Saturday, February 9, 2008
Sunday, January 13, 2008
Wireless Sensor Network Devices
Wireless, ad hoc sensor networks (WSNs) differ from the more commonly seen sensor networks in that they offer more than automated data collection and monitoring of systems.
Fleck circuitboardCSIRO has designed and developed a durable, yet versatile device capable of sensing, computation, actuation and wireless communications. These devices, known as "Flecks", consist of a low power CPU with additional off-chip flash memory and a radio transceiver. Each Fleck has the capacity for sensor boards to be connected, however, some sensors such as temperature and charge current are built into the base platform as standard.
CSIRO's Flecks run the Berkley TinyOS operating system (with programs built using NesC) on the Atmega128L microprocessor, making them functionality similar to other MOTES such as Crossbow's MICA series. Fleck's utilise the Nordic nRF903 transceiver for communications achieving ranges up to 500m in open outdoor environments.
There are many technical and theoretical issues to address in realising a network of devices that sense the environment, share information with other devices through wireless communications, formulate group decisions and instigate useful actions while managing energy through energy harvesting from the environment and minimising energy expenditure where possible.
To research these various issues large Fleck networks are being deployed to create a number of real-world experimental testbeds. While these short term experiments will focus on applications to enhance Australian agricultural and telecommunications industries, it is envisaged that such research will be applicable to many other applications.
Network and Sensor Technology
The aim of this project is to conduct a scoping study of various sensor and network technologies for integrated plant systems. More precisely, to investigate the nature and performance of various combinations of modern sensor and network technologies in coal preparation plants.This study will help establish design and performance criteria of sensor and network technologies. This knowledge will highlight the potential to retrofit existing systems, and to assist with the purchase of new systems. Eventually, though the reduced cost and reliability of sensor and network infrastructure, preparation plants will become increasing automated.In the coal industry, improved computer models of process and control technologies has brought major benefits for the operators of coal preparation plants. However, a truly autonomous plant requires an integrated approach to all aspects of its operation. The software for mine management, process control, and plant maintenance is already available in many forms, and improvements are taking place continuously. Typically these systems comprise of disparate specialised applications that are not integrated. At present an integrated systems approach is not feasible, due in part to design incompatibility of commercial turn-key systems.The cost of sensor technologies has plummeted in recent years, however, the cost of networking sensors has actually risen as a consequence of increased system complexity and performance demands. Technological developments that enable integration of disparate sensors and networks is occurring, but the best and most efficient solutions for the coal preparation plant have yet to be determined.This project will evaluate:*The state of modern fieldbus technology, including industrial ethernet*The suitability of commercial middleware, to bridge disparate systems*The performance of wireless communications in the coal prep environment (Bluetooth, WiFi, AdHoc RF networks, GSM, SMS, GPRS)
The aim of this project is to conduct a scoping study of various sensor and network technologies for integrated plant systems. More precisely, to investigate the nature and performance of various combinations of modern sensor and network technologies in coal preparation plants.This study will help establish design and performance criteria of sensor and network technologies. This knowledge will highlight the potential to retrofit existing systems, and to assist with the purchase of new systems. Eventually, though the reduced cost and reliability of sensor and network infrastructure, preparation plants will become increasing automated.In the coal industry, improved computer models of process and control technologies has brought major benefits for the operators of coal preparation plants. However, a truly autonomous plant requires an integrated approach to all aspects of its operation. The software for mine management, process control, and plant maintenance is already available in many forms, and improvements are taking place continuously. Typically these systems comprise of disparate specialised applications that are not integrated. At present an integrated systems approach is not feasible, due in part to design incompatibility of commercial turn-key systems.The cost of sensor technologies has plummeted in recent years, however, the cost of networking sensors has actually risen as a consequence of increased system complexity and performance demands. Technological developments that enable integration of disparate sensors and networks is occurring, but the best and most efficient solutions for the coal preparation plant have yet to be determined.This project will evaluate:*The state of modern fieldbus technology, including industrial ethernet*The suitability of commercial middleware, to bridge disparate systems*The performance of wireless communications in the coal prep environment (Bluetooth, WiFi, AdHoc RF networks, GSM, SMS, GPRS)
