## 3 Air Pollution Monitoring System Infrastructure
### e-Science Infrastructure Based on Grid Computing
#### 3.3.2 P2P-based Sensor Grid architecture in MoDisNet  
Within large scale mobile sensor network architectures, the sensors themselves naturally form and communicate with each other as a P2P network. As the GUSTO sensor can measure pollutants at very high level of accuracy and throughput at very short intervals, which means the volumes of generated and transferred data can be up to gigabit magnitude each day per sensor. This raises many informatics challenges to the data process and storage. In order to satisfy the real-time analysis requirements, the sensors themselves must store part of the information and communicate with each other within a P2P network. The measurements from sensors, both mobile and static, will be filtered and processed using a set of specialized algorithmic processes, before being warehoused within a repository. In order to satisfy the real-time analysis requirements as well as the data storage/communication trade-offs, the sensors in MoDisNet Grid are equipped with sufficient computational capabilities to participate in the Grid environment and to feed data to the warehouse as well as perform analysis tasks and communicating with their peers.  
Within MoDisNet the sensor grid should be developed to support two important techniques: firstly the techniques allowing the analysis of transport, weather and pollution data in real-time using P2P methods and protocols; secondly the design of new communication protocols supporting dynamic real time data aggregation and statistics. These will provide MoDisNet with the ability to support the full scale analytical task ranging from dynamic real time mining of sensor data to the analysis of off-line data warehoused for historical analysis. To satisfy the demands above, the MoDisNet sensor grid architecture is designed as illustrated in Figure 5.  
The GUSTO sensors (including SSN and MSN) connect to the MoDisNet Grid by several Sensor Gateways (SGs) according to different wireless access protocols. The sensors are capable of collecting the air pollution data up to 1 Hz frequency and sending the data to the remote Grid service hop by hop (a multi-hop style). This capability enable the sensors exchange their raw data locally and then realize the data analysis and mining in distributed way. This capability also presents the potential for further data fusion and aggregation (which is beyond the research of this paper, and we will discuss it briefly in section 7). The SGs take in charge of connecting the wireless sensor network with the IP backbone, which can be either wired or wireless. These SGs can monitor the volumes of the data streams from the sensors and execute the load balancing function to avoid transfer collisions, which is very useful for improving the throughput and performance of the Grid architecture. A warehouse that can be accessed by SQL database is managed by the Grid architecture which centrally stores and maintains all the archived data, including derived sensor data and the third part data such as the traffic data, the weather data and the health data. These data can provide wealth of information for the Grid computation to generate the short-term or long-term models that relate to the air pollution and traffic, down to the level of streets and buildings. Further more, it may give the supervision for the prediction of the forthcoming events about the traffic change and pollution trend. In term of the visual workflow tools, it can also provide real-time output to the end user to make a full understanding of the air pollution and traffic conditions in different locations within the monitor areas.