Summary: Despite the growing development of space‐based systems aimed at monitoring and studying natural hazards, they continue to harm humankind worldwide, causing enormous human and economic losses. In a view of improving the effectiveness and the timeliness of existing emergency systems, we propose a service‐oriented cloud‐based software architecture for mobile sensing applications. Exploiting existing Global Navigation Satellite Systems and public Cloud Computing services, we implement a set of mobile geolocated services enabling mobile devices to send real‐time in‐field observations. Such observations could be used by authorities and first responders for both Early Warning and Emergency Response services complementing in real time the situational assessment provided by existing means, eg, remote sensed information and in‐situ sensors. We propose a Service Oriented Architecture that can be used as a reference for implementing the back end of mobile applications requiring to send crowdsourced geolocated reports. We fully implement a real application, and we evaluate its performances with Microsoft Azure varying the user load and the main deployment parameters. Our results can be taken as reference to assess the capability of future applications with similar requirements.

Despite the growing importance of mobile crowd-sourcing applications and cloud computing, little is known about the actual performances of web services deployed within public cloud computing platforms. In order to provide an assessment of the achievable performances in such scenario, we design and implement a back-end general architecture for mobile applications requiring crowd-sourcing. We deploy our back-end in the Microsoft Azure cloud computing platform using the PaaS approach, and we evaluate its performance in terms of autoscaling, response time and request rate; while varying the number of instances, the instance type, and the number of concurrent users. Our results shed light on the achievable performances of web services aimed at ingesting crowdsourced data.