Poster abstract : Energy-efficient rate adaptive MAC Protocol (RA-MAC) for long-lived sensor networks

This paper introduces an energy-efficient Rate Adaptive MAC (RA-MAC) protocol for long-lived Wireless Sensor Networks (WSN). Previous research shows that the dynamic and lossy nature of wireless communication is one of the major challenges to reliable data delivery in a WSN. RA-MAC achieves high link reliability in such situations by dynamically trading off radio bit rate for signal processing gain. This extra gain reduces the packet loss rate which results in lower energy expenditure by reducing the number of retransmissions. RA-MAC selects the optimal data rate based on channel conditions with the aim of minimizing energy consumption. We have implemented RA-MAC in TinyOS on an off-the-shelf sensor platform (TinyNode), and evaluated its performance by comparing RA-MAC with state-ofthe- art WSN MAC protocol (SCP-MAC) by experiments.

Connecting people and resource consumption in real time

The authors currently engage in two projects to improve human-computer interaction (HCI) designs that can help conserve resources. The projects explore motivation and persuasion strategies relevant to ubiquitous computing systems that bring real-time consumption data into the homes and hands of residents in Brisbane, Australia. The first project seeks to increase understanding among university staff of the tangible and negative effects that excessive printing has on the workplace and local environment. The second project seeks to shift attitudes toward domestic energy conservation through software and hardware that monitor real-time, in situ electricity consumption in homes across Queensland. The insights drawn from these projects will help develop resource consumption user archetypes, providing a framework linking people to differing interface design requirements.

Alkaline hydrothermal treatment of titanate nanostructures

Since its initial proposal in 1998, alkaline hydrothermal processing has rapidly become an established technology for the production of titanate nanostructures. This simple, highly reproducible process has gained a strong research following since its conception. However, complete understanding and elucidation of nanostructure phase and formation have not yet been achieved. Without fully understanding phase, formation, and other important competing effects of the synthesis parameters on the final structure, the maximum potential of these nanostructures cannot be obtained. Therefore this study examined the influence of synthesis parameters on the formation of titanate nanostructures produced by alkaline hydrothermal treatment. The parameters included alkaline concentration, hydrothermal temperature, the precursor material‘s crystallite size and also the phase of the titanium dioxide precursor (TiO2, or titania). The nanostructure‘s phase and morphology was analysed using X-ray diffraction (XRD), Raman spectroscopy and transmission electron microscopy. X-ray photoelectron spectroscopy (XPS), dynamic light scattering (non-invasive backscattering), nitrogen sorption, and Rietveld analysis were used to determine phase, for particle sizing, surface area determinations, and establishing phase concentrations, respectively. This project rigorously examined the effect of alkaline concentration and hydrothermal temperature on three commercially sourced and two self-prepared TiO2 powders. These precursors consisted of both pure- or mixed-phase anatase and rutile polymorphs, and were selected to cover a range of phase concentrations and crystallite sizes. Typically, these precursors were treated with 5–10 M sodium hydroxide (NaOH) solutions at temperatures between 100–220 °C. Both nanotube and nanoribbon morphologies could be produced depending on the combination of these hydrothermal conditions. Both titania and titanate phases are comprised of TiO6 units which are assembled in different combinations. The arrangement of these atoms affects the binding energy between the Ti–O bonds. Raman spectroscopy and XPS were therefore employed in a preliminary study of phase determination for these materials. The change in binding energy from a titania to a titanate binding energy was investigated in this study, and the transformation of titania precursor into nanotubes and titanate nanoribbons was directly observed by these methods. Evaluation of the Raman and XPS results indicated a strengthening in the binding energies of both the Ti (2p3/2) and O (1s) bands which correlated to an increase in strength and decrease in resolution of the characteristic nanotube doublet observed between 320 and 220 cm.1 in the Raman spectra of these products. The effect of phase and crystallite size on nanotube formation was examined over a series of temperatures (100.200 �‹C in 20 �‹C increments) at a set alkaline concentration (7.5 M NaOH). These parameters were investigated by employing both pure- and mixed- phase precursors of anatase and rutile. This study indicated that both the crystallite size and phase affect nanotube formation, with rutile requiring a greater driving force (essentially �\harsher. hydrothermal conditions) than anatase to form nanotubes, where larger crystallites forms of the precursor also appeared to impede nanotube formation slightly. These parameters were further examined in later studies. The influence of alkaline concentration and hydrothermal temperature were systematically examined for the transformation of Degussa P25 into nanotubes and nanoribbons, and exact conditions for nanostructure synthesis were determined. Correlation of these data sets resulted in the construction of a morphological phase diagram, which is an effective reference for nanostructure formation. This morphological phase diagram effectively provides a .recipe book�e for the formation of titanate nanostructures. Morphological phase diagrams were also constructed for larger, near phase-pure anatase and rutile precursors, to further investigate the influence of hydrothermal reaction parameters on the formation of titanate nanotubes and nanoribbons. The effects of alkaline concentration, hydrothermal temperature, crystallite phase and size are observed when the three morphological phase diagrams are compared. Through the analysis of these results it was determined that alkaline concentration and hydrothermal temperature affect nanotube and nanoribbon formation independently through a complex relationship, where nanotubes are primarily affected by temperature, whilst nanoribbons are strongly influenced by alkaline concentration. Crystallite size and phase also affected the nanostructure formation. Smaller precursor crystallites formed nanostructures at reduced hydrothermal temperature, and rutile displayed a slower rate of precursor consumption compared to anatase, with incomplete conversion observed for most hydrothermal conditions. The incomplete conversion of rutile into nanotubes was examined in detail in the final study. This study selectively examined the kinetics of precursor dissolution in order to understand why rutile incompletely converted. This was achieved by selecting a single hydrothermal condition (9 M NaOH, 160 °C) where nanotubes are known to form from both anatase and rutile, where the synthesis was quenched after 2, 4, 8, 16 and 32 hours. The influence of precursor phase on nanostructure formation was explicitly determined to be due to different dissolution kinetics; where anatase exhibited zero-order dissolution and rutile second-order. This difference in kinetic order cannot be simply explained by the variation in crystallite size, as the inherent surface areas of the two precursors were determined to have first-order relationships with time. Therefore, the crystallite size (and inherent surface area) does not affect the overall kinetic order of dissolution; rather, it determines the rate of reaction. Finally, nanostructure formation was found to be controlled by the availability of dissolved titanium (Ti4+) species in solution, which is mediated by the dissolution kinetics of the precursor.

