Abstract
Gathering field data from crisis areas requires overcoming a major technical hurdle: infrastructures and terminals for Internet access are non-existent or unreliable. Teams of professionals acting on the field lack a cost-effective, convenient way to provide their data to central management headquarters and the worldwide public in a timely manner. Cell-phone software can be leveraged to bridge this. Using cellphone applications, people living or working in low-tech areas can provide their knowledge worldwide, taking full advantage of georeferenced functionalities. In this paper we present the prototype of a platform based on a wiki+map server backbone for direct information entry, query, and editing, by people on the field using cell-phones, with strong version management. This georeferenced information can then be visualized centrally for tackling development/crisis issues, such as drought problems, outbreaks of diseases, etc. - giving NGOs and governments a better framework upon which to act.

Abstract
Application execution requests in cloud architecture and fog paradigm are generally heterogeneous in terms of contexts at the device and application level. The scheduling of requests in these architectures is an optimization problem with multiple constraints. Despite numerous efforts, task scheduling in these architectures and paradigms still presents some enticing challenges that make us question how tasks are routed between different physical devices, fog, and cloud nodes. The fog is defined as an extension of the cloud, which provides processing, storage, and network services near the edge network, and due to the density and heterogeneity of devices, the scheduling is very complex, and, in the literature, we still find few studies. Trying to bring innovative contributions in these areas, in this paper, we propose a solution to the context-aware task-scheduling problem for fog paradigm. In our proposal, different context parameters are normalized through Min-Max normalization, requisition priorities are defined through the application of the Multiple Linear Regression (MLR) technique and scheduling is performed using Multi-Objective Non-Linear Programming Optimization (MONLIP) technique. The results obtained from simulations in the iFogSim toolkit, show that our proposal performs better compared to the non-context-aware proposals.

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