Doug Palmer

A deployed multi-agent framework for distributed energy applications

In this paper, we describe the adaptation of an open-source multi-agent platform for distributed energy applications and the trial deployment of resource-controller agents. The platform provides real-time, two-way communication and decision making between distributed energy resources --- loads and generators --- in electricity distribution networks. Such a decentralized architecture improves grid reliability, allows consumers to play a more active role in their energy usage, benefits the network by alleviating the effects of peak wholesale prices and network constraints, and creates new business opportunities in a deregulated market. Agents have been deployed to control hardware at trial locations in Australia, providing a realistic test environment for the platform, and medium-scale trials are anticipated in the near future.

Annotating with light for remote guidance

This paper presents a system that will support a remote guidance collaboration, in which a local expert guides a remotely located assistant to perform physical, three-dimensional tasks. The system supports this remote guidance by allowing the expert to annotate, point at and draw upon objects in the remote location using a pen and tablet-based interface to control a laser projection device. The specific design criteria for this system are drawn from a tele-health scenario involving remote medical examination of patients and the paper presents the software architecture and implementation details of the associated hardware. In particular, the algorithm for aligning the representation of the laser projection over the video display of the remote scene is described. Early evaluations by medical specialists are presented, the usability of the system in laboratory experiments is discussed and ideas for future developments are outlined.

Do-it-Yourself Digital Agriculture applications with semantically enhanced IoT platform

Internet of Things (IoT) enables various applications (crop growth monitoring and selection, irrigation decision support, etc) in Digital Agriculture domain. Semantic enhancements to IoT platforms address challenges of interoperability, data fusion, integration of heterogeneous IoT silos, annotation of data streams, just to name a few. This paper discusses the recently developed OpenIoT platform which demonstrated its applicability and efficiency in a number of use cases, including a Digital Agriculture use case (Phenonet). An ontology to represent Phenonet domain concepts in order to facilitate smart collection, annotation, validation, processing and storing of data streams from sensors in the field has been proposed and the results of experimental study, related semantic queries and reasoning using the ontology are presented. A Do-It-Yourself principle-driven zero-programming enabling Phenonet user interface demonstrates benefits, novelty and efficiency of the approach.

SensorDB: a virtual laboratory for the integration, visualization and analysis of varied biological sensor data.

BackgroundTo our knowledge, there is no software or database solution that supports large volumes of biological time series sensor data efficiently and enables data visualization and analysis in real time. Existing solutions for managing data typically use unstructured file systems or relational databases. These systems are not designed to provide instantaneous response to user queries. Furthermore, they do not support rapid data analysis and visualization to enable interactive experiments. In large scale experiments, this behaviour slows research discovery, discourages the widespread sharing and reuse of data that could otherwise inform critical decisions in a timely manner and encourage effective collaboration between groups.ResultsIn this paper we present SensorDB, a web based virtual laboratory that can manage large volumes of biological time series sensor data while supporting rapid data queries and real-time user interaction. SensorDB is sensor agnostic and uses web-based, state-of-the-art cloud and storage technologies to efficiently gather, analyse and visualize data.Conclusions Collaboration and data sharing between different agencies and groups is thereby facilitated. SensorDB is available online at http://sensordb.csiro.au.

Automated Analysis of Java Message Service Providers

The Java Message Service (JMS) is a specification that provides a consistent Java API for accessing message-oriented middleware services. This paper presents a test harness that automates the testing of JMS implementations (providers) for correctness and performance. Since the JMS specification is expressed in informal language, a formal model for JMS behaviour is developed, based on the I/O automata used in other group communication systems. The test harness has been successfully used to test a number of JMS implementations. This paper contains a descriptive presentation of the formal model, the full details are found in a technical report

ElectricCow: a simulator for mobile sensors and actuators mounted on herds of cattle

ElectricCOW is a network, animal behaviour and agent simulator designed to allow detailed simulation of an ad-hoc model network built from small mote-like devices called flecks. Detailed radio communications, cattle behaviour and sensor and actuator network modelling allows a closed-loop environment, where the network can influence the behaviour of its mobile platforms.

Addressing Information Processing Needs of Digital Agriculture with OpenIoT Platform

Food security is a global challenge and agriculture can address this challenge through radical improvements in productivity, efficiency and effectiveness. Internet of Things (IoT) is a major enabler of such improvements. This paper discusses challenges that agricultural industry is facing and proposes a solution based on IoT technology and a specific platform called OpenIoT developed jointly by the EU FP7 OpenIoT consortium. Phenonet is an OpenIoT use case developed by CSIRO, Australia and demonstrates how digital agriculture benefits from deploying the IoT. Experience and lessons from using OpenIoT middleware for Phenonet development are also presented and analysed.

SensorFeed: An architecture for model-based sensor network data enrichment

Perceiving the environment, a sensor network collects huge volume of data to be used in different application domains. The gathered data is often used and analyzed by e-research scientists other than the original investigator. Therefore, the sensed data needs to be captured, processed and stored in a form that will allow someone to use the data with confidence long after the original investigators have left the scene. To address this need, in this paper we present SensorFeed, which integrates metadata repositories and sensor data management systems. Using SensorFeed, scientists can annotate sensor readings automatically as they are streamed, through direct use of statistical modeling frameworks. These annotations enrich sensor readings, thus making datasets generated from sensor network deployments usable by external scientists. A real-world use case of SensorFeed for agriculture engineering is presented to show the applicability of our approach to an e-research application.