Animal behaviour understanding using wireless sensor networks

This paper presents research that is being conducted by the Commonwealth Scientific and Industrial Research Organisation (CSIRO) with the aim of investigating the use of wireless sensor networks for automated livestock monitoring and control. It is difficult to achieve practical and reliable cattle monitoring with current conventional technologies due to challenges such as large grazing areas of cattle, long time periods of data sampling, and constantly varying physical environments. Wireless sensor networks bring a new level of possibilities into this area with the potential for greatly increased spatial and temporal resolution of measurement data. CSIRO has created a wireless sensor platform for animal behaviour monitoring where we are able to observe and collect information of animals without significantly interfering with them. Based on such monitoring information, we can identify each animal's behaviour and activities successfully

Wireless adhoc sensor and actuator networks on the farm

Agriculture accounts for a significant portion of the GDP in most developed countries. However, managing farms, particularly largescale extensive farming systems, is hindered by lack of data and increasing shortage of labour. We have deployed a large heterogeneous sensor network on a working farm to explore sensor network applications that can address some of the issues identified above. Our network is solar powered and has been running for over 6 months. The current deployment consists of over 40 moisture sensors that provide soil moisture profiles at varying depths, weight sensors to compute the amount of food and water consumed by animals, electronic tag readers, up to 40 sensors that can be used to track animal movement (consisting of GPS, compass and accelerometers), and 20 sensor/actuators that can be used to apply different stimuli (audio, vibration and mild electric shock) to the animal. The static part of the network is designed for 24/7 operation and is linked to the Internet via a dedicated high-gain radio link, also solar powered. The initial goals of the deployment are to provide a testbed for sensor network research in programmability and data handling while also being a vital tool for scientists to study animal behavior. Our longer term aim is to create a management system that completely transforms the way farms are managed.

Wireless sensor devices for animal tracking and control

This paper describes some new wireless sensor hardware developed for pastoral and environmental applications. From our early experiments with Mote hardware we were inspired to develop our devices with improved radio range, solar power capability, mechanical and electrical robustness, and with unique combinations of sensors. Here we describe the design and evolution of a small family of devices: radio/processor board, a soil moisture sensor interface, and a single board multi-sensor unit for animal tracking experiments.

Coordinating aerial robots and sensor networks for localization and navigation

We consider multi-robot systems that include sensor nodes and aerial or ground robots networked together. We describe two cooperative algorithms that allow robots and sensors to enhance each other's performance. In the first algorithm, an aerial robot assists the localization of the sensors. In the second algorithm, a localized sensor network controls the navigation of an aerial robot. We present physical experiments with an flying robot and a large Mica Mote sensor network.

Networked robots : flying robot navigation using a sensor net

This paper introduces the application of a sensor network to navigate a flying robot. We have developed distributed algorithms and efficient geographic routing techniques to incrementally guide one or more robots to points of interest based on sensor gradient fields, or along paths defined in terms of Cartesian coordinates. The robot itself is an integral part of the localization process which establishes the positions of sensors which are not known a priori. We use this system in a large-scale outdoor experiment with Mote sensors to guide an autonomous helicopter along a path encoded in the network. A simple handheld device, using this same environmental infrastructure, is used to guide humans.

Validity of the Waterlow screening tool and risks for pressure injury in acute care

Objectives: To assess the validity of the Waterlow screening tool in a cohort of internal medicine patients and to identify factors contributing to pressure injury. Design: Longitudinal cohort study Setting: A tertiary hospital in Brisbane, Australia Participants: 274 patients admitted through the Emergency Department or outpatient clinics and expected to remain in hospital for at least three days were included in the study. The mean age was 65.3 years. Interventions: Patients were screened on admission using the Waterlow screening tool. Every second day, their pressure ulcer status was monitored and recorded. Main outcome measures: Pressure ulcer incidence Results: Fifteen participants (5.5%) had an existing pressure ulcer and a further 12 (4.4%) developed a pressure ulcer during their hospital stay. Sensitivity of the Waterlow scale was 0.67, (95% CI: 0.35 to 0.88); specificity 0.79, (95% CI: 0.73 to 0.85); PPV 0.13, (95% CI: 0.07 to 0.24); NPV 0.98, (95% CI: 0.94 to 0.99). Conclusion: This study provides further evidence of the poor predictive validity of the Waterlow scale. A suitably powered randomised controlled trial is urgently needed to provide definitive evidence about the usefulness of the Waterlow scale compared with other screening tools and with clinical judgement.

