[4] Naumowicz, T., et al., Wireless Sensor Network for Habitat Monitoring on Skomer Island, 35th Conference on Local Computer Networks (LCN). 2010: Denver ...
37th Annual IEEE Conference on Local Computer Networks
LCN 2012, Clearwater, Florida
A Wireless Mesh Sensor Network for Hazard and Safety Monitoring At the Port of Brisbane Amin Ahmadi*, Abbas Bigdeli*§, Mahsa Baktashmotlagh*§, Brian C. Lovell*§ * National ICT Australia (NICTA), Brisbane, Australia § The University of Queensland, School of ITEE, Brisbane, Australia {amin.ahmadi, abbas.bigdeli, mahsa.baktashmotlagh, brian.lovell}@nicta.com.au
Abstract— A wireless sensor network (WSN) was designed and implemented to provide reliable long-term hazard monitoring at the Port of Brisbane, Australia. The proposed system consists of four sensor nodes, a wireless gateway and a central monitoring computer. The sensor nodes are capable of measuring a range of hazardous events along with the time and location of events in maritime environment. Each sensor node is also equipped with a Global Positioning System (GPS) module and a ZigBee module. The monitoring server is a personal computer application server with Internet connectivity. Sensor nodes utilize smart algorithm to save energy and AES-128 encryption to encode data prior to sending the data packet through the wireless ZigBee protocol to gateway. The gateway collects and decrypts the data packets and forwards them to the monitoring computer through wireless connection. A database server running on the monitoring computer stores the captured data for visualization and further analysis. The monitoring server is interfaced to Google Maps to overlay real-time data from the sensor nodes onto map in the correct corresponding locations. The WSN system was successfully deployed and tested at the Port of Brisbane, Queensland, Australia.
applications. In this study, we describe the design and implementation of a wireless sensor network at the Port of Brisbane, Australia. The main motivating factors arose during a practical investigation into the task of improving security and surveillance in maritime environments. Over 90% of world trade goes by sea and most of the international maritime ports in the world are handling oil, gas and dangerous goods. There are also some ports that allow visits from nuclear powered warships (NPW). Hazardous events in such a maritime environment can take many forms, including fires and smoke, oil spills, releases of toxic gases and fumes, and chemical, biological and radiation hazards. Fire hazards can be devastating in a maritime port, particularly during the handling of cargo such as Liquefied Petroleum Gas (LPG) gas, oil, and chemicals. A land-based oil spill that released liquid petroleum hydrocarbon into the port environment could have serious effects on maritime safety. An accident involving a reactor plant in an NPW may cause significant radiation damage to the public, property and the environment. Therefore, protecting and monitoring port operations is one of the most critical challenges facing a nation’s homeland security. A major part of port security and safety is Closed Circuit Television (CCTV) video surveillance [11, 12]. However the latest surveillance camera technology alone cannot ensure and establish a reliable, efficient safety system covering all marine operations. For example, CCTV alone cannot detect chemical spills or radiation. To ensure that risks are kept to a minimal level and that emergencies are well managed, we should use more senses than just sight alone. For example, a human senses danger with all five senses of sight, smell, hearing, touch, and taste. If these senses could all work together with the latest video surveillance systems, security could be increased to a much higher level. Wireless sensor networks have shown a new dimension of environmental monitoring, due to the recent advances in wireless communication and MEMS sensor technologies. This technology has made possible the development of low power, low-cost multifunctional wireless micro sensor nodes. Therefore, with the use of well-designed wireless sensor networks, the environment’s safety status could be monitored and hazards could be detected rapidly. One of the main challenges for environmental sensor networks is finding a
Keywords-component; Wireless Sensor Network (WSN), hazard monitoring, microcontroller systems, Internet connectivity.
I.
