Real-time traffic state estimation in urban corridors from heterogeneous data

In recent years, rapid advances in information technology have led to various data collection systems which are enriching the sources of empirical data for use in transport systems. Currently, traffic data are collected through various sensors including loop detectors, probe vehicles, cell-phones, Bluetooth, video cameras, remote sensing and public transport smart cards. It has been argued that combining the complementary information from multiple sources will generally result in better accuracy, increased robustness and reduced ambiguity. Despite the fact that there have been substantial advances in data assimilation techniques to reconstruct and predict the traffic state from multiple data sources, such methods are generally data-driven and do not fully utilize the power of traffic models. Furthermore, the existing methods are still limited to freeway networks and are not yet applicable in the urban context due to the enhanced complexity of the flow behavior. The main traffic phenomena on urban links are generally caused by the boundary conditions at intersections, un-signalized or signalized, at which the switching of the traffic lights and the turning maneuvers of the road users lead to shock-wave phenomena that propagate upstream of the intersections. This paper develops a new model-based methodology to build up a real-time traffic prediction model for arterial corridors using data from multiple sources, particularly from loop detectors and partial observations from Bluetooth and GPS devices.

Priority threat management of non-native plants to maintain ecosystem integrity across heterogeneous landscapes

Invasive non-native plants have negatively impacted on biodiversity and ecosystem functions world-wide. Because of the large number of species, their wide distributions and varying degrees of impact, we need a more effective method for prioritizing control strategies for cost-effective investment across heterogeneous landscapes. Here, we develop a prioritization framework that synthesizes scientific data, elicits knowledge from experts and stakeholders to identify control strategies, and appraises the cost-effectiveness of strategies. Our objective was to identify the most cost-effective strategies for reducing the total area dominated by high-impact non-native plants in the Lake Eyre Basin (LEB). We use a case study of the ˜120 million ha Lake Eyre Basin that comprises some of the most distinctive Australian landscapes, including Uluru-Kata Tjuta National Park. More than 240 non-native plant species are recorded in the Lake Eyre Basin, with many predicted to spread, but there are insufficient resources to control all species. Lake Eyre Basin experts identified 12 strategies to control, contain or eradicate non-native species over the next 50 years. The total cost of the proposed Lake Eyre Basin strategies was estimated at AU$1·7 billion, an average of AU$34 million annually. Implementation of these strategies is estimated to reduce non-native plant dominance by 17 million ha – there would be a 32% reduction in the likely area dominated by non-native plants within 50 years if these strategies were implemented. The three most cost-effective strategies were controlling Parkinsonia aculeata, Ziziphus mauritiana and Prosopis spp. These three strategies combined were estimated to cost only 0·01% of total cost of all the strategies, but would provide 20% of the total benefits. Over 50 years, cost-effective spending of AU$2·3 million could eradicate all non-native plant species from the only threatened ecological community within the Lake Eyre Basin, the Great Artesian Basin discharge springs. Synthesis and applications. Our framework, based on a case study of the ˜120 million ha Lake Eyre Basin in Australia, provides a rationale for financially efficient investment in non-native plant management and reveals combinations of strategies that are optimal for different budgets. It also highlights knowledge gaps and incidental findings that could improve effective management of non-native plants, for example addressing the reliability of species distribution data and prevalence of information sharing across states and regions.

Increasing autonomy transparency through capability communication in multiple heterogeneous UAV management

Interest in the area of collaborative Unmanned Aerial Vehicles (UAVs) in a Multi-Agent System is growing to compliment the strengths and weaknesses of the human-machine relationship. To achieve effective management of multiple heterogeneous UAVs, the status model of the agents must be communicated to each other. This paper presents the effects on operator Cognitive Workload (CW), Situation Awareness (SA), trust and performance by increasing the autonomy capability transparency through text-based communication of the UAVs to the human agents. The results revealed a reduction in CW, increase in SA, increase in the Competence, Predictability and Reliability dimensions of trust, and the operator performance.