Adaptive GPS duty cycling and radio ranging for energy-efficient localization

This paper addresses the tradeoff between energy consumption and localization performance in a mobile sensor network application. The focus is on augmenting GPS location with more energy-efficient location sensors to bound position estimate uncertainty in order to prolong node lifetime. We use empirical GPS and radio contact data from a largescale animal tracking deployment to model node mobility, GPS and radio performance. These models are used to explore duty cycling strategies for maintaining position uncertainty within specified bounds. We then explore the benefits of using short-range radio contact logging alongside GPS as an energy-inexpensive means of lowering uncertainty while the GPS is off, and we propose a versatile contact logging strategy that relies on RSSI ranging and GPS lock back-offs for reducing the node energy consumption relative to GPS duty cycling. Results show that our strategy can cut the node energy consumption by half while meeting application specific positioning criteria.

Energy-oriented design tools for collaboration in the cloud

Emerging from the challenge to reduce energy consumption in buildings is the need for energy simulation to be used more effectively to support integrated decision making in early design. As a critical response to a Green Star case study, we present DEEPA, a parametric modeling framework that enables architects and engineers to work at the same semantic level to generate shared models for energy simulation. A cloud-based toolkit provides web and data services for parametric design software that automate the process of simulating and tracking design alternatives, by linking building geometry more directly to analysis inputs. Data, semantics, models and simulation results can be shared on the fly. This allows the complex relationships between architecture, building services and energy consumption to be explored in an integrated manner, and decisions to be made collaboratively.

