Efficient, decentralized detection of qualitative spatial events in a dynamic scalar field

This paper describes an efficient, decentralized algorithm to monitor qualitative spatial events in a dynamic scalar field. The events of interest involve changes to the critical points (i.e., peak, pits and passes) and edges of the surface network derived from the field. Four fundamental types of event (appearance, disappearance, movement and switch) are defined. Our algorithm is designed to rely purely on qualitative information about the neighborhoods of nodes in the sensor network and does not require information about nodes' coordinate positions. Experimental investigations confirm that our algorithm is efficient, with O(n) overall communication complexity (where n is the number of nodes in the sensor network), an even load balance and low operational latency. The accuracy of event detection is comparable to established centralized algorithms for the identification of critical points of a surface network. Our algorithm is relevant to a broad range of environmental monitoring applications of sensor networks

Monitoring Dynamic Spatial Fields Using Responsive Geosensor Networks

Many environmental phenomena (e.g., changes in global levels of atmospheric carbon dioxide) can be modeled as variations of attributes over regions of space and time, called dynamic spatial fields. The goal of this project is to provide efficient ways for sensor networks to monitor such fields, and to report significant changes in them. The focus is on qualitative changes, such as splitting of areas or emergence of holes in a region of study. The approach is to develop qualitative and topological methods to deal with changes. Qualitative properties form a small, discrete space, whereas quantitative values form a large, continuous space, and this enables efficiencies to be gained over traditional quantitative methods. The combinatorial map model of the spatial embedding of the sensor network is rich enough so that for each sensor, its position, and the distances and bearings of neighboring sensors, are easily computed. The sensors are responsive to changes to the spatial field, so that sensors are activated in the vicinity of interesting developments in the field, while sensors are deactivated in quiescent locations. All computation and message passing is local , with no centralized control. Optimization is addressed through use of techniques in qualitative representation and reasoning, and efficient update through a dynamic and responsive underlying spatial framework. Effective deployment of very large arrays of sensors for environmental monitoring has important scientific and societal benefits. The project is integrated with the NSF IGERT program on Sensor Science, Engineering, and Informatics at the University of Maine, which will enhance educational and outreach opportunities. The project Web site (http://www.spatial.maine.edu/~worboys/sensors.html) will be used for broad results dissemination

Visual Analytic Design for Contextualising Sensor Data

The design choices behind a visual analytic system for monitoring a geosensor network are described and justified. Sparse spatial but fine-grained temporal data drive the layout choices privileging temporal pattern and classified location symbolised by colour. Additional context is provided by clustering space-time signatures and ordering data by cluster size. Uncertainty in classification is reflected in interpolation of categorical colours and interaction allowing multiple mappings of high dimensional clusters onto a onedimensional ordering

Identifying surrounds and engulfs relations in mobile and coordinate-free geosensor networks

This paper concerns the definition and identification of qualitative spatial relationships for the full and partial enclosure of spatial regions. The paper precisely defines three relationships between regions, 'surrounds', 'engulfs', and 'envelops', highlighting the correspondence to similar definitions in the literature. An efficient algorithm capable of identifying these qualitative spatial relations in a network of dynamic (mobile) geosensor nodes is developed and tested. The algorithms are wholly decentralized, and operate in-network with no centralized control. The algorithms are also ``coordinate-free,'' able to operate in distributed spatial computing environments where coordinate locations are expensive to capture or otherwise unavailable. Experimental evaluation of the algorithms designed demonstrates the efficiency of the approach. Although the algorithm communication complexity is dominated by an overall worst-case O(n^2) leader election algorithm, the experiments show in practice an average-case complexity approaching linear, O(n^{1.1})

Sensor-Driven, Spatially Explicit Agent-Based Models

Conventionally, agent-based models (ABMs) are specified from well-established theory about the systems under investigation. For such models, data is only introduced to ensure the validity of the specified models. In cases where the underlying mechanisms of the system of interest are unknown, rich datasets about the system can reveal patterns and processes of the systems. Sensors have become ubiquitous allowing researchers to capture precise characteristics of entities in both time and space. The combination of data from in situ sensors to geospatial outputs provides a rich resource for characterising geospatial environments and entities on earth. More importantly, the sensor data can capture behaviours and interactions of entities allowing us to visualise emerging patterns from the interactions. However, there is a paucity of standardised methods for the integration of dynamic sensor data streams into ABMs. Further, only few models have attempted to incorporate spatial and temporal data dynamically from sensors for model specification, calibration and validation. This chapter documents the state of the art of methods for bridging the gap between sensor data observations and specification of accurate spatially explicit agent-based models. In addition, this work proposes a conceptual framework for dynamic validation of sensor-driven spatial ABMs to address the risk of model overfitting