INTRODUCTION
Recent developments in low power embedded processors, radio modules and Micro-Electro-Mechanical Systems (MEMS) have made possible the development of new wirelessly interconnected sensors. These Wireless Sensor Networks (WSNs) are ushering in another technological revolution that facilitates observation and control of the physical world, just as the Internet’s working technology has done for the ways in which individuals and organizations exchange information. The application space for wireless sensor networks is numerous. Over the past decade, a number of applications have been emerged, deployed and evaluated. Environmental monitoring [1-4], patient monitoring [5, 6], athlete performance monitoring [7, 8] and security and surveillance [9, 10] are just a few of those diverse
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reliable source of energy. Sensor networks can be sustainable if the system can be powered for a long time without the need for human intervention. Solar energy has been utilized as a long-term energy source for wireless sensor networks. Given the above high-level overview of the problem, this work is dedicated to the investigation, design and implementation of a wireless sensor network to detect hazardous events occurring in such environments. Although the design of this wireless sensor network is based on the problem of the Port of Brisbane, we note that all the research work and real-life deployment performed here would apply to the field of wireless sensor networks in general. A number of recent studies have increased the focus on field and environmental monitoring using wireless sensor networks [1-4, 13]. However, none of these systems have been implemented to measure the whole range of critical physical phenomena in the marine environment. In this paper, we propose a wireless sensor network encompassing sensor nodes, a wind detection sensor, a radiation sensor board, a dust sensor board, a gas sensor board, a GPS module, a ZigBee module, a wireless access point and a monitoring computer. Each sensor node is connected to a solar panel to harness solar energy for recharging the batteries. The designed networked system sends the encrypted data packets to a gateway, which in turn is connected to the monitoring computer through the WiFi connection. The database server running on the computer is capable of storing the captured data on a 24/7 basis for further data analysis. The developed web interface has been designed to allow easy visualization of the data returned from each node as well as to flag problems in the network – e.g. nodes are not responding due to low battery level. II.
• Power supply subsystem consisting of rechargeable battery, DC-DC converter, battery charge controller and solar panel. TGS4161 and TGS2611 gas sensors were used to monitor CO2 and CH4 respectively. A 2-axis ultrasonic wind sensor (WindSonic, Gill Instruments) was used to sense wind speed (0-60m/s) as well as wind direction (0-359º) at the Port. The Geiger tube (J305β) was utilized to detect β and γ radioactive particles in an omnidirectional way. The dust sensor (GP2Y1010AU0F) is an optical sensor. Infrared light reflected by dust particles can be detected and captured by means of a phototransistor. The gateway was an off-the-shelf multi-protocol router, which supports five wireless standards including WiFi, ZigBee, GPRS, Bluetooth and GPS, as well as Ethernet. The main function of the gateway is to retrieve data from the sensor network and forward them to the database for further processing and viewing by end users. For this experiment, wireless sensors sent their data to the gateway through the ZigBee radio. A gateway utilizing the Web Manager System can collect and pass the sensor data to the MySQL database in the monitoring server using WiFi wireless protocol. III.
SOFTWARE PLATFORM
Two layers are involved in developing the system’s software architecture: a data acquisition layer and a presentation layer. A. Data Acquisition Layer The data acquisition layer is responsible for reading the sensor data from the sensor boards and the GPS module. All sensor data, along with the GPS coordinate data, can be read and encapsulated in a standard packet using the ATmega1281 microcontroller. The microcontroller then sends the data packet to the gateway through the ZigBee radio link. The gateway then sends the data to the monitoring computer through the WiFi connection. The data acquisition layer was implemented in C++ programming language and consisted of the following main functions.
HARDWARE PLATFORM
Four sensor nodes, being a gas sensor node, a wind sensor node, a radiation sensor node and a dust sensor node, as well as a gateway and a monitoring server, were required to design and implement the wireless sensor network for the Port. All of the sensor nodes use an ATmega1281 microcontroller operating at 8MHz along with the 802.15.4/ZigBee radio transceiver module operating in different frequencies including 868MHz, 900MHz and 2.4GHz. The device also incorporates a Real-Time Clock (RTC) chip to reduce microcontroller overheads for timing operations and a 2GB SD memory card as an external storage support. The architecture relies on the I2C, UART and SPI buses where the ATmega1281 acts as a master and can communicate with the radio, the flash memory the RTC module and external sensors and sensor boards. All nodes are powered from a combination of rechargeable Li-Ion batteries, a DC/DC boost converter and solar cells. The system architecture comprises four basic subsystems: • Computing subsystem consisting of a microcontroller; • Communication subsystem consisting of low power radio transceiver; • Sensing subsystem consisting of a group of sensor modules; and
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Creating Network: In order to create a network, each ZigBee module needed to be initialized and connected to the sensor node via a UART port. The communication protocol and the baud rate used to open the UART were the required parameters to be defined in the initialization stage. All the ZigBee modules had to have the same initialization parameters within a network. Next, the PAN ID, a unique number to differentiate a network, was assigned to each node of the network. Finally, the frequency channel used by each module to transmit and receive the data was set.
