Abstract: Methods and systems are described for providing cost effective leak detection in a fluid network. Step testing procedures are represented by decision trees and associated expected costs are calculated. Selection of step testing procedures is optimized for low expected cost. The total expected cost rate for a network configuration may be calculated from the rate of leak occurrence and the optimal expected costs associated therewith. Network configuration changes may be recommended by optimizing for total expected cost rate for a fluid network.

Abstract: A computerized method for monitoring a water utility network, the water utility network comprising a network of pipes for delivering water to consumers and a plurality of meters positioned within the pipes across the water distribution network. The method includes receiving meter data representing parameters measured by the meters, such as flow, pressure, chlorine level, pH and turbidity of the water being distributed through the pipes. The method also includes receiving secondary data from sources external to the meters and representing conditions affecting consumption of water in a region serviced by the water utility network such as weather and holidays. The meter and secondary data is analyzed using statistical techniques to identify water network events including leakage events and other events regarding quantity and quality of water flowing through the pipes and operation of the water network. The events are reported to users via a user interface.

Abstract: A computerized method for determining one or more statistically likely geographical locations of an anomaly in a region or zone of a water utility network, the water utility network comprising at least a network of pipes for delivering water to consumers and a plurality of meters positioned within the water utility network including a plurality of meters capturing data regarding the water delivering within the region or zone of the anomaly, the method comprising. The method includes receiving anomaly event data, the anomaly event data representing an indication of an anomaly occurring or having occurred within a region or zone of the water utility network, the anomaly event data being associated with meter data produced by one or more of the meters. The method also includes performing a plurality of tests on the anomaly event data each designed to statistically determine a likely geographical location of the anomaly within the region or zone, the performance of each test producing a result.

Abstract: A computerized method for modeling a utility network. The method includes retrieving geographical information system (GIS) data, asset management data, and sensor archive data of one or more assets of the utility network. The method also includes generating one or more mathematical elements from the one or more assets and creating probable connections between the one or more mathematical graph elements based on the GIS and asset data. A mathematical graph is generated based on the probable connections, the mathematical graph including one or more asset characteristics of the one or more assets. Analysis is performed on the utility network using the mathematical graph and the mathematical graph data is stored for use by other systems within the utility network.

Abstract: A computerized method for determining one or more statistically likely geographical locations of an anomaly in a region or zone of a water utility network, the water utility network comprising at least a network of pipes for delivering water to consumers and a plurality of meters positioned within the water utility network including a plurality of meters capturing data regarding the water delivering within the region or zone of the anomaly, the method comprising. The method includes receiving anomaly event data, the anomaly event data representing an indication of an anomaly occurring or having occurred within a region or zone of the water utility network, the anomaly event data being associated with meter data produced by one or more of the meters. The method also includes performing a plurality of tests on the anomaly event data each designed to statistically determine a likely geographical location of the anomaly within the region or zone, the performance of each test producing a result.

Abstract: Methods and systems are described for providing cost effective leak detection in a fluid network. Step testing procedures are represented by decision trees and associated expected costs are calculated. Selection of step testing procedures is optimized for low expected cost. The total expected cost rate for a network configuration may be calculated from the rate of leak occurrence and the optimal expected costs associated therewith. Network configuration changes may be recommended by optimizing for total expected cost rate for a fluid network.

Abstract: A computerized method for determining one or more statistically likely geographical locations of an anomaly in a region or zone of a water utility network, the water utility network comprising at least a network of pipes for delivering water to consumers and a plurality of meters positioned within the water utility network including a plurality of meters capturing data regarding the water delivering within the region or zone of the anomaly, the method comprising. The method includes receiving anomaly event data, the anomaly event data representing an indication of an anomaly occurring or having occurred within a region or zone of the water utility network, the anomaly event data being associated with meter data produced by one or more of the meters. The method also includes performing a plurality of tests on the anomaly event data each designed to statistically determine a likely geographical location of the anomaly within the region or zone, the performance of each test producing a result.