Robotics and Sensor Networks

Robotics in mines, aerospace, underwater, everyday unstructured environments and sensor networks with communicating devices that collect data.

On the feasibility of using servo-mechanisms in wireless multimedia sensor network deployments

This paper considers the use of servo-mechanisms as part of a tightly integrated homogeneous Wireless Multi- media Sensor Network (WMSN). We describe the design of our second generation WMSN node platform, which has increased image resolution, in-built audio sensors, PIR sensors, and servo- mechanisms. These devices have a wide disparity in their energy consumption and in the information quality they return. As a result, we propose a framework that establishes a hierarchy of devices (sensors and actuators) within the node and uses frequent sampling of cheaper devices to trigger the activation of more energy-hungry devices. Within this framework, we consider the suitability of servos for WMSNs by examining the functional characteristics and by measuring the energy consumption of 2 analog and 2 digital servos, in order to determine their impact on overall node energy cost. We also implement a simple version of our hierarchical sampling framework to evaluate the energy consumption of servos relative to other node components. The evaluation results show that: (1) the energy consumption of servos is small relative to audio/image signal processing energy cost in WMSN nodes; (2) digital servos do not necessarily consume as much energy as is currently believed; and (3) the energy cost per degree panning is lower for larger panning angles.

Statistical modeling to determine resonant frequency information present in combustion chamber pressure signals

A statistical modeling method to accurately determine combustion chamber resonance is proposed and demonstrated. This method utilises Markov-chain Monte Carlo (MCMC) through the use of the Metropolis-Hastings (MH) algorithm to yield a probability density function for the combustion chamber frequency and find the best estimate of the resonant frequency, along with uncertainty. The accurate determination of combustion chamber resonance is then used to investigate various engine phenomena, with appropriate uncertainty, for a range of engine cycles. It is shown that, when operating on various ethanol/diesel fuel combinations, a 20% substitution yields the least amount of inter-cycle variability, in relation to combustion chamber resonance.

A fiber Bragg grating sensor system for train axle counting

Railway signaling facilitates two main functions, namely, train detection and train control, in order to maintain safe separations among the trains. Track circuits are the most commonly used train detection means with the simple open/close circuit principles; and subsequent adoption of axle counters further allows the detection of trains under adverse track conditions. However, with electrification and power electronics traction drive systems, aggravated by the electromagnetic interference in the vicinity of the signaling system, railway engineers often find unstable or even faulty operations of track circuits and axle counting systems, which inevitably jeopardizes the safe operation of trains. A new means of train detection, which is completely free from electromagnetic interference, is therefore required for the modern railway signaling system. This paper presents a novel optical fiber sensor signaling system. The sensor operation, field setup, axle detection solution set, and test results of an installation in a trial system on a busy suburban railway line are given.

Camera calibration in wireless multimedia sensor networks

Wireless Multi-media Sensor Networks (WMSNs) have become increasingly popular in recent years, driven in part by the increasing commoditization of small, low-cost CMOS sensors. As such, the challenge of automatically calibrating these types of cameras nodes has become an important research problem, especially for the case when a large quantity of these type of devices are deployed. This paper presents a method for automatically calibrating a wireless camera node with the ability to rotate around one axis. The method involves capturing images as the camera is rotated and computing the homographies between the images. The camera parameters, including focal length, principal point and the angle and axis of rotation can then recovered from two or more homographies. The homography computation algorithm is designed to deal with the limited resources of the wireless sensor and to minimize energy con- sumption. In this paper, a modified RANdom SAmple Consensus (RANSAC) algorithm is proposed to effectively increase the efficiency and reliability of the calibration procedure.

