In the context of environmental monitoring it is of great importance to capture the dynamic of the underlying physical systems and phenomena to understand how both human-generated and naturally-generated events impact the environment itself.
Acquiring, collecting, storing and analysing data over long time spans is fundamental to understand these complex dynamics and to proactively address potential threats as well as understanding cause-effect relationships. Moreover, real time insights allows to get immediately warned as soon as dangerous anomalies arise and to act quickly to prevent more damages.
Implementing an effective environmental monitoring system is very demanding on several fronts.
Outdoor sensors are exposed to harsh environments and they do not always deliver accurate readings, making it very hard for the subject matter experts to perform manual checks.
Moreover, to fully take advantage of the IoT revolution and to enable real time insights, these monitoring systems need to work in continuous operation mode, and require live, compute-intensive data processing.
Technical challenges include:
- Collecting data coming from different acquisition systems;
- Analyzing data with different sampling rates;
- Manual data correction that requires extensive experience, focus and time;
- Sensor malfunctions that generate missing and misleading data;
- The complexity of developing complex models describing environmental systems dynamics;
- The development of real-time methods to automatically detect structural dynamic changes requires several different technologies and expertise, including IoT, signal processing, artificial neural networks and web applications.
Our platform allows to easily create real-time systems capable of automatically spotting and correcting inaccurate readings in order to understand cause-effect relationships and to promptly and reliably detect anomalies or specific events.
Sharpsense combines the information coming from hundreds of sensors, the knowledge of subject-matter experts and cutting edge Artificial Intelligence models to provide next generation monitoring and early warning systems tailored for the specific client use cases.
Technical solutions include:
- A software agent to easily connect several different sensor types, acquisition systems or databases to our platform;
- A centralised cloud platform to manage all data, analyses and reports;
- Automatic data validation methods to check incoming data quality (e.g. periodicity and cross-correlation analysis);
- Easy to use graphical interface to train complex models (no coding required);
- Real-time analyses and anomaly detection methods based on complex AI models.