Water distribution systems comprise labyrinthine networks of pipes, often in poor states of repair, that are buried beneath our city streets and relatively inaccessible. Engineers who manage these systems need reliable data to understand and detect water losses due to leaks or burst events, anomalies in the control of water quality and the impacts of operational activities (such as pipe isolation, maintenance or repair) on water supply to customers. Water Wise is a platform that manages and analyses data from a network of wireless sensor nodes, continuously monitoring hydraulic, acoustic and water quality parameters. Water Wise supports many applications including rolling predictions of water demand and hydraulic state, online detection of events such as pipe bursts, and data mining for identification of longer-term trends. This paper illustrates the advantage of the Water Wise platform in resolving operational decisions.

Global climate change can be mitigated with innovative solutions in the ambient intelligence domain that utilize distributed and heterogeneous sensor networks for high resolution sensing of resource utilization (e.g. energy, water, etc.) via intelligent coordination among sensor nodes. We propose the EcoSense framework as a holistic solution that enables dynamic control and reconfiguration of the sensor nodes in an ambient environment based on our resource consumption patterns. EcoSense introduces service and context layers to existing sensor network stacks to enable interoperability between heterogeneous sensor node platforms. A prototype of the framework for an eco-aware smart space has been developed as a pervasive infrastructure for monitoring and minimizing resource consumption at individual levels and also for building eco-aware homes and workspaces.

In this paper, we present an overview of a system that we are developing for the continuous monitoring of water distribution systems in Singapore. We discuss the design and implementation of the system that enables real-time detection of pipe burst events.

Based on our proposed semantics-based service-oriented model, we are building a large-scale sensor grid infrastructure that can seamlessly integrate heterogeneous sensor resources from different projects distributed across a wide geographical area. This model has several important features. First, it connects heterogeneous sensor resources via the Internet to automatically collect and aggregate sensor data in real-time. Second, the varied types and formats of sensor data are linked to the ontology of the respective sensor resource and the data is converted into a standard ontology-based (OWL) format in order to bind data semantics with the data for ease of access and processing. Third, the sensor data are stored in a large-scale distributed data archive whose schema is also derived from the common concepts found in the ontologies of various sensor nodes. Fourth, the sensor grid integrates computational resources for the compute-intensive processing of sensor data. Fifth, the sensor data can be conveniently accessed and shared via the Web through mash-ups, blogs and Web-services such as Google Earth and Microsoft's SensorMap. We are developing techniques and tools to publish, query, process, visualize, archive and search the vast amount of sensor data efficiently.

The middleware for a cyber-physical system is crucial as it tightly integrates computation with physical processes to achieve better reliability, distributed coordination, higher precision and efficiency, and better autonomous control. In this work, we design and develop the cyber-physical middleware framework for a large-scale water distribution system monitoring effort.

At present, the sensor node platforms from different vendors cannot easily interoperate with each other due to their heterogeneous programming environments, communication stacks and data management protocols. In this work, we develop a sensor network virtualization framework that will enable the seamless interoperability and scalability of heterogeneous sensor node platforms.

In this paper, we present the idea that large-scale ambient intelligence takes the vision of anytime-anywhere to anytime-anywhere-anything (A3). Based on this vision, we argue that a mix of computing, communication and interface technologies remains limited in providing seamless access to services if the data and services from various autonomously operating entities remain non-sharable. Thus, we propose a sensor grid infrastructure that forms the key resource sharing backbone and provides secure access to valuable sensor, computational, data, and storage resources for supporting large-scale ambient intelligence.

This paper proposes a cross-layer protocol for energy-aware routing in wireless sensor networks. The protocol combines the energy depreciation rate, node distance and neighbourhood information from physical layer together with TDMA schedules from the MAC layer and also network life requirements from the application layer, to effectively determine the most efficient routes to the base station. This cross layer efficiency measure is then used to form dynamic clusters that adapt to changing traffic and energy conditions so assisting to both control the transmission power and schedule the sleep-wake cycles of nodes for better energy utilisation. The proposed protocol is a recursive aggregation scheme that transforms a network-wide routing dissemination problem into a single-hop query protocol that aids nodes in making multi-hop routing decisions based solely upon the information provided by their single-hop neighbours. Results confirm that the proposed technique balances the load on forwarding nodes, adapts the MAC layer precisely to the routing layer and minimizes data delivery time for increased traffic and large scale networks.

With the rapid advances in technologies such as MEMS sensors, low-power embedded processing and wireless networking, sensor networks are becoming more powerful in terms of data acquisition and processing capabilities. Sensor networks can now be deployed in the physical world for various important applications such as environmental monitoring, weather monitoring and modeling, military surveillance, healthcare monitoring, tracking of goods and manufacturing processes, smart homes and offices, etc.

Any ad hoc ubiquitous network should be capable of taking proactive measures for smooth reconfiguration in the event of energy loss and a failure to key network Parent Nodes (PN). The overall objective of reconfiguration is to keep the network operating effectively for a prescribed time frame. The load profiles of PNs can be used to define its current state as well as to predict potential failures caused by energy loss due to high loads on particular PNs. This paper presents a new technique utilising historical load profile information about each PN to improve the estimate accuracy of its remaining lifetime before failure. A novel load balancing model is introduced that maintains PNs in a state whereby the time- to-live requirement is met by the PNs comprising the backbone of the network. Results prove that the proposed methodologies achieve the reconfiguration objectives as well as maintaining QoS, especially in high density ad hoc networks.

Proactive self-configuration is indispensable for MANETs like ubiquitous sensor networks (USNs), as component devices of the network are usually exposed to natural or man-made disasters due to the hostile deployment and ad hoc nature of USNs. Network state beacons (NSBs) are exchanged among the key nodes of the network for crucial and effective monitoring of the network for steady state operation. The rate of beacon exchange (F/sub E/) and its contents, define the time and nature of the proactive action. Therefore it is very important to optimize these parameters to tune the functional response of the USN. The paper presents a novel F/sub E/ selection model based on autonomic, load-adaptive optimization of beacon exchange rate for monitoring and proactively reconfiguring the network. The results confirm the improved throughput while maintaining QoS with a little overhead control traffic.