# Optimized Sensors Network and Dynamical Maps for Monitoring Traffic Noise in a Large Urban Zone
## 2 State of the Art
### Innovative Characteristics of the Dynamap System  
The usual approach to real-time noise mapping consists of implementing a network of fixed sensors that collect noise data continuously, transmitting them to a data center for the analysis. The latter is performed by using a traffic noise model software (Cadna) which rescales some pre-computed partial noise maps according to the measured data. Each monitoring station can, in principle, identify each relevant single noise source such as road, railway, etc. Then, the new rescaled partial maps are summed up together in order to build a new noise map of the whole area, which is periodically updated on a web site. Thanks to this approach, it is possible to achieve a clear and understandable real-time graphic picture of the noise distribution in the monitored area, thus building up a kind of acoustic 'consciousness' in the citizens.  
Unfortunately, the application of such real-time noise mapping is limited to small areas because of the high cost of both: the monitoring stations (implementing expensive features not needed for the main scope of the network) and noise model software (which should run continuously to obtain real-time maps). Moreover, the available systems compute and publicly update only noise maps, but no other environmental parameters such as vibrations, temperature, humidity, UV, CO, NOx, PM10, etc., with no possibility to drive any real-time action to control unhealthy situations in the form of variable road message panels, dynamic speed limits, etc.  
In view of this situation, the Dynamap real-time mapping approach [65; 66; 67], which uses purposely developed low cost monitoring stations and does not require a continuously running complex software, can be considered as the missing link between a well consolidated technology and the need for a cheaper and scalable system to map noise and other environmental pollution parameters.  
Specifically, the Dynamap network is built on a reduced number (few dozens) of noise stations, distributed appropriately within the monitored urban zone, continuously recording noise data at 1 s resolution [68; 69; 70]. The system has a recognition algorithm for the removal of spurious noise and easy to read updated noise maps on a website endowed with a geographic information system (GIS). Therefore, the project confirms its compliance with END requirements for both noise map production and information to citizens.  
Moreover, anomalous sound events have to be detected and eliminated from the recording signals in order to build up a robust system where the measured levels used for maps scaling are not affected by occasionally or external events that could lead to unreliable results. The intrinsic complexity of the environmental sound recognition problem has been approached with novel techniques and tested successfully in many different circumstances. Indeed, environmental sounds present a structure and characteristics very different from those of, say, speech and music to name the two types of sound sources for which recognition techniques have been mostly developed. For this reason, trying to recognize environmental sounds by just adapting well-established approaches to music or speech recognition is a suboptimal way to proceed. On the contrary, it becomes necessary to devise specific recognition strategies to tackle this kind of problem [71; 72; 73; 74; 75; 76].