Estimation of energy saving of commercial building by living wall and green facade in sub-tropical climate of Australia

This paper investigates energy saving potential of commercial building by living wall and green façade system using Envelope Thermal Transfer Value (ETTV) equation in Sub-tropical climate of Australia. Energy saving of four commercial buildings was quantified by applying living wall and green façade system to the west facing wall. A field experimental facility, from which temperature data of living wall system was collected, was used to quantify wall temperatures and heat gain under controlled conditions. The experimental parameters were accumulated with extensive data of existing commercial building to quantify energy saving. Based on temperature data of living wall system comprised of Australian native plants, equivalent temperature of living wall system has been computed. Then, shading coefficient of plants in green façade system has been included in mathematical equation and in graphical analysis. To minimize the air-conditioned load of commercial building, therefore to minimize the heat gain of commercial building, an analysis of building heat gain reduction by living wall and green façade system has been performed. Overall, cooling energy performance of commercial building before and after living wall and green façade system application has been examined. The quantified energy saving showed that only living wall system on opaque part of west facing wall can save 8-13 % of cooling energy consumption where as only green façade system on opaque part of west facing wall can save 9.5-18% cooling energy consumption of commercial building. Again, green façade system on fenestration system on west facing wall can save 28-35 % of cooling energy consumption where as combination of both living wall on opaque part of west facing wall and green façade on fenestration system on west facing wall can save 35-40% cooling energy consumption of commercial building in sub-tropical climate of Australia.

Investigation of cooling energy performance of commercial building in sub-tropical climate through the application of green roof and living wall

This paper investigates cooling energy performance of commercial building before and after green roof and living wall application based on integrated building heat gain model developed from Overall Thermal Transfer Value (OTTV) of building wall and steady state heat transfer process of roof in sub-tropical climate. Using the modelled equation and eQUEST energy simulation tool, commercial building envelope parameters and relevant heat gain parameters have been accumulated to analyse the heat gain and cooling energy consumption of commercial building. Real life commercial building envelope and air-conditioned load data for the sub-tropical climate zone have been collected and compared with the modelled analysis. Relevant temperature data required for living wall and green roof analysis have been collected from experimental setup comprised of both green roof and west facing living wall. Then, Commercial building heat flux and cooling energy performance before and after green roof and living wall application have been scrutinized.

Energy-efficient localization : GPS duty cycling with radio ranging

GPS is a commonly used and convenient technology for determining absolute position in outdoor environments, but its high power consumption leads to rapid battery depletion in mobile devices. An obvious solution is to duty cycle the GPS module, which prolongs the device lifetime at the cost of increased position uncertainty while the GPS is off. This article addresses the trade-off between energy consumption and localization performance in a mobile sensor network application. The focus is on augmenting GPS location with more energy-efficient location sensors to bound position estimate uncertainty while GPS is off. Empirical GPS and radio contact data from a large-scale animal tracking deployment is used to model node mobility, radio performance, and GPS. Because GPS takes a considerable, and variable, time after powering up before it delivers a good position measurement, we model the GPS behaviour through empirical measurements of two GPS modules. These models are then used to explore duty cycling strategies for maintaining position uncertainty within specified bounds. We then explore the benefits of using short-range radio contact logging alongside GPS as an energy-inexpensive means of lowering uncertainty while the GPS is off, and we propose strategies that use RSSI ranging and GPS back-offs to further reduce energy consumption. Results show that our combined strategies can cut node energy consumption by one third while still meeting application-specific positioning criteria.

Developing a dashboard for real-time data stream composition and visualization

Technological advances have led to an influx of affordable hardware that supports sensing, computation and communication. This hardware is increasingly deployed in public and private spaces, tracking and aggregating a wealth of real-time environmental data. Although these technologies are the focus of several research areas, there is a lack of research dealing with the problem of making these capabilities accessible to everyday users. This thesis represents a first step towards developing systems that will allow users to leverage the available infrastructure and create custom tailored solutions. It explores how this notion can be utilized in the context of energy monitoring to improve conventional approaches. The project adopted a user-centered design process to inform the development of a flexible system for real-time data stream composition and visualization. This system features an extensible architecture and defines a unified API for heterogeneous data streams. Rather than displaying the data in a predetermined fashion, it makes this information available as building blocks that can be combined and shared. It is based on the insight that individual users have diverse information needs and presentation preferences. Therefore, it allows users to compose rich information displays, incorporating personally relevant data from an extensive information ecosystem. The prototype was evaluated in an exploratory study to observe its natural use in a real-world setting, gathering empirical usage statistics and conducting semi-structured interviews. The results show that a high degree of customization does not warrant sustained usage. Other factors were identified, yielding recommendations for increasing the impact on energy consumption.

