Real-time high-resolution traffic event data streaming by edge devices turns every line-crossing event into data points that can be acted upon immediately by traffic managers, be communicated to connected vehicles, feed traffic simulation engines and digital twin systems. By streaming events for every vehicle, cyclist and pedestrian the moment they cross a georeferenced line, the proposed system (i) detects anomalies immediately after they occur, (ii) inputs to safety and efficiency applications with sub-second situational awareness information, and (iii) uses low-cost hardware and lightweight software stack for scalable network-wide deployments. This extended abstract describes the architecture of a system that we are upgrading our live fleet of traffic sensors and experiments in a simulated environment.

Passenger behaviour on public transport has become a source of great interest in the wake of the COVID-19 pandemic. Operators are interested in employing new methods to monitor vehicle utilisation and passenger behaviour. One way to do this is through the use of Machine Learning, using the CCTV footage that is already being captured from the vehicles. However, one of the limitations of Machine Learning is that it requires large amounts of annotated training data, which is not always available. In this poster, we present a technique that uses 3D models to generate synthetic training images/data and discuss the effect that training with the synthetic data had on the Machine Learning models when applied to real-world CCTV footage.

The rise of big data and smart sensing, with the promise of more educated and informed decisions, has fuelled a shift towards more data-driven decision-making in local and national government. However, we are observing a disconnect between the people who are affected by these decisions and their access to tools and resources to collect data in order to provide the needed evidence for change. To truly democratise this process and for citizens to become active prosumers of data, new mechanisms of citizen data production are needed. In this paper we report on a two-year ethnographic and iterative co-design process with the local community. This work encompassed the design, development and deployment of SenseMyStreet (SeMS), a bespoke sensor commissioning toolkit that enables citizens and community groups to use and commission a city's scientific-grade environmental monitors, determining where they will be located on their streets and collecting data to evidence hyper-local issues. Unlike prior research, which creates alternative data sources to contest city data, our toolkit helps integrate citizen commissioned data into the city datasets used by citizens and decision-makers. Reflecting on the design process and evaluating the ways people engaged with the digital tools of the toolkit, we highlight how commissioning can be configured to promote equity in the smart city, empower citizens to take ownership of issues and facilitate the creation of community networks that utilise the data for local benefit.

Background—Photo-realistic terapixel visualization is computationally intensive and to date there have been no such visualizations of urban digital twins, the few terapixel visualizations that exist have looked towards space rather than earth. Objective—Our aims are: creating ascalablecloud supercomputer software architecture for visualization; aphoto-realistic terapixel 3D visualizationof urban IoT data supporting daily updates; arigorous evaluationof cloud supercomputing for our application. Method—We migrated the Blender Cycles path tracer to the public cloud within a new software framework designed to scale to petaFLOP performance. Results—We demonstrate that we can compute a terapixel visualization in under one hour, the system scaling at 98 percent efficiency to use 1024 public cloud GPU nodes delivering 14 petaFLOPS. The resulting terapixel image supports interactive browsing of the city and its data at a wide range of sensing scales. Conclusion—The GPU compute resource available in the cloud is greater than anything available on our national supercomputers providing access to the globally competitive resources. The direct financial cost of access, compared to procuring and running these systems, was low. The indirect cost, in overcoming teething issues with cloud software development, should reduce significantly over time.

We demonstrate AHOM, a system that can Actively Observe Hazards via Monitoring Social Media Streams. AHOM proposes an active way to include the human in the loop of hazard information ac-quisition for social media. Different from state of the art, it supports bi-directional interaction between social media data processing system and social media users, which leads to the establishment of deeper and more accurate situational awareness of hazard events. We demonstrate how AHOM utilizes Twitter streams and bi-directional information exchange with social media users for enhanced hazard observation.