Pressure filtration of Australian bagasse pulp

A one-dimensional pressure filtration model that can be used to predict the behaviour of bagasse pulp has been developed and verified in this study.The dynamic filtration model uses steady state compressibility parameters determined experimentally by uniaxial loading. The compressibility parameters M and N for depithed bagasse pulp were determined to be in the ranges 3000–8000kPa and 2.5–3.0 units, respectively. The model also incorporates experimentally determined steady state permeability data from separate experiments to predict the pulp concentration and fibre pressure throughout a pulp mat during dynamic filtration. Under steady state conditions, a variable Kozeny factor required different values for the permeability parameters when compared to a constant Kozeny factor. The specific surface area was 25–30% lower and the swelling factor was 20–25% higher when a variable Kozeny factor was used. Excellent agreement between experimental data and the dynamic filtration model was achieved when a variable Kozeny factor was used.

Design and implementation of sensor networks for the observation and research of harmful algal blooms in southern California coastal waters

Harmful Algal Blooms (HABs) have become an important environmental concern along the western coast of the United States. Toxic and noxious blooms adversely impact the economies of coastal communities in the region, pose risks to human health, and cause mortality events that have resulted in the deaths of thousands of fish, marine mammals and seabirds. One goal of field-based research efforts on this topic is the development of predictive models of HABs that would enable rapid response, mitigation and ultimately prevention of these events. In turn, these objectives are predicated on understanding the environmental conditions that stimulate these transient phenomena. An embedded sensor network (Fig. 1), under development in the San Pedro Shelf region off the Southern California coast, is providing tools for acquiring chemical, physical and biological data at high temporal and spatial resolution to help document the emergence and persistence of HAB events, supporting the design and testing of predictive models, and providing contextual information for experimental studies designed to reveal the environmental conditions promoting HABs. The sensor platforms contained within this network include pier-based sensor arrays, ocean moorings, HF radar stations, along with mobile sensor nodes in the form of surface and subsurface autonomous vehicles. FreewaveTM radio modems facilitate network communication and form a minimally-intrusive, wireless communication infrastructure throughout the Southern California coastal region, allowing rapid and cost-effective data transfer. An emerging focus of this project is the incorporation of a predictive ocean model that assimilates near-real time, in situ data from deployed Autonomous Underwater Vehicles (AUVs). The model then assimilates the data to increase the skill of both nowcasts and forecasts, thus providing insight into bloom initiation as well as the movement of blooms or other oceanic features of interest (e.g., thermoclines, fronts, river discharge, etc.). From these predictions, deployed mobile sensors can be tasked to track a designated feature. This focus has led to the creation of a technology chain in which algorithms are being implemented for the innovative trajectory design for AUVs. Such intelligent mission planning is required to maneuver a vehicle to precise depths and locations that are the sites of active blooms, or physical/chemical features that might be sources of bloom initiation or persistence. The embedded network yields high-resolution, temporal and spatial measurements of pertinent environmental parameters and resulting biology (see Fig. 1). Supplementing this with ocean current information and remotely sensed imagery and meteorological data, we obtain a comprehensive foundation for developing a fundamental understanding of HAB events. This then directs labor- intensive and costly sampling efforts and analyses. Additionally, we provide coastal municipalities, managers and state agencies with detailed information to aid their efforts in providing responsible environmental stewardship of their coastal waters.

Implementation of an embedded sensor network for the coordination of Slocum gliders for coastal monitoring and observation

Autonomous Underwater Vehicles (AUVs) are revolutionizing oceanography through their versatility, autonomy and endurance. However, they are still an underutilized technology. For coastal operations, the ability to track a certain feature is of interest to ocean scientists. Adaptive and predictive path planning requires frequent communication with significant data transfer. Currently, most AUVs rely on satellite phones as their primary communication. This communication protocol is expensive and slow. To reduce communication costs and provide adequate data transfer rates, we present a hardware modification along with a software system that provides an alternative robust disruption- tolerant communications framework enabling cost-effective glider operation in coastal regions. The framework is specifically designed to address multi-sensor deployments. We provide a system overview and present testing and coverage data for the network. Additionally, we include an application of ocean-model driven trajectory design, which can benefit from the use of this network and communication system. Simulation and implementation results are presented for single and multiple vehicle deployments. The presented combination of infrastructure, software development and deployment experience brings us closer to the goal of providing a reliable and cost-effective data transfer framework to enable real-time, optimal trajectory design, based on ocean model predictions, to gather in situ measurements of interesting and evolving ocean features and phenomena.