Sensor networks are applied in a variety of spheres today – manufacturing, construction, medicine, science, and many others. Sensors have different capabilities – motion detection, temperature and pressure measurement, light detection, etc.
Often sensor networks are wireless. Besides, there are tactile sensors that assume physical interaction with them performed by humans or by robots. These can be mobile and wearable devices that utilize accelerometer. In these cases, we deal with iOS and Android sensor data processing.
What sensor-based networks have in common is a huge data array that they provide – namely, a great number of indicators being monitored. To get the maximum from the data received and to have an opportunity to make useful insights based on it, the incoming streams need to be processed properly.
With our experience in this field, we name the two major issues that sensor data processing is usually accompanied by:
After the data streams are processed, we face the next big challenge – to provide a user with the output that he/she requests. To cope with this issue, we make the necessary calculations and generate various reports beforehand. Based on these reports, a system can further create a customized report upon a user’s request. This adds significantly to data retrieval process acceleration.
To implement this approach, we utilize three types of data warehouse:
As long as Big Data is applied here, performance and scalability issues need to be taken into account.
Actually, when dealing with sensor-based networks data processing, we apply the same techniques as we use in Big Data tasks. That is, we process data beforehand (say, at night). The next day we have the already processed information.
We’ll give you an example. To calculate an average value for a year, we need to know average values for each month. If we already have the monthly values calculated, coming up with the average value for a year isn’t an issue anymore.
In addition to this, we constantly receive fresh data. Thus, real-time sensor data processing is needed as well.
ISS Art team has rich experience in creating complex systems that apply latest advances in IT. Sensor data processing is not an exception. We built a powerful prognostic solution for the industrial sphere. To provide an accurate forecast, this system analyzes large data streams coming from sensor controllers. The created application is a great instrument to prevent equipment malfunctions and, thus, to reduce production downtime.
We are proud of our works, and we are ready for new challenges. Should you need assistance in building a robust application that involves Big Data processing – say, data processing in wireless sensor networks – we are at your service. Please contact us to learn more.
