Feasibility of P2p-Stb Based Crowdsourcing to Speed-Up Photo Classification for Natural Disasters

We present a distributed platform aimed to process photos taken after a natural disaster strikes by people witnesses of the situation. These photos have to be processed as quickly as possible to collect statistical data used by the decision makers to coordinate rescue teams. A photo can be classified using a predefined taxonomy such as infrastructure and service, affected people, emotional support, among others. Some photos can be classified automatically while other photos require human intervention. The proposed platform is organized in three layers: an architecture, a communication pattern algorithm and optimization modules. The architecture is based on a community of digital volunteers forming a peer-to-peer network. The digital volunteers receive photos from a centralized server that collects and integrates the results into the management process to improve the general understanding of the situation or rescue actions. We present three communication pattern algorithms that define the flow of tasks between the volunteers and the server. The first algorithm is based on point-to-point communication and the other two algorithms use cache techniques inside the peer-to-peer network. Our proposal is devised for short term campaigns and we aim to speed-up the image processing process, to reduce the workload of the server and to reduce communication latency between the server and the volunteers. We evaluate our proposed platform under highly demanding task traffic rates. We analyze the impact of the input parameters of each communication pattern algorithm. We evaluate the performance of our proposed platform with different approaches presented in the technical literature which are deployed as optimization modules. Results show that the performance of the platform when using the cache-based communication pattern algorithms can outperform the one-to-one communication algorithm under high task traffic rates. © 2021, The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature.