA (Semidynamic Interference aware Channel Assignment) based on game theory formulation. SICA is an interference aware, distributed channel assignment which preserves the network connectivity without relying on a common channel nor central node for coordination between mesh routers. SICA applies a real time learner algorithm which assumes that nodes do not have perfect information about the network topology. To the best of our knowledge this is the first game formulation of channel assignment which takes the co-channel interference into account. We have simulated SICA and compared against other channel assignment mechanisms proposed in the literature. Simulation results show that SICA outperforms other mechanisms.

Community networks are decentralized communication networks built and operated by citizens, for citizens. The consolidation of todays cloud technologies offers now for community networks the possibility to collectively built community clouds, building upon user-provided networks and extending towards cloud services. Cloud storage and in particular secure and reliable cloud storage, could become a key community cloud service to enable end-user applications. In this paper we evaluate in a real deployment the performance of Tahoe-LAFS, a decentralized storage system with provider-independent security that guarantees privacy to the users. We evaluate how the Tahoe-LAFS storage system performs when it is deployed over distributed community cloud nodes in a real community network. Furthermore, we evaluate Tahoe-LAFS in the Microsoft Azure commercial cloud platform, in order to compare and understand the impact of homogeneous network and hardware resources on the performance of the Tahoe-LAFS. We observed that the write operation of Tahoe-LAFS resulted in similar performance when using either the community network cloud or the commercial cloud, but the read operation achieved better performance in the Azure cloud, where the reading from multiple nodes of Tahoe-LAFS benefited from the homogeneity of the network and nodes. Our results suggest that Tahoe-LAFS can run on community network clouds with suitable performance for the needed end-user experience.

Abstract-Opportunistic Routing (OR) is a new class of routing protocols that selects the next-hop forwarder on-the-fly. In contrast to traditionally routing, OR does not select a single node as the next-hop forwarder, but a set of forwarder candidates. When a packet is transmitted, the candidates coordinate such that the best one receiving the packet will forward it, while the others will discard the packet. The selection and prioritization of candidates, referred to as candidate selection algorithm, has a great impact on OR performance. In this paper we propose and study two new candidate selection algorithms based on the geographic position of nodes. This information is used by the candidate selection algorithms in order to maximize the distance progress towards the destination. We compare our proposals with other well-known candidate selection algorithms proposed in the literature through mathematical analysis and simulation. We show that candidate selection algorithms based on distance progress achieve almost the same performance as the optimum algorithms proposed in the literature, while the computational cost is dramatically reduced.