Estimation of light interception in research environments: A joint approach using directional light sensors and 3D virtual plants applied to sunflower (Helianthus annuus) and Arabidopsis thaliana in natural and artificial conditions

Light interception is a major factor influencing plant development and biomass production. Several methods have been proposed to determine this variable, but its calculation remains difficult in artificial environments with heterogeneous light. We propose a method that uses 3D virtual plant modelling and directional light characterisation to estimate light interception in highly heterogeneous light environments such as growth chambers and glasshouses. Intercepted light was estimated by coupling an architectural model and a light model for different genotypes of the rosette species Arabidopsis thaliana (L.) Heynh and a sunflower crop. The model was applied to plants of contrasting architectures, cultivated in isolation or in canopy, in natural or artificial environments, and under contrasting light conditions. The model gave satisfactory results when compared with observed data and enabled calculation of light interception in situations where direct measurements or classical methods were inefficient, such as young crops, isolated plants or artificial conditions. Furthermore, the model revealed that A. thaliana increased its light interception efficiency when shaded. To conclude, the method can be used to calculate intercepted light at organ, plant and plot levels, in natural and artificial environments, and should be useful in the investigation of genotype-environment interactions for plant architecture and light interception efficiency. This paper originates from a presentation at the 5th International Workshop on Functional–Structural Plant Models, Napier, New Zealand, November 2007.

Middleware for Mobile Sensing Applications in Urban Environments

Sensor networks represent an attractive tool to observe the physical world. Networks of tiny sensors can be used to detect a fire in a forest, to monitor the level of pollution in a river, or to check on the structural integrity of a bridge. Application-specific deployments of static-sensor networks have been widely investigated. Commonly, these networks involve a centralized data-collection point and no sharing of data outside the organization that owns it. Although this approach can accommodate many application scenarios, it significantly deviates from the pervasive computing vision of ubiquitous sensing where user applications seamlessly access anytime, anywhere data produced by sensors embedded in the surroundings. With the ubiquity and ever-increasing capabilities of mobile devices, urban environments can help give substance to the ubiquitous sensing vision through Urbanets, spontaneously created urban networks. Urbanets consist of mobile multi-sensor devices, such as smart phones and vehicular systems, public sensor networks deployed by municipalities, and individual sensors incorporated in buildings, roads, or daily artifacts. My thesis is that "multi-sensor mobile devices can be successfully programmed to become the underpinning elements of an open, infrastructure-less, distributed sensing platform that can bring sensor data out of their traditional close-loop networks into everyday urban applications". Urbanets can support a variety of services ranging from emergency and surveillance to tourist guidance and entertainment. For instance, cars can be used to provide traffic information services to alert drivers to upcoming traffic jams, and phones to provide shopping recommender services to inform users of special offers at the mall. Urbanets cannot be programmed using traditional distributed computing models, which assume underlying networks with functionally homogeneous nodes, stable configurations, and known delays. Conversely, Urbanets have functionally heterogeneous nodes, volatile configurations, and unknown delays. Instead, solutions developed for sensor networks and mobile ad hoc networks can be leveraged to provide novel architectures that address Urbanet-specific requirements, while providing useful abstractions that hide the network complexity from the programmer. This dissertation presents two middleware architectures that can support mobile sensing applications in Urbanets. Contory offers a declarative programming model that views Urbanets as a distributed sensor database and exposes an SQL-like interface to developers. Context-aware Migratory Services provides a client-server paradigm, where services are capable of migrating to different nodes in the network in order to maintain a continuous and semantically correct interaction with clients. Compared to previous approaches to supporting mobile sensing urban applications, our architectures are entirely distributed and do not assume constant availability of Internet connectivity. In addition, they allow on-demand collection of sensor data with the accuracy and at the frequency required by every application. These architectures have been implemented in Java and tested on smart phones. They have proved successful in supporting several prototype applications and experimental results obtained in ad hoc networks of phones have demonstrated their feasibility with reasonable performance in terms of latency, memory, and energy consumption.