Summary With the proliferation of Internet of Things (IoT) and edge computing paradigms, billions of IoT devices are being networked to support data‐driven and real‐time decision making across numerous application domains, including smart homes, smart transport, and smart buildings. These ubiquitously distributed IoT devices send the raw data to their respective edge device (eg, IoT gateways) or the cloud directly. The wide spectrum of possible application use cases make the design and networking of IoT and edge computing layers a very tedious process due to the: (i) complexity and heterogeneity of end‐point networks (eg, Wi‐Fi, 4G, and Bluetooth); (ii) heterogeneity of edge and IoT hardware resources and software stack; (iv) mobility of IoT devices; and (iii) the complex interplay between the IoT and edge layers. Unlike cloud computing, where researchers and developers seeking to test capacity planning, resource selection, network configuration, computation placement, and security management strategies had access to public cloud infrastructure (eg, Amazon and Azure), establishing an IoT and edge computing testbed that offers a high degree of verisimilitude is not only complex, costly, and resource‐intensive but also time‐intensive. Moreover, testing in real IoT and edge computing environments is not feasible due to the high cost and diverse domain knowledge required in order to reason about their diversity, scalability, and usability. To support performance testing and validation of IoT and edge computing configurations and algorithms at scale, simulation frameworks should be developed. Hence, this article proposes a novel simulator IoTSim‐Edge, which captures the behavior of heterogeneous IoT and edge computing infrastructure and allows users to test their infrastructure and framework in an easy and configurable manner. IoTSim‐Edge extends the capability of CloudSim to incorporate the different features of edge and IoT devices. The effectiveness of IoTSim‐Edge is described using three test cases. Results show the varying capability of IoTSim‐Edge in terms of application composition, battery‐oriented modeling, heterogeneous protocols modeling, and mobility modeling along with the resources provisioning for IoT applications.

Modern big data processing systems are becoming very complex in terms of large-scale, high-concurrency and multiple talents. Thus, many failures and performance reductions only happen at run-time and are very difficult to capture. Moreover, some issues may only be triggered when some components are executed. To analyze the root cause of these types of issues, we have to capture the dependencies of each component in real-time. In this paper, we propose SmartMonit, a real-time big data monitoring system, which collects infrastructure information such as the process status of each task. At the same time, we develop a real-time stream processing framework to analyze the coordination among the tasks and the infrastructures. This coordination information is essential for troubleshooting the reasons for failures and performance reduction, especially the ones propagated from other causes.

This article presents an innovative approach to establish the quality and credibility of Volunteered Geographic Information (VGI) such that it can be considered in Land Administration Systems (LAS) on a Fit for Purpose (FFP) basis. A participatory land information system can provide affordable and timely FFP information about land and its resources. However, the establishment of such a system involves more than just technical solutions and administrative procedures: many social, economic and political aspects must be considered. Innovative approaches like VGI can help address the lack of accurate, reliable and FFP land information for LAS, but integration of such sources relies on the quality and credibility of VGI. Verifying volunteer efforts can be difficult without reference to ground truth: a novel Trust and Reputation Modelling methodology is proposed as a suitable technique to effect such VGI data set validation. This method has been applied to successfully demonstrate that VGI can produce accurate and reliable data sets which can be used to conduct regular systematic updates of geographic information in official systems. It relies on a view that the public can police themselves in establishing proxy measures of VGI quality thus facilitating VGI to be used on a FFP basis in LAS.

The value of data in supporting citizen participation in processes of place-making and community building is widely recognised. While the open data movement now permits citizens to acquire governmental data relating to their communities, little to no effort is made to ensure that these datasets are accessible and interpretable by non-professionals. Through a series of community engagements spanning an 18-month period, we co-designed Data:In Place, an open source web tool which supports citizens in accessing, interpreting and making sense of open data. Leveraging visual map-based querying, citizens can access official statistics about their community, interrogate the data, and map their own data sources to create data visualisations. Reflecting on the participatory design process and the designed technology, we provide a framing to make open data work for civic advocacy.