Abstract: A computerized method for modeling a utility network. The method includes retrieving geographical information system (GIS) data, asset management data, and sensor archive data of one or more assets of the utility network. The method also includes generating one or more mathematical elements from the one or more assets and creating probable connections between the one or more mathematical graph elements based on the GIS and asset data. A mathematical graph is generated based on the probable connections, the mathematical graph including one or more asset characteristics of the one or more assets. Analysis is performed on the utility network using the mathematical graph and the mathematical graph data is stored for use by other systems within the utility network.

Abstract: A resource-node of a peer-to-peer (P2P) network adapted to manage the allocation of resources to channel swarms. Resource allocation is based on a distributed algorithm in which resource-nodes of the P2P network occasionally join and leave channel-swarms. Each resource-node independently decides whether or not to stay in the channel-swarm that it has joined to. These decisions are based on values of a transience parameter set by a transience mechanism of the resource-node.

Abstract: A computerized method for detecting related events in a water monitoring system. The method includes receiving event data from a plurality of sensors in a water network. The method also includes identifying at least two candidate events from the event data as being candidate events which are likely to constitute part of a common anomaly event, the candidate events each being substantially anomalous. An event combination rule is selected. One or more event characteristics are compared between the at least two candidate events based on the event combination rule. The at least two candidate events are determined whether they are related and associated with a common event causing the at least two candidate events on the basis of the comparison. The determination of the at least two candidate events are related and associated with a common event is reported to a user via a user interface.

Abstract: A computerized method for determining one or more statistically likely geographical locations of an anomaly in a region or zone of a water utility network, the water utility network comprising at least a network of pipes for delivering water to consumers and a plurality of meters positioned within the water utility network including a plurality of meters capturing data regarding the water delivering within the region or zone of the anomaly, the method comprising. The method includes receiving anomaly event data, the anomaly event data representing an indication of an anomaly occurring or having occurred within a region or zone of the water utility network, the anomaly event data being associated with meter data produced by one or more of the meters. The method also includes performing a plurality of tests on the anomaly event data each designed to statistically determine a likely geographical location of the anomaly within the region or zone, the performance of each test producing a result.

Abstract: A computerized method for monitoring a water utility network, the water utility network comprising a network of pipes for delivering water to consumers and a plurality of meters positioned within the pipes across the water distribution network. The method includes receiving meter data representing parameters measured by the meters, such as flow, pressure, chlorine level, pH and turbidity of the water being distributed through the pipes. The method also includes receiving secondary data from sources external to the meters and representing conditions affecting consumption of water in a region serviced by the water utility network such as weather and holidays. The meter and secondary data is analyzed using statistical techniques to identify water network events including leakage events and other events regarding quantity and quality of water flowing through the pipes and operation of the water network. The events are reported to users via a user interface.

Abstract: A computerized method for monitoring a water utility network, the water utility network comprising a network of pipes for delivering water to consumers and a plurality of meters positioned within the pipes across the water distribution network. The method includes receiving meter data representing parameters measured by the meters, such as flow, pressure, chlorine level, pH and turbidity of the water being distributed through the pipes. The method also includes receiving secondary data from sources external to the meters and representing conditions affecting consumption of water in a region serviced by the water utility network such as weather and holidays. The meter and secondary data is analyzed using statistical techniques to identify water network events including leakage events and other events regarding quantity and quality of water flowing through the pipes and operation of the water network. The events are reported to users via a user interface.

Abstract: A resource-node of a peer-to-peer (P2P) network adapted to manage the allocation of resources to channel swarms. Resource allocation is based on a distributed algorithm in which resource-nodes of the P2P network occasionally join and leave channel-swarms. Each resource-node independently decides whether or not to stay in the channel-swarm that it has joined to. These decisions are based on values of a transience parameter set by a transience mechanism of the resource-node.

Abstract: A method for managing the allocation of resources to channel swarms in a peer-to-peer (P2P) network. The method is based on a distributed algorithm in which resource-nodes occasionally join and leave channel-swarms. Each resource-node independently decides whether or not to stay in the channel-swarm that it has joined to. These decisions are based on values of a transience parameter set by a transience mechanism.

Abstract: A method for managing the allocation of resources to channel swarms in a peer-to-peer (P2P) network. The method is based on a distributed algorithm in which resource-nodes occasionally join and leave channel-swarms. Each resource-node independently decides whether or not to stay in the channel-swarm that it has joined to. These decisions are based on values of a transience parameter set by a transience mechanism.