Indexing Heterogeneous XML for Full-Text Search

XML documents are becoming more and more common in various environments. In particular, enterprise-scale document management is commonly centred around XML, and desktop applications as well as online document collections are soon to follow. The growing number of XML documents increases the importance of appropriate indexing methods and search tools in keeping the information accessible. Therefore, we focus on content that is stored in XML format as we develop such indexing methods. Because XML is used for different kinds of content ranging all the way from records of data fields to narrative full-texts, the methods for Information Retrieval are facing a new challenge in identifying which content is subject to data queries and which should be indexed for full-text search. In response to this challenge, we analyse the relation of character content and XML tags in XML documents in order to separate the full-text from data. As a result, we are able to both reduce the size of the index by 5-6\% and improve the retrieval precision as we select the XML fragments to be indexed. Besides being challenging, XML comes with many unexplored opportunities which are not paid much attention in the literature. For example, authors often tag the content they want to emphasise by using a typeface that stands out. The tagged content constitutes phrases that are descriptive of the content and useful for full-text search. They are simple to detect in XML documents, but also possible to confuse with other inline-level text. Nonetheless, the search results seem to improve when the detected phrases are given additional weight in the index. Similar improvements are reported when related content is associated with the indexed full-text including titles, captions, and references. Experimental results show that for certain types of document collections, at least, the proposed methods help us find the relevant answers. Even when we know nothing about the document structure but the XML syntax, we are able to take advantage of the XML structure when the content is indexed for full-text search.

Data-aware task scheduling for all-to-all comparison problems in heterogeneous distributed systems

Solving large-scale all-to-all comparison problems using distributed computing is increasingly significant for various applications. Previous efforts to implement distributed all-to-all comparison frameworks have treated the two phases of data distribution and comparison task scheduling separately. This leads to high storage demands as well as poor data locality for the comparison tasks, thus creating a need to redistribute the data at runtime. Furthermore, most previous methods have been developed for homogeneous computing environments, so their overall performance is degraded even further when they are used in heterogeneous distributed systems. To tackle these challenges, this paper presents a data-aware task scheduling approach for solving all-to-all comparison problems in heterogeneous distributed systems. The approach formulates the requirements for data distribution and comparison task scheduling simultaneously as a constrained optimization problem. Then, metaheuristic data pre-scheduling and dynamic task scheduling strategies are developed along with an algorithmic implementation to solve the problem. The approach provides perfect data locality for all comparison tasks, avoiding rearrangement of data at runtime. It achieves load balancing among heterogeneous computing nodes, thus enhancing the overall computation time. It also reduces data storage requirements across the network. The effectiveness of the approach is demonstrated through experimental studies.

RESTful Discovery and Eventing for Service Provisioning in Assisted Living Environments

Service provisioning in assisted living environments faces distinct challenges due to the heterogeneity of networks, access technology, and sensing/actuation devices in such an environment. Existing solutions, such as SOAP-based web services, can interconnect heterogeneous devices and services, and can be published, discovered and invoked dynamically. However, it is considered heavier than what is required in the smart environment-like context and hence suffers from performance degradation. Alternatively, REpresentational State Transfer (REST) has gained much attention from the community and is considered as a lighter and cleaner technology compared to the SOAP-based web services. Since it is simple to publish and use a RESTful web service, more and more service providers are moving toward REST-based solutions, which promote a resource-centric conceptualization as opposed to a service-centric conceptualization. Despite such benefits of REST, the dynamic discovery and eventing of RESTful services are yet considered a major hurdle to utilization of the full potential of REST-based approaches. In this paper, we address this issue, by providing a RESTful discovery and eventing specification and demonstrate it in an assisted living healthcare scenario. We envisage that through this approach, the service provisioning in ambient assisted living or other smart environment settings will be more efficient, timely, and less resource-intensive.

M(C6H16N3)2(VO3)4 as heterogeneous catalysts. Study of three new hybrid vanadates of cobalt(II), nickel(II) and copper(II) with 1-(2-aminoethyl)piperazonium.

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Subterfuge-safe trust management for delegation of permissions in open environments