Wireless Sensor and Actuator Network
Wireless sensor and actuator networks (WSANs) are a new technology for collecting data about the natural or built environment. Capitalizing on Moore ’s Law which facilitates low-cost embedded sensory and computational elements with ad hoc wireless capability, these networks will provide information on an unprecedented temporal and spatial scale. They are a new instrument for observing our world.Management of our natural resources, land, rivers and oceans, is a national priority and increased information is critical to improved outcomes. There exists enormous opportunities for scientific leadership in creating robust distributed and adaptive systems that utilize this data. This report describes the results of the first two years of CSIRO’s research program, from July 2004 to June 2006, in the new world of wireless sensor networks. It shows a rich mixture of cutting edge technology and novel applications.It contains information about the wireless sensor and actuator networks activities in the areas of:* hardware* programming the nodes* handling the data* solar power* the QCAT test bed* micro-climate* farm environment* water quality monitoring* marine environment* energy monitoring and control* sensor networks and robotic interaction* publications* visitors, visits and seminars* linkages.
Wireless sensor and actuator networks (WSANs) are a new technology for collecting data about the natural or built environment. Capitalizing on Moore ’s Law which facilitates low-cost embedded sensory and computational elements with ad hoc wireless capability, these networks will provide information on an unprecedented temporal and spatial scale. They are a new instrument for observing our world.Management of our natural resources, land, rivers and oceans, is a national priority and increased information is critical to improved outcomes. There exists enormous opportunities for scientific leadership in creating robust distributed and adaptive systems that utilize this data. This report describes the results of the first two years of CSIRO’s research program, from July 2004 to June 2006, in the new world of wireless sensor networks. It shows a rich mixture of cutting edge technology and novel applications.It contains information about the wireless sensor and actuator networks activities in the areas of:* hardware* programming the nodes* handling the data* solar power* the QCAT test bed* micro-climate* farm environment* water quality monitoring* marine environment* energy monitoring and control* sensor networks and robotic interaction* publications* visitors, visits and seminars* linkages.
Introduction to Wireless Sensor Network
A wireless sensor network (WSN) is a wireless network consisting of spatially distributed autonomous devices using sensors to cooperatively monitor physical or environmental conditions, such as temperature, sound, vibration, pressure, motion or pollutants, at different locations. The development of wireless sensor networks was originally motivated by military applications such as battlefield surveillance. However, wireless sensor networks are now used in many civilian application areas, including environment and habitat monitoring, healthcare applications, home automation, and traffic control. In addition to one or more sensors, each node in a sensor network is typically equipped with a radio transceiver or other wireless communications device, a small microcontroller, and an energy source, usually a battery. The size of a single sensor node can vary from shoebox-sized nodes down to devices the size of grain of dust. The cost of sensor nodes is similarly variable, ranging from hundreds of dollars to a few cents, depending on the size of the sensor network and the complexity required of individual sensor nodes. Size and cost constraints on sensor nodes result in corresponding constraints on resources such as energy, memory, computational speed and bandwidth. In computer science and telecommunications, wireless sensor networks are an active research area with numerous workshops and conferences arranged each year.
A wireless sensor network (WSN) is a wireless network consisting of spatially distributed autonomous devices using sensors to cooperatively monitor physical or environmental conditions, such as temperature, sound, vibration, pressure, motion or pollutants, at different locations. The development of wireless sensor networks was originally motivated by military applications such as battlefield surveillance. However, wireless sensor networks are now used in many civilian application areas, including environment and habitat monitoring, healthcare applications, home automation, and traffic control. In addition to one or more sensors, each node in a sensor network is typically equipped with a radio transceiver or other wireless communications device, a small microcontroller, and an energy source, usually a battery. The size of a single sensor node can vary from shoebox-sized nodes down to devices the size of grain of dust. The cost of sensor nodes is similarly variable, ranging from hundreds of dollars to a few cents, depending on the size of the sensor network and the complexity required of individual sensor nodes. Size and cost constraints on sensor nodes result in corresponding constraints on resources such as energy, memory, computational speed and bandwidth. In computer science and telecommunications, wireless sensor networks are an active research area with numerous workshops and conferences arranged each year.
