Connectivity sharing in wireless mesh network

Internet access is still unavailable to one-third of the world population due to the lack of infrastructure, high cost, and the digital divide. Many access-limited communities opt for shared Internet access to build common network infrastructures to mitigate the cost. Internet connectivity in such infrastructures is typically provided by several limited, sometimes non-dedicated, gateways. Client nodes, i.e., the end-user hosts, use one gateway and switch to another when the first fails. In this scheme, the gateway configuration is done manually on the end-user side. This form of Internet connectivity is widespread and has the advantage that no central control is required, but it is also unreliable and inefficient due to several factors, such as unbalanced traffic load across the gateways. The thesis proposes that the network would benefit from a gateway selection mechanism that can provide good connectivity to the client nodes, balanced load distribution, and a dynamic adaptation to the current network state. However, providing such a dynamic gateway selection is complicated: since the performance of the gateways as perceived by the client nodes changes frequently and might depend on the location of the latter in the network, and optimal selection would require the continuous monitoring of the gateway performance by the client node. The cost of such network-wide performance monitoring is high in large-scale networks and would outweigh the benefits of the dynamic gateway selection. The thesis aims to design a low-cost distributed mechanism that provides an efficient and dynamic gateway selection while considering the overall balanced gateway selection distribution. ​ We presented the Sense-Share-Select framework, a distributed, collaborative Internet gateway selection framework designed for large-scale networks. We integrated the Power of Two Choices randomized load balancing algorithm with our close neighbors' collaborative performance monitoring algorithm to reduce and distribute the gateway performance monitoring overhead. The selection algorithm utilizes the capacity-based selection algorithm to avoid selecting the unstable gateways and provide a suitable gateway selection for each node. The result shows the framework provides over 90% accurate collaborative performance monitoring with minimal overhead and equally distributes the clients over the good, stable performing gateways. The framework is implemented in the guifi.net community network testing environment with production gateways to demonstrate the efficiency of the proposed framework in the large-scale, heterogeneous network. The Sense-Share-Select framework improves the current manual gateway selection of the guifi.net by improving the QoE of all client nodes and provides better QoE than the other state-of-the-art gateway selection algorithms. ​(SC - Sciences) -- UCL, 202