HeatWave : a handheld 3D thermography system for energy auditing

Energy auditing is an effective but costly approach for reducing the long-term energy consumption of buildings. When well-executed, energy loss can be quickly identified in the building structure and its subsystems. This then presents opportunities for improving energy efficiency. We present a low-cost, portable technology called "HeatWave" which allows non-experts to generate detailed 3D surface temperature models for energy auditing. This handheld 3D thermography system consists of two commercially available imaging sensors and a set of software algorithms which can be run on a laptop. The 3D model can be visualized in real-time by the operator so that they can monitor their degree of coverage as the sensors are used to capture data. In addition, results can be analyzed offline using the proposed "Spectra" multispectral visualization toolbox. The presence of surface temperature data in the generated 3D model enables the operator to easily identify and measure thermal irregularities such as thermal bridges, insulation leaks, moisture build-up and HVAC faults. Moreover, 3D models generated from subsequent audits of the same environment can be automatically compared to detect temporal changes in conditions and energy use over time.

Development of gas sensing technology for ground and airborne applications powered by solar energy : methodology and experimental results

Monitoring gases for environmental, industrial and agricultural fields is a demanding task that requires long periods of observation, large quantity of sensors, data management, high temporal and spatial resolution, long term stability, recalibration procedures, computational resources, and energy availability. Wireless Sensor Networks (WSNs) and Unmanned Aerial Vehicles (UAVs) are currently representing the best alternative to monitor large, remote, and difficult access areas, as these technologies have the possibility of carrying specialised gas sensing systems, and offer the possibility of geo-located and time stamp samples. However, these technologies are not fully functional for scientific and commercial applications as their development and availability is limited by a number of factors: the cost of sensors required to cover large areas, their stability over long periods, their power consumption, and the weight of the system to be used on small UAVs. Energy availability is a serious challenge when WSN are deployed in remote areas with difficult access to the grid, while small UAVs are limited by the energy in their reservoir tank or batteries. Another important challenge is the management of data produced by the sensor nodes, requiring large amount of resources to be stored, analysed and displayed after long periods of operation. In response to these challenges, this research proposes the following solutions aiming to improve the availability and development of these technologies for gas sensing monitoring: first, the integration of WSNs and UAVs for environmental gas sensing in order to monitor large volumes at ground and aerial levels with a minimum of sensor nodes for an effective 3D monitoring; second, the use of solar energy as a main power source to allow continuous monitoring; and lastly, the creation of a data management platform to store, analyse and share the information with operators and external users. The principal outcomes of this research are the creation of a gas sensing system suitable for monitoring any kind of gas, which has been installed and tested on CH4 and CO2 in a sensor network (WSN) and on a UAV. The use of the same gas sensing system in a WSN and a UAV reduces significantly the complexity and cost of the application as it allows: a) the standardisation of the signal acquisition and data processing, thereby reducing the required computational resources; b) the standardisation of calibration and operational procedures, reducing systematic errors and complexity; c) the reduction of the weight and energy consumption, leading to an improved power management and weight balance in the case of UAVs; d) the simplification of the sensor node architecture, which is easily replicated in all the nodes. I evaluated two different sensor modules by laboratory, bench, and field tests: a non-dispersive infrared module (NDIR) and a metal-oxide resistive nano-sensor module (MOX nano-sensor). The tests revealed advantages and disadvantages of the two modules when used for static nodes at the ground level and mobile nodes on-board a UAV. Commercial NDIR modules for CO2 have been successfully tested and evaluated in the WSN and on board of the UAV. Their advantage is the precision and stability, but their application is limited to a few gases. The advantages of the MOX nano-sensors are the small size, low weight, low power consumption and their sensitivity to a broad range of gases. However, selectivity is still a concern that needs to be addressed with further studies. An electronic board to interface sensors in a large range of resistivity was successfully designed, created and adapted to operate on ground nodes and on-board UAV. The WSN and UAV created were powered with solar energy in order to facilitate outdoor deployment, data collection and continuous monitoring over large and remote volumes. The gas sensing, solar power, transmission and data management systems of the WSN and UAV were fully evaluated by laboratory, bench and field testing. The methodology created to design, developed, integrate and test these systems was extensively described and experimentally validated. The sampling and transmission capabilities of the WSN and UAV were successfully tested in an emulated mission involving the detection and measurement of CO2 concentrations in a field coming from a contaminant source; the data collected during the mission was transmitted in real time to a central node for data analysis and 3D mapping of the target gas. The major outcome of this research is the accomplishment of the first flight mission, never reported before in the literature, of a solar powered UAV equipped with a CO2 sensing system in conjunction with a network of ground sensor nodes for an effective 3D monitoring of the target gas. A data management platform was created using an external internet server, which manages, stores, and shares the data collected in two web pages, showing statistics and static graph images for internal and external users as requested. The system was bench tested with real data produced by the sensor nodes and the architecture of the platform was widely described and illustrated in order to provide guidance and support on how to replicate the system. In conclusion, the overall results of the project provide guidance on how to create a gas sensing system integrating WSNs and UAVs, how to power the system with solar energy and manage the data produced by the sensor nodes. This system can be used in a wide range of outdoor applications, especially in agriculture, bushfires, mining studies, zoology, and botanical studies opening the way to an ubiquitous low cost environmental monitoring, which may help to decrease our carbon footprint and to improve the health of the planet.