Body Sensor Networks (BSN)
The last decade has witnessed a rapid surge of interest in new sensing and monitoring devices for healthcare and the use of wearable/wireless devices for clinical applications. One key development in this area is implantable in vivo monitoring and intervention devices. While the problem of long-term stability and biocompatibility is being addressed, several promising prototypes are starting to emerge for managing patients with acute diabetes, for treatment of epilepsy and other debilitating neurological disorders and for monitoring of patients with chronic cardiac diseases. Despite the technological developments of sensing and monitoring devices, issues related to system integration, sensor miniaturization, low-power sensor interface circuitry design, wireless telemetric links and signal processing have still to be investigated. Moreover, issues related to Quality of Service, security, multi-sensory data fusion, and decision support are active research topics. To address general issues related to using wearable/wireless and implantable sensors and to bring together scientists from computing, electronics, bioengineering, medicine and industry, the term BSN – Body Sensor Networks was coined by Prof Guang-Zhong Yang of Imperial College in 2002 and after much preparation, the first International Workshop on BSN was launched in 2004. Some of the key research focuses of the BSN community includes the latest technological developments and clinical applications of:
Novel bioelectrical, biochemical, biophysical, and mechanical sensors
Hardware considerations: low power RF transceiver, energy scavenging, battery technology, miniaturisation, system integration, process and cost of manufacturing
Biocompatibility and materials
Context awareness and multi-sensor data fusion
Data inferencing, knowledge discovery, and prediction
Quality of service, trust and security issues
Autonomic sensor networks
Standards and light-weight communication protocols
Integration with ambient sensing with applications in smart dwellings, and home monitoring
Wearable and implantable sensor integration and development platforms
Clinical applications of body-sensor networks
The last decade has witnessed a rapid surge of interest in new sensing and monitoring devices for healthcare and the use of wearable/wireless devices for clinical applications. One key development in this area is implantable in vivo monitoring and intervention devices. While the problem of long-term stability and biocompatibility is being addressed, several promising prototypes are starting to emerge for managing patients with acute diabetes, for treatment of epilepsy and other debilitating neurological disorders and for monitoring of patients with chronic cardiac diseases. Despite the technological developments of sensing and monitoring devices, issues related to system integration, sensor miniaturization, low-power sensor interface circuitry design, wireless telemetric links and signal processing have still to be investigated. Moreover, issues related to Quality of Service, security, multi-sensory data fusion, and decision support are active research topics. To address general issues related to using wearable/wireless and implantable sensors and to bring together scientists from computing, electronics, bioengineering, medicine and industry, the term BSN – Body Sensor Networks was coined by Prof Guang-Zhong Yang of Imperial College in 2002 and after much preparation, the first International Workshop on BSN was launched in 2004. Some of the key research focuses of the BSN community includes the latest technological developments and clinical applications of:
Novel bioelectrical, biochemical, biophysical, and mechanical sensors
Hardware considerations: low power RF transceiver, energy scavenging, battery technology, miniaturisation, system integration, process and cost of manufacturing
Biocompatibility and materials
Context awareness and multi-sensor data fusion
Data inferencing, knowledge discovery, and prediction
Quality of service, trust and security issues
Autonomic sensor networks
Standards and light-weight communication protocols
Integration with ambient sensing with applications in smart dwellings, and home monitoring
Wearable and implantable sensor integration and development platforms
Clinical applications of body-sensor networks
To coordinate work in a distributed sensor network, an active network of mobile code is being constructed. The system uses resource bounded optimization to adapt dynamically to a chaotic environment. This 3-year research effort started in July 1999.
