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The COVID-19 pandemic has served as a wake-up call for humanitarian aid actors to reconsider data collection methods, as old ways of doing business become increasingly obsolete. Although access to information on the affected population is critical now more than ever to support the pandemic response, the limitation of aid workers’ presence in the field imposes hard constraints on relief projects. In this article, we consider how aid actors can use “big data” as a crisis response tool to support humanitarian projects, in cases when the General Data Protection Regulation is applicable. We also provide a framework for examining open-source platforms, and discuss the advantages and privacy challenges of big data.

Whether segregating a sophisticated software component into smaller services, transferring data between computers, or creating a general gateway for seamless communication, you can rely on middleware to achieve communication between different devices, applications, and software layers. Following the increasing agile movement, the tech industry has adopted the use of fast waterfall models to create stacks of layers for each structural need, including integration, communication, data, and security. Given this scope, emphasis must now be on endpoint connection and agile development. This means that middleware should not serve solely as an object-oriented solution to execute simple request-response commands. Middleware can incorporate pull-push events and streams via multiple gateways by combining microservices architectures to develop a holistic decentralized ecosystem.

This article presents a Wireless Sensor Network (WSN) system, developed to monitor concealed box-shaped underground passages to protect wildlife. The system's purpose is to recognize the species of passengers (ie, human, canis, flying species, and so on) without using video sensors (image recognition) through data provided by sensors that measure distance and weight.Moreover, the information provided by the WSN was analyzed via a variety of methods, including a neural pattern recognition network, as well as clustering algorithms, which were able to recognize the species of passengers with certainty scores of over 90%. The main concern for future study is the evaluation of these passages, which are frequently located along main highways, and using data fusion to evaluate low-cost and low-power microsensors regarding their effectiveness, that is, whether they are frequently utilized by animals.