Visual Sensor

The components of the visual sensing unit are shown in Figure4.5. The camera installed at the test site is an Axis P1344-E IP camera, which is an IP66-rated camera that has protection against dust, rain, snow and sunlight, and can operate in temperature as low as −40 ^◦C. It also provides 1 Mega Pixel HDTV 720p resolution, day and night image and/or video streams. Another advantage of this camera is that it can be either powered by an 8 − 20 V external power source or powered over Ethernet (POE). It also consumes

Figure 4.4: An example of biofouling (6 weeks after deployed) on the deployed sensor at Dublin Port during May-June 2013.

relatively low power (max. 6.4 W) compared to other commonly used IP cameras (e.g.

Vivoteck IP8352 IP camera consumes max. 10 W). The camera was mounted on a pole at a height of 4.36 m above the ground and approximately 20 m from the river bank wall.

This position is suitable for monitoring the shipping traffic while also being close to the location of the sonde. The camera is connected to a Fit-PC2i control board through Eth-ernet cable. For this pilot system, the visual sensor is connected to the mains electricity.

Figure 4.5: The visual sensor unit, which consists of an IP66-rated Axis P1344-E IP camera for image capturing, a Huawei E353 3G modem for image data transmission and a Fit-PC2i nettop for controlling.

The Fit-PC is a tiny, light, fan-less, inexpensive nettop computer. It supports the main operating systems such as Window and Linux. It consumes relatively low power, 6 W at low load and 8 W at full load. It supports a Wi-Fi connection by using a Wi-Fi network

card and a 3G mobile network by using a mobile broadband modem, WiMAX network connection by using a WiMax dongle or RJ45 wired Internet connection. It also provides standard USB and HDMI ports, which are convenient for on-site diagnostics. In this initial system, Fit-PC is chosen due to its convenience for development and on-site diagnostics.

For a future release version of visual sensing system, a much more cost effective embed-ded board, such as Raspberry Pi (10 % of the cost and 30 % of the power consumption compare to Fit-PC), could be investigated. The control board connects to the IP camera through RJ45 connection and retrieves image data from it via HTTP protocol. Image data are then sent back to a cloud server through a 3G mobile network. At our test site, the frame rate of the camera is set to 1 frame every 10 seconds. This is due to two main rea-sons: the network speed at the location and the duration of the target events. From human inspection, we found that the fastest object moving on the water surface is speeding boats.

The configured frame rate will capture at least one image of such an event. In some cases, there may also a upper limit of the amount of data that can be transmitted, e.g. the monthly data allowance of some mobile broadband package allows 10 Gigabytes data per month.

Mobile broadband is one of the wireless internet connection mechanisms that is based on third generation wireless broadband technologies (3G). A mobile broadband service can be used anywhere within a coverage area. It provides high speed upload internet access.

Current HSDPA (one of the 3G standards) deployments support down-link speeds of up to 42 Mbps and up-link speeds of up to 5.76 Mbps. In this work, a HuaWei E353 3G modem with Meteor mobile carrier is used. Figure4.6 shows the results of Meteor 3G mobile broadband upload and download speed tests at various locations in Dublin. From the graph, it can be seen that the minimum upload speed is 0.75 Mbps. At the test site (Dublin Bay), the upload speed is 2.7 Mbps. The upload speed that the system requires is 0.31 Mbps when uploading image data at 1 frame per second. Thus, 3G mobile broadband provides sufficient bandwidth for the visual sensor.

The main technical issue of the visual sensing system is the unreliable 3G connection.

The control board has to restart itself to establish a new connection, which can result in a small disruption to the image data stream (2 minutes data lost).

Figure 4.6: Meteor 3G mobile broadband upload and download speed test. Dublin Bay data is obtained from the test site.

4.4 Summary

This chapter provides an overview of the test location used for a practical deployment of the system introduced in the previous chapter and the complexity of the site is dis-cussed. This test site presents a real challenge in environmental monitoring because of the complex interactions of parameters such as tide, stratification and human activities.

The technologies deployed at the site are also discussed in this chapter along with their maintenance procedure. It should be noted that both of the sensors suffer real world issues such as biofouling and data communication issues.

CHAPTER 5

IN-SITU DATA PROCESSING

5.1 Introduction

In this chapter, a case study of abnormal event detection and clustering from in-situ sen-sor data is carried out. The case study illustrates how state-of-the-art computer science techniques can be used to automate the processing of raw sensing data measured from aquatic sensing instruments to provide comprehensive information, which is more suit-able for management especially at a much larger scale. Anomaly sensor readings are first isolated from the input data stream and further grouped into events based on their tempo-ral information. These abnormal events are then catalogued into clusters based on their similarities. This chapter is organized as follows. Section5.2introduces the importance of salinity and turbidity at estuaries. An abnormal event detection and clustering system framework is proposed in Section 5.3. The testing data, which is used for evaluating the proposed methods is described in Section5.4and statistical analysis of this testing dataset is carried out in Section5.5. Section5.6shows how the parameters of the detection and clustering system are selected. The experimental results are described in Section5.7and 5.8followed by a discussion in Section5.9. The analysis carried out in this chapter relates to research question 1 and 2 in Chapter1.