Users make demands for information. Placing them at the center of an active network. Sensors are present at multiple locations in the environment. The network forages for information, like ants forage for food
Users make demands for information. Placing them at the center of an active network. Sensors are present at multiple locations in the environment. The network forages for information, like ants forage for food
Environmental Sensor Networks
Introduction
Environmental sensor networks have the capability of capturing local and broadly-dispersed information simultaneously; they also have the capacity to respond to sudden change in one l ocation by triggering observations selectively across the network while simultaneously updating the underlying complex system model and/or reconfiguring the network. Data gathered by wireless sensor networks, either fixed or mobile, pose unique challenges for environmental modeling: a complex system is being observed by a dynamical network. Technical challenges in statistics (sampling design to prediction and prediction uncertainty), in mathematics (computational geometry to data fusion to robotics), and in computers science (self-organizing networks to algorithm analysis) combine with the technical challenges of the models themselves and the sciences that underlie them.
Sampling from wireless networks:Cost of spatio-temporal data in terms of both energy and delay: Each sample has a footprint in power in space and time, some value to one or more process models (e.g., importance of parameters in space and time, sensitivity of estimates to the observation), and some cost (e.g., data transmission). Is it possible to derive frameworks such that the utility of each sample exceeds its cost?
Frameworks for adaptive sampling: game-theoretic, reinforced learning, dynamic experimental design. How can a sampling scheme to respond to highly non-stationary dynamics in energy-constrained sampling networks. Are modes of operation controlled by adaptive state machines enough?
Environmental modeling from sensor networks:Model complexity and adequacy: In the trade-off of dimensionality and predictive accuracy, what are the diagnostics for excessive vs. insufficient parametrization? Can models be developed so that reduced forms (e.g., deleting submodels, subsets of parameters, or reducing resolution of observations and/or parameter specification) still function simultaneously with near-optimality at several scales?
Prediction Uncertainty: Appropriate modeling of sources of uncertainty due to sampling (e.g., "lost" samples, outliers, bad sensors, measurement noise, and unreliable communication), and integration of data models with process models.
Model adequacy: Is there coherent noise in bio-micrometeorological systems that should drive exploration of new regions of the state space?
Networks, forests and global change:Process level understanding of how forested ecosystems respond to global change is critical for anticipating consequences of human impacts on landscapes. To be successful an approach will entail integrated models of a complex system and will involve heterogeneous data and analyses that directly address uncertainty and model selection issues.
Specific analyses will focus on increasing the capacity to forecast consequences of global change, using existing data from forest sensor networks as both testbed and primary research objective.
Inference on scaling relationships will be implemented in simulation of whole forests to examine potential consequences of changing climate and atmospheric CO2 for forest diversity and carbon sequestration. These results will have immediate application to the problem of forecasting biosphere responses to atmospheric change.
Environmental sensor networks have the capability of capturing local and broadly-dispersed information simultaneously; they also have the capacity to respond to sudden change in one l ocation by triggering observations selectively across the network while simultaneously updating the underlying complex system model and/or reconfiguring the network. Data gathered by wireless sensor networks, either fixed or mobile, pose unique challenges for environmental modeling: a complex system is being observed by a dynamical network. Technical challenges in statistics (sampling design to prediction and prediction uncertainty), in mathematics (computational geometry to data fusion to robotics), and in computers science (self-organizing networks to algorithm analysis) combine with the technical challenges of the models themselves and the sciences that underlie them.
Sampling from wireless networks:Cost of spatio-temporal data in terms of both energy and delay: Each sample has a footprint in power in space and time, some value to one or more process models (e.g., importance of parameters in space and time, sensitivity of estimates to the observation), and some cost (e.g., data transmission). Is it possible to derive frameworks such that the utility of each sample exceeds its cost?
Frameworks for adaptive sampling: game-theoretic, reinforced learning, dynamic experimental design. How can a sampling scheme to respond to highly non-stationary dynamics in energy-constrained sampling networks. Are modes of operation controlled by adaptive state machines enough?