Open environments involve distributed entities interacting with each other in an open manner. Many distributed entities are unknown to each other but need to collaborate and share resources in a secure fashion. Usually resource owners alone decide who is trusted to access their resources. Since resource owners in open environments do not have a complete picture of all trusted entities, trust management frameworks are used to ensure that only authorized entities will access requested resources. Every trust management system has limitations, and the limitations can be exploited by malicious entities. One vulnerability is due to the lack of globally unique interpretation for permission specifications. This limitation means that a malicious entity which receives a permission in one domain may misuse the permission in another domain via some deceptive but apparently authorized route; this malicious behaviour is called subterfuge. This thesis develops a secure approach, Subterfuge Safe Trust Management (SSTM), that prevents subterfuge by malicious entities. SSTM employs the Subterfuge Safe Authorization Language (SSAL) which uses the idea of a local permission with a globally unique interpretation (localPermission) to resolve the misinterpretation of permissions. We model and implement SSAL with an ontology-based approach, SSALO, which provides a generic representation for knowledge related to the SSAL-based security policy. SSALO enables integration of heterogeneous security policies which is useful for secure cooperation among principals in open environments where each principal may have a different security policy with different implementation. The other advantage of an ontology-based approach is the Open World Assumption, whereby reasoning over an existing security policy is easily extended to include further security policies that might be discovered in an open distributed environment. We add two extra SSAL rules to support dynamic coalition formation and secure cooperation among coalitions. Secure federation of cloud computing platforms and secure federation of XMPP servers are presented as case studies of SSTM. The results show that SSTM provides robust accountability for the use of permissions in federation. It is also shown that SSAL is a suitable policy language to express the subterfuge-safe policy statements due to its well-defined semantics, ease of use, and integrability.

Runtime Support for Adaptive Power Capping on Heterogeneous SoCs

Power capping is a fundamental method for reducing the energy consumption of a wide range of modern computing environments, ranging from mobile embedded systems to datacentres. Unfortunately, maximising performance and system efficiency under static power caps remains challenging, while maximising performance under dynamic power caps has been largely unexplored. We present an adaptive power capping method that reduces the power consumption and maximizes the performance of heterogeneous SoCs for mobile and server platforms. Our technique combines power capping with coordinated DVFS, data partitioning and core allocations on a heterogeneous SoC with ARM processors and FPGA resources. We design our framework as a run-time system based on OpenMP and OpenCL to utilise the heterogeneous resources. We evaluate it through five data-parallel benchmarks on the Xilinx SoC which allows fully voltage and frequency control. Our experiments show a significant performance boost of 30% under dynamic power caps with concurrent execution on ARM and FPGA, compared to a naive separate approach.

Dependable wireless real-time communications for open environments

Wireless communication technologies have become widely adopted, appearing in heterogeneous applications ranging from tracking victims, responders and equipments in disaster scenarios to machine health monitoring in networked manufacturing systems. Very often, applications demand a strictly bounded timing response, which, in distributed systems, is generally highly dependent on the performance of the underlying communication technology. These systems are said to have real-time timeliness requirements since data communication must be conducted within predefined temporal bounds, whose unfulfillment may compromise the correct behavior of the system and cause economic losses or endanger human lives. The potential adoption of wireless technologies for an increasingly broad range of application scenarios has made the operational requirements more complex and heterogeneous than before for wired technologies. On par with this trend, there is an increasing demand for the provision of cost-effective distributed systems with improved deployment, maintenance and adaptation features. These systems tend to require operational flexibility, which can only be ensured if the underlying communication technology provides both time and event triggered data transmission services while supporting on-line, on-the-fly parameter modification. Generally, wireless enabled applications have deployment requirements that can only be addressed through the use of batteries and/or energy harvesting mechanisms for power supply. These applications usually have stringent autonomy requirements and demand a small form factor, which hinders the use of large batteries. As the communication support may represent a significant part of the energy requirements of a station, the use of power-hungry technologies is not adequate. Hence, in such applications, low-range technologies have been widely adopted. In fact, although low range technologies provide smaller data rates, they spend just a fraction of the energy of their higher-power counterparts. The timeliness requirements of data communications, in general, can be met by ensuring the availability of the medium for any station initiating a transmission. In controlled (close) environments this can be guaranteed, as there is a strict regulation of which stations are installed in the area and for which purpose. Nevertheless, in open environments, this is hard to control because no a priori abstract knowledge is available of which stations and technologies may contend for the medium at any given instant. Hence, the support of wireless real-time communications in unmanaged scenarios is a highly challenging task. Wireless low-power technologies have been the focus of a large research effort, for example, in the Wireless Sensor Network domain. Although bringing extended autonomy to battery powered stations, such technologies are known to be negatively influenced by similar technologies contending for the medium and, especially, by technologies using higher power transmissions over the same frequency bands. A frequency band that is becoming increasingly crowded with competing technologies is the 2.4 GHz Industrial, Scientific and Medical band, encompassing, for example, Bluetooth and ZigBee, two lowpower communication standards which are the base of several real-time protocols. Although these technologies employ mechanisms to improve their coexistence, they are still vulnerable to transmissions from uncoordinated stations with similar technologies or to higher power technologies such as Wi- Fi, which hinders the support of wireless dependable real-time communications in open environments. The Wireless Flexible Time-Triggered Protocol (WFTT) is a master/multi-slave protocol that builds on the flexibility and timeliness provided by the FTT paradigm and on the deterministic medium capture and maintenance provided by the bandjacking technique. This dissertation presents the WFTT protocol and argues that it allows supporting wireless real-time communication services with high dependability requirements in open environments where multiple contention-based technologies may dispute the medium access. Besides, it claims that it is feasible to provide flexible and timely wireless communications at the same time in open environments. The WFTT protocol was inspired on the FTT paradigm, from which higher layer services such as, for example, admission control has been ported. After realizing that bandjacking was an effective technique to ensure the medium access and maintenance in open environments crowded with contention-based communication technologies, it was recognized that the mechanism could be used to devise a wireless medium access protocol that could bring the features offered by the FTT paradigm to the wireless domain. The performance of the WFTT protocol is reported in this dissertation with a description of the implemented devices, the test-bed and a discussion of the obtained results.