The geospatial sensor web is set to revolutionise real-time geospatial applications by making up-to-date spatially and temporally referenced data relating to real-world phenomena ubiquitously available. The uptake of sensor web technologies is largely being driven by the recent introduction of the OpenGIS Sensor Web Enablement framework, a standardisation initiative that defines a set of web service interfaces and encodings to task and query geospatial sensors in near real time. However, live geospatial sensors are capable of producing vast quantities of data over a short time period, which presents a large, fluctuating and ongoing processing requirement that is difficult to adequately provide with the necessary computational resources. Grid computing appears to offer a promising solution to this problem but its usage thus far has primarily been restricted to processing static as opposed to real-time data sets. A new approach is presented in this work whereby geospatial data streams are processed on grid computing resources. This is achieved by submitting ongoing processing jobs to the grid that continually poll sensor data repositories using relevant OpenGIS standards. To evaluate this approach a road-traffic monitoring application was developed to process streams of GPS observations from a fleet of vehicles. Specifically, a Bayesian map-matching algorithm is performed that matches each GPS observation to a link on the road network. The results show that over 90% of observations were matched correctly and that the adopted approach is capable of achieving timely results for a linear time geoprocessing operation performed every 60 seconds. However, testing in a production grid environment highlighted some scalability and efficiency problems. Open Geospatial Consortium (OGC) data services were found to present an IO bottleneck and the adopted job submission method was found to be inefficient. Consequently, a number of recommendations are made regarding the grid job-scheduling mechanism, shortcomings in the OGC Web Processing Service specification and IO bottlenecks in OGC data services.

The need for computational resources capable of processing geospatial data has accelerated the uptake of geospatial web services. Several academic and commercial organizations now offer geospatial web services for data provision, coordinate transformation, geocoding and several other tasks. These web services adopt specifications developed by the Open Geospatial Consortium (OGC) - the leading standardization body for Geographic Information Systems. In parallel with efforts of the OGC, the Grid computing community has published specifications for developing Grid applications. The Open Grid Forum (OGF) is the main body that promotes interoperability between Grid computing systems. This study examines the integration of Grid services and geospatial web services into workflows for Geoscientific processing. An architecture is proposed that bridges web services based on the abstract geospatial architecture (ISO19119) and the Open Grid Services Architecture (OGSA). The paper presents a workflow management system, called SAW-GEO, that supports orchestration of Grid-enabled geospatial web services. An implementation of SAW-GEO is presented, based on both the Simple Conceptual Unified Flow Language (SCUFL) and the Business Process Execution Language for Web Services (WS-BPEL or BPEL for short).

The value of service oriented architectures has been demonstrated in several studies. A key aspect of the advantage of web services is their orchestration into complex business workflows. The Organization for the Advancement of Structured Information Systems (OASIS) has recently approved an industry-wide standard for workflow specification, the Business Process Execution Language (BPEL). The Open Geospatial Consortium (OGC), a member of OASIS, has adopted BPEL for its series of interoperability experiments. This paper presents a study concerned with the use of ontology in assisting geospatial web service orchestration. A methodology for calculating the degree of suitability of various candidate workflows is proposed. The implementation of a prototype plug-in for Eclipse-based BPEL editors is discussed. The proposed system presents candidate workflows based on semantic descriptions of feature, coverage and processing services. An evaluation of the system, based on a workflow involving a variety of geospatial web services is also presented.

A key issue in mobile and ambient computing is the effort required to rapidly prototype and evaluate user interfaces and applications. Existing technologies for these tasks suffer either from low fidelity (e.g. paper prototypes, mental walkthroughs) or effectively require a near full-scale deployment. We propose an approach using immersive video with surround sound and a simulated infrastructure to create a very realistic environment in the office or the lab. It provides a low-cost and rapid means to prototype user interfaces and applications, and to evaluate them in a realistic simulation of the context, in which they are intended to be used.

The ever‐increasing number of spatial data sets accessible through spatial data clearinghouses continues to make geographic information retrieval and spatial data discovery major challenges. Such challenges have been addressed in the discipline of Information Retrieval through ranking of data according to inferred degrees of relevance. Spatial data, however, present an additional challenge as they are characteristically made up of geometry, attribute and, optionally, temporal components. As these components are mutually independent of one another, this paper suggests that they be ranked independently of one another. The representation of the results of the independent ranking of these three components of spatial data suggests that representation of the results of the ranking process requires an alternative approach to currently used textual ranked lists: visualisation of relevance in a three‐dimensional visualisation environment. To illustrate the possible application of such an approach, a prototype browser is presented.