The FloodNet project centres upon the development of providing a pervasive, continuous, embedded monitoring presence. By processing and synthesizing collected
information over a river and functional floodplain, FloodNet obtains an environmental
self-awareness and resilience to ensure robust transmission of data in adverse
conditions and environments. This new class of environmental monitoring will greatly
enhance the data quality and density that is available to decision makers, and
transform the way environments are explored, monitored and controlled.
FloodNet provides a dynamic infrastructure for sensors. It allows for the spatio-temporal understanding of an environment through coordinated efforts between it's sensor nodes, which are fixed at specific points within a river or floodplain. Each sensor communicates wirelessly within its local network, thus distributing information about its environment, and thus providing a self aware, intelligent sensor network. The intelligent routing element of the system will provide the basis by which the system will be data transmission aware and robust.
FloodNet data management provides a platform by which expert decisions can be made regarding flood warning and mitigating activities. For example, a series of guidelines from operator agencies might result in the issuing of operational instructions through any number of media. These might include pagers, telephones, computer screens, with the message suggesting operation rooms become active and potential mitigation activities. Additionally, the design of the data management element of FloodNet would allow direct activation of flood warning road signs, flood warden notification, emergency services data provision (closed roads etc.)
The practical and accessible management, storage and retrieval of data are the key elements of the FloodNet data management protocols. This is required for post event analysis and to provide timely and appropriate information for post event report writing. The utilisation of simulator and flood databases provide a platform through which post-event analysis can be carried out and predictions and official responses monitored.
The FloodNet helps develop an "at risk" database of properties. The improvement in the models brought about by the detailed calibration and robust data delivery provides a better forecast of potential flood risk in monitored areas. This data set might also be of substantive interest to the insurance industry.
An ad-hoc network simulator developed by MAC Ltd and based on IEEE 802.11b technology, helps simulate the behaviour of the wireless network implemented in the FloodNet testbed, and provides the following capabilities:
- It enables a user to design and optimise an IEEE 802.11b wireless network.
- It allows a user to evaluate the network performance statistics, such as link reliability, packet loss, packet delay, network load and so forth.
- It serves as a platform for evaluating alternative strategies for improving the performance of the network before implementing these strategies in the testbed.
- It enables a user to simulate 'what-if' scenarios beyond what can be implemented in the testbed. This allows a user to test and evaluate viable scenarios.
The simulator uses MAC Ltd's MiniWorks and MicroWorks proprietary radio propagation prediction models to predict the radio coverage of the wireless nodes. These prediction algorithms take into consideration the diffraction losses caused by the underlying terrain and building features. The simulator allows the user to configure various network and node properties, such as the node antenna patterns, antenna heights, transmit powers, link budget parameters, target SIR thresholds, packet sizes and transmission bit rates (eg, 1, 2, 5.5 or 11 Mbps). The Carrier Sense Multiple Access / Collision Avoidance (CSMA/CA) protocol of the IEEE 802.11b standard is simulated and used to control the packet transmissions.
There are two routing algorithms in the simulator, namely an ideal routing algorithm and the dynamic source routing (DSR) algorithm. The ideal routing algorithm chooses the lowest cost route, where the cost of each link is its path loss. Other routing algorithms can be developed and easily integrated with the simulator.
FloodNet focuses on power conservation and low maintenance of instrumentation in the field. FloodNet adopts an automated adaptive sampling approach, which ensures a maximisation of data acquisition efficiency. Utilising a series of adjustable rule based guidelines the rate of sampling and number of sensors can be altered. A critical issue in the deployment of the sensors is the power supply available to each instrument at any given time. There is a requirement to set sampling at a reasonable rate to cover both the current predicted event as well as the potential for future events (contingency). Where power levels are minimal a strategy of decreased sampling or a reduction in the number of sensors utilised for any given event is employed.
The diagram below describes the system architecture of FloodNet. The ad hoc network is based on 802.11, and consists of powerful nodes that hosts IBM's Websphere MQ software. The nodes transmit data at regular intervals via GPRS to a micro-broker or gateway, and the gateway subsequently transmits the data to an IBM's Messaging Broker. The sensor data is transcoded and transformed at this end, and is delivered to any application that subscribes to the data. The sensor data might be used for various applications such as simulation models, GIS Visualisation and database services.
There exist 2 loops, an inner and outer loop, which utilizes the adaptive sampling approach described above. The inner loop consists of the sensor nodes that share and interpret the data being distributed within the inner network. The outer loop makes use of external sources such as Met data, and influences the sampling regime.