Safe Distributed Coordination of Heterogeneous Robots through Dynamic Simple Temporal Networks

Research on autonomous intelligent systems has focused on how robots can robustly carry out missions in uncertain and harsh environments with very little or no human intervention. Robotic execution languages such as RAPs, ESL, and TDL improve robustness by managing functionally redundant procedures for achieving goals. The model-based programming approach extends this by guaranteeing correctness of execution through pre-planning of non-deterministic timed threads of activities. Executing model-based programs effectively on distributed autonomous platforms requires distributing this pre-planning process. This thesis presents a distributed planner for modelbased programs whose planning and execution is distributed among agents with widely varying levels of processor power and memory resources. We make two key contributions. First, we reformulate a model-based program, which describes cooperative activities, into a hierarchical dynamic simple temporal network. This enables efficient distributed coordination of robots and supports deployment on heterogeneous robots. Second, we introduce a distributed temporal planner, called DTP, which solves hierarchical dynamic simple temporal networks with the assistance of the distributed Bellman-Ford shortest path algorithm. The implementation of DTP has been demonstrated successfully on a wide range of randomly generated examples and on a pursuer-evader challenge problem in simulation.

Accessing biodiversity resources in computational environments from workflow applications

In the Biodiversity World (BDW) project we have created a flexible and extensible Web Services-based Grid environment for biodiversity researchers to solve problems in biodiversity and analyse biodiversity patterns. In this environment, heterogeneous and globally distributed biodiversity-related resources such as data sets and analytical tools are made available to be accessed and assembled by users into workflows to perform complex scientific experiments. One such experiment is bioclimatic modelling of the geographical distribution of individual species using climate variables in order to predict past and future climate-related changes in species distribution. Data sources and analytical tools required for such analysis of species distribution are widely dispersed, available on heterogeneous platforms, present data in different formats and lack interoperability. The BDW system brings all these disparate units together so that the user can combine tools with little thought as to their availability, data formats and interoperability. The current Web Servicesbased Grid environment enables execution of the BDW workflow tasks in remote nodes but with a limited scope. The next step in the evolution of the BDW architecture is to enable workflow tasks to utilise computational resources available within and outside the BDW domain. We describe the present BDW architecture and its transition to a new framework which provides a distributed computational environment for mapping and executing workflows in addition to bringing together heterogeneous resources and analytical tools.

Homogeneous and heterogeneous distributed classification for pocket data mining

Pocket Data Mining (PDM) describes the full process of analysing data streams in mobile ad hoc distributed environments. Advances in mobile devices like smart phones and tablet computers have made it possible for a wide range of applications to run in such an environment. In this paper, we propose the adoption of data stream classification techniques for PDM. Evident by a thorough experimental study, it has been proved that running heterogeneous/different, or homogeneous/similar data stream classification techniques over vertically partitioned data (data partitioned according to the feature space) results in comparable performance to batch and centralised learning techniques.