Profiling : an application assignment approach for green data centers

In the past few years, there has been a steady increase in the attention, importance and focus of green initiatives related to data centers. While various energy aware measures have been developed for data centers, the requirement of improving the performance efficiency of application assignment at the same time has yet to be fulfilled. For instance, many energy aware measures applied to data centers maintain a trade-off between energy consumption and Quality of Service (QoS). To address this problem, this paper presents a novel concept of profiling to facilitate offline optimization for a deterministic application assignment to virtual machines. Then, a profile-based model is established for obtaining near-optimal allocations of applications to virtual machines with consideration of three major objectives: energy cost, CPU utilization efficiency and application completion time. From this model, a profile-based and scalable matching algorithm is developed to solve the profile-based model. The assignment efficiency of our algorithm is then compared with that of the Hungarian algorithm, which does not scale well though giving the optimal solution.

Data as an asset: what the oil and gas sector can learn from other industries about “Big Data”

The upstream oil and gas industry has been contending with massive data sets and monolithic files for many years, but “Big Data” is a relatively new concept that has the potential to significantly re-shape the industry. Despite the impressive amount of value that is being realized by Big Data technologies in other parts of the marketplace, however, much of the data collected within the oil and gas sector tends to be discarded, ignored, or analyzed in a very cursory way. This viewpoint examines existing data management practices in the upstream oil and gas industry, and compares them to practices and philosophies that have emerged in organizations that are leading the way in Big Data. The comparison shows that, in companies that are widely considered to be leaders in Big Data analytics, data is regarded as a valuable asset—but this is usually not true within the oil and gas industry insofar as data is frequently regarded there as descriptive information about a physical asset rather than something that is valuable in and of itself. The paper then discusses how the industry could potentially extract more value from data, and concludes with a series of policy-related questions to this end.

Using genetic algorithm in profile-based assignment of applications to virtual machines for greener data centers

The increase in data center dependent services has made energy optimization of data centers one of the most exigent challenges in today's Information Age. The necessity of green and energy-efficient measures is very high for reducing carbon footprint and exorbitant energy costs. However, inefficient application management of data centers results in high energy consumption and low resource utilization efficiency. Unfortunately, in most cases, deploying an energy-efficient application management solution inevitably degrades the resource utilization efficiency of the data centers. To address this problem, a Penalty-based Genetic Algorithm (GA) is presented in this paper to solve a defined profile-based application assignment problem whilst maintaining a trade-off between the power consumption performance and resource utilization performance. Case studies show that the penalty-based GA is highly scalable and provides 16% to 32% better solutions than a greedy algorithm.

A penalty-based genetic algorithm for the migration cost-aware virtual machine placement problem in cloud data centers

In the past few years, the virtual machine (VM) placement problem has been studied intensively and many algorithms for the VM placement problem have been proposed. However, those proposed VM placement algorithms have not been widely used in today's cloud data centers as they do not consider the migration cost from current VM placement to the new optimal VM placement. As a result, the gain from optimizing VM placement may be less than the loss of the migration cost from current VM placement to the new VM placement. To address this issue, this paper presents a penalty-based genetic algorithm (GA) for the VM placement problem that considers the migration cost in addition to the energy-consumption of the new VM placement and the total inter-VM traffic flow in the new VM placement. The GA has been implemented and evaluated by experiments, and the experimental results show that the GA outperforms two well known algorithms for the VM placement problem.