Environmental modeling from sensor networks:Model complexity and adequacy: In the trade-off of dimensionality and predictive accuracy, what are the diagnostics for excessive vs. insufficient parametrization? Can models be developed so that reduced forms (e.g., deleting submodels, subsets of parameters, or reducing resolution of observations and/or parameter specification) still function simultaneously with near-optimality at several scales?
Prediction Uncertainty: Appropriate modeling of sources of uncertainty due to sampling (e.g., "lost" samples, outliers, bad sensors, measurement noise, and unreliable communication), and integration of data models with process models.
Model adequacy: Is there coherent noise in bio-micrometeorological systems that should drive exploration of new regions of the state space?
Networks, forests and global change:Process level understanding of how forested ecosystems respond to global change is critical for anticipating consequences of human impacts on landscapes. To be successful an approach will entail integrated models of a complex system and will involve heterogeneous data and analyses that directly address uncertainty and model selection issues.
Specific analyses will focus on increasing the capacity to forecast consequences of global change, using existing data from forest sensor networks as both testbed and primary research objective.
Inference on scaling relationships will be implemented in simulation of whole forests to examine potential consequences of changing climate and atmospheric CO2 for forest diversity and carbon sequestration. These results will have immediate application to the problem of forecasting biosphere responses to atmospheric change.
Nuclear smuggling sensor networks upgraded
The U.S. Department of Homeland Security is funding a $1.9 million upgrade of a computer program involving detection of nuclear smuggling.The grant was awarded University of Texas at Austin Professor David Morton and colleagues to expand an existing computer model that guides the placement of sensors to detect nuclear smuggling attempts.The U.S. Department of Homeland Security provided the funds to improve the design of sensor networks in Russia and other former Soviet Union nations that have insufficient security for their stores of nuclear weapons and radioactive material."Russia's got the biggest border of any country on the planet, making it highly unlikely the country could seal its borders," said Morton. "So the real issue becomes: given the limited resources and the fact that radiation detectors can cost upward of $1 million to set up, can we provide a computer tool that locates the detectors optimally?"The United States has provided more than $100 million to place radiation detectors at Russian sites where smugglers could escape with material for preparing nuclear weapons or dirty bombs. The computer model prioritizes decisions on site selection.
Semantic Sensor Networks Workshop
Semantic technologies are often proposed as important components of complex, cross-jurisdictional, heterogeneous, dynamic information systems. The needs and opportunities arising from the rapidly growing capabilities of networked sensing devices are a challenging case.
Current and future sensing systems involve distributed wired and wireless networks consisting of large numbers of sensors, including active and passive RFID tags. Geographically distributed sensor nodes are capable of forming ad hoc networking topologies that interconnect with backend information management systems and services. Sensor nodes are expected to be dynamically inserted and removed from a network due to deployment of new sensor nodes, failure of deployed sensor nodes, and mobility of tagged objects or sensing platforms.
The goal of a sensor networking system is to improve the situational awareness of business activities across widely distributed deployment environments involving a large number of diverse active and passive sensor nodes. Important applications include natural resource management, product lifecycle management, supply chain management and situation awareness on the battlefield.
The goal of the Semantic Sensor Net workshop is to develop an understanding of the ways semantic web technologies, including ontologies, agent architectures and semantic web services can contribute to the growth, application and deployment of large-scale sensor networks. The workshop will provide an inter-disciplinary forum to explore and promote these concepts.
Topics include, but are not limited to:
Ontologies for sensor and RFID networks
Semantic web services architectures for sensor networks
Semantic data integration in large-scale heterogeneous sensor networks
Semantic middleware for active and passive sensor networks
Semantic algorithms for data fusion and situation awareness
Experience in applications of semantic technologies in sensor networks
Rule-based sensor systems
Reasoning with incomplete or uncertain information in sensor networks
Semantic policy management in inter-organisational networks
Semantic feedback and control
Scalability in semantic sensor networks
Sensor network topology management using semantic reasoning
Emergent semantics in sensor network systems
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