Dynamic Decentralization Domains for the Internet of Things

The Internet of Things (IoT) and edge computing are fostering a future of ecosystems hosting complex decentralized computations that are deeply integrated with our very dynamic environments. Digitalized buildings, communities of people, and cities will be the next-generation "hardware and platform,"counting myriads of interconnected devices, on top of which intrinsically distributed computational processes will run and self-organize. They will spontaneously spawn, diffuse to pertinent logical/physical regions, cooperate and compete, opportunistically summon required resources, collect and analyze data, compute results, trigger distributed actions, and eventually decay. What would a programming model for such ecosystems look like? Based on research findings on self-adaptive/self-organizing systems, this article proposes design abstractions based on "dynamic decentralization domains": regions of space opportunistically formed to support situated recognition and action. We embody the approach into a Scala application program interface (API) enacting distributed execution and show its applicability in a case study of environmental monitoring

Support Vector Methods for Higher-Level Event Extraction in Point Data

Phenomena occur both in space and time. Correspondingly, ability to model spatiotemporal behavior translates into ability to model phenomena as they occur in reality. Given the complexity inherent when integrating spatial and temporal dimensions, however, the establishment of computational methods for spatiotemporal analysis has proven relatively elusive. Nonetheless, one method, the spatiotemporal helix, has emerged from the field of video processing. Designed to efficiently summarize and query the deformation and movement of spatiotemporal events, the spatiotemporal helix has been demonstrated as capable of describing and differentiating the evolution of hurricanes from sequences of images. Being derived from image data, the representations of events for which the spatiotemporal helix was originally created appear in areal form (e.g., a hurricane covering several square miles is represented by groups of pixels). ii Many sources of spatiotemporal data, however, are not in areal form and instead appear as points. Examples of spatiotemporal point data include those from an epidemiologist recording the time and location of cases of disease and environmental observations collected by a geosensor at the point of its location. As points, these data cannot be directly incorporated into the spatiotemporal helix for analysis. However, with the analytic potential for clouds of point data limited, phenomena represented by point data are often described in terms of events. Defined as change units localized in space and time, the concept of events allows for analysis at multiple levels. For instance lower-level events refer to occurrences of interest described by single data streams at point locations (e.g., an individual case of a certain disease or a significant change in chemical concentration in the environment) while higher-level events describe occurrences of interest derived from aggregations of lower-level events and are frequently described in areal form (e.g., a disease cluster or a pollution cloud). Considering that these higher-level events appear in areal form, they could potentially be incorporated into the spatiotemporal helix. With deformation being an important element of spatiotemporal analysis, however, at the crux of a process for spatiotemporal analysis based on point data would be accurate translation of lower-level event points into representations of higher-level areal events. A limitation of current techniques for the derivation of higher-level events is that they imply bias a priori regarding the shape of higher-level events (e.g., elliptical, convex, linear) which could limit the description of the deformation of higher-level events over time. The objective of this research is to propose two newly developed kernel methods, support vector clustering (SVC) and support vector machines (SVMs), as means for iii translating lower-level event points into higher-level event areas that follow the distribution of lower-level points. SVC is suggested for the derivation of higher-level events arising in point process data while SVMs are explored for their potential with scalar field data (i.e., spatially continuous real-valued data). Developed in the field of machine learning to solve complex non-linear problems, both of these methods are capable of producing highly non-linear representations of higher-level events that may be more suitable than existing methods for spatiotemporal analysis of deformation. To introduce these methods, this thesis is organized so that a context for these methods is first established through a description of existing techniques. This discussion leads to a technical explanation of the mechanics of SVC and SVMs and to the implementation of each of the kernel methods on simulated datasets. Results from these simulations inform discussion regarding the application potential of SVC and SVMs

Indigeneity and spatial information science

Spatial information science has given rise to a set of concepts, tools, and techniques for understanding our geographic world. In turn, the technologies built on this body of knowledge embed certain ways of knowing. This vision paper traces the roots and impacts of those embeddings and explores how they can sometimes be inherently at odds with or completely subvert Indigenous Peoples\u27 ways of knowing. However advancements in spatial information science offer opportunities for innovation whilst working towards reconciliation. We highlight as examples four active research topics in the field to support a call to action for greater inclusion of Indigenous perspectives in spatial information science