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Sensor-Read: This function is responsible for configuring the input/output ports and reading the sensor values from the corresponding sockets on different sensor boards. The read sensor values were in volts, which in turn needed to be translated to a meaningful value, such as gas concentration or dust density.
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GPS-Read: This function was used to acquire position data, including latitude, longitude and altitude, from the GPS module attached to the sensor node through a serial port.
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Data-Encryption: When creating or joining a network, using security is essential to prevent the network from attacks or intruder nodes. A 128-bit Advanced Encryption Standard (AES) encryption key was used to encrypt/decrypt data. The entire payload of the packet was encrypted using the key and the Cyclic Redundancy Check (CRC) was computed across the cipher text. When encryption is enabled, each packet carries an additional 16 bytes to convey the random Cypher Block Chaining (CBC) Initialization Vector (IV) to the receivers.
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Sleep-Mode: This function stops the main program and reduces the power consumption of microcontroller and other modules. Interrupts from the Real Time Clock (RTC) and sensors can wake up the system. It is feasible to specify whether the “time to wake up” is to be added to the actual time (offset) or is to be treated as an actual time when the RTC should be activated to send the interrupt. Being in sleep mode reduces the power consumption of the microcontroller and the ZigBee module. Both the ZigBee and the microcontrollers were fully operational while in normal mode. While in sleep mode, ZigBee module could neither receive nor send any data packets and the microcontroller could only receive interrupts from the sensors and the RTC.
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Data-Send: This function is responsible for sending the RF data from one sensor node to another node or to the gateway using the Unicast and Broadcast modes. In Unicast mode, receiving modules send an acknowledgment (ACK) signal of RF packet reception to the transmitter. If the transmitting module does not receive the ACK, it will resend the packet up to three times or until the ACK is received. Broadcast mode can also be employed if a packet needs to be sent to all the nodes in a network. In order to send a packet from one node to another, destination address (sensor node Mac address) needs to be known. In some cases, due to maximum payload limitation, it is necessary to fragment a big data packet and send it to the destination through a number of packets.
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Data-Receive: This function is responsible for receiving a packet or fragmented data packet from a sender. First it checked whether the received packet was a new packet or a fragment of an already existing packet. Once a packet was received via RF, the module would send the data via UART, so it was required to check periodically if data was available. The function responsible for reading packets could read more than one fragment, but the ZigBee module may overflow its buffer, thereby requiring all packets to be read one by one.
B. Presentation Layer The data acquisition service consisted of a Java-based program running as a Windows service. This service queries the WSN Gateway on the TCP port and updates the MySQL database. The database comprises of five tables. Every time a data packet is received from a sensor node, the data are extracted from the packet and stored in a table. Data from different sensor nodes and GPS module are stored in different tables. The entry for each table provides the identification of the data frame (ID-Frame), the date and the time at which the data were received to the gateway (Timestamp), and the Media-Access-Control address (MAC-Address) of the sensor node along with the data values reported by each sensor node. The data presentation logic was implemented as a set of Java servlets, which is the core of this architecture. These Java servlets generated geographic maps using Google Maps API to identify exact locations of sensors, based on GPS coordinates. The servlets also generated data charts requested by users and presented to users via an Apache Tomcat web server. The main feature of this design is that all components were designed on a modular architecture so that they can be independently managed. In other words, applying changes in one software component does not affect other software components. Therefore, change management can be easily incorporated to the system. The data presentation layer is illustrated in Fig. 1. IV.
IMPLEMENTATION AND RESULTS
The sensor network is presented to the end users as a geographic map with sensors located in their GPS coordinates as shown in Fig. 2. This figure shows how a user can use the Internet, via Google Maps, to access sensor data at the Port of Brisbane. In the interface shown in Fig. 2, each sensor node is provided with its details including the type of sensor board as well as a hyperlink pointing to its data charts. Users can explore sensor measurements using various types of time varying charts. A user can click on each sensor node to retrieve the corresponding sensor data in a new web page. As an example of the system output presentation, real data acquired from gas sensor node installed at the Port of Brisbane are illustrated in Fig. 3. Figure 3 shows the data retrieved from the gas sensor node. The CO2 and CH4 gas concentrations, as well as the relative humidity and the temperature, were captured using this sensor node. It can be seen that the relative humidity was approximately 17% and the temperature was just below 30 degrees Celsius. The CH4 gas concentration was quite steady and was around 875 ppm, as opposed to the CO2 gas concentration, which varied between 1000ppm, and 3500ppm. One of the main reasons that a sensor node stops working is that a sensor battery has run out. Therefore, it is important to continuously monitor the remaining battery charge for each sensor node. The remaining battery charge for the gas sensor node is also shown in Fig. 3.
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CONCLUSION
In this paper, the design and implementation of wireless sensor networks for environmental safety monitoring at the Port of Brisbane was discussed. The system consists of various sensor nodes to detect physical phenomena, including wind speed and direction, radiation, dust density, temperature, humidity and dangerous gases. The data collected by sensor nodes were transmitted to the gateway using the ZigBee link. The gateway in turn can send the data to the database resident inside the monitoring computer through the WiFi connection. Google Maps interface was employed to access the sensor data through the Internet. It can be envisaged that the WSN implemented at the Brisbane port can be employed as a safety and security monitoring system in different environments.
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ACKNOWLEDGMENT NICTA is funded by the Australian Government’s Department of Broadband, Communications and Digital Economy as well as the Australian Research Council through Backing Australia’s Ability and the ICT Research Centre of Excellence programs. The authors would like to thank Mr. John Kohlbach for his invaluable assistance in this project. REFERENCES [1]
[2] [3]
[4]
Fig. 2. Sensor location presentation on a map
[5] [6] [7]
[8] [9]
[10]
[11]
[12] [13]
Fig. 3. Gas sensor node data
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Al-Ali, A.R., Zualkernan, I., and Aloul, F., A Mobile GPRS-Sensors Array for Air Pollution Monitoring. IEEE Sensors, 2010. 10(10): p. 1666-1671.) Szewczyk, R., et al., Habitat monitoring with sensor networks. Commun. ACM, 2004. 47(6): p. 34-40. Wark, T., et al., Springbrook: Challenges in Developing a Long-Term, Rainforest Wireless Sensor Network, in Intelligent Sensors, Sensor Networks and Information Processing (ISSNIP). 2008: Sydney, Australia. p. 599-604. Naumowicz, T., et al., Wireless Sensor Network for Habitat Monitoring on Skomer Island, 35th Conference on Local Computer Networks (LCN). 2010: Denver, CO, p. 882-889. Hande, A., et al., Self-Powered Wireless Sensor Networks for Remote Patient Monitoring in Hospitals. Sensors, 2006. 6(9): p. 1102-1117. Walker, W., et al. Remote Blood Pressure Monitoring Using a Wireless Sensor Network. 2006. USA. Ahmadi, A., Rowlands, D., and James, D.A., Towards a wearable device for skill assessment and skill acquisition of a tennis player during the first serve. Sports Technology, 2009. 2(3-4): p. 129-136. Llosa, J., et al., REMOTE, a Wireless Sensor Network Based System to Monitor Rowing Performance. Sensors, 2009. 9(9): p. 7069-7082. Son, B., Her, Y.-S, and Kim, J.-G., A Design and Implementation of Forest-Fires Surveillance System based on Wireless Sensor Networks for South Korea Mountains. International Journal of Computer Science and Network Security, 2006. 6(9): p. 124-130. He, T., et al., Energy-efficient surveillance system using wireless sensor networks, in Proceedings of the 2nd international conference on Mobile systems, applications, and services. 2004, ACM: Boston, MA, USA. Bigdeli, A.L., Lovell, B.C., Sanderson, C., Shan, T., Chen, S., Vision Processing in Intelligent CCTV for Mass Transport Security, in Signal Processing Applications for Public Security and Forensics. 2007: Washington, DC, USA p. 1-4. Talley, W.K., Maritime safety, security and piracy. 2008, London: Informa Law. Barrenetxea, G., Ingelrest, F., Schaefer, G., Vetterli, M., Couach, O., Parlange, M., Sensorscope: Out-of-the-box environmental monitoring. International Conference on Information Processing in Sensor Networks (IPSN). 2008: St. Louis, MO, p. 332 - 343.
