Chasing Puppies: Mobile Beacon Routing on Closed Curves

We solve an open problem posed by Michael Biro at CCCG 2013 that was inspired by his and others' work on beacon-based routing. Consider a human and a puppy on a simple closed curve in the plane. The human can walk along the curve at bounded speed and change direction as desired. The puppy runs with unbounded speed along the curve as long as the Euclidean straight-line distance to the human is decreasing, so that it is always at a point on the curve where the distance is locally minimal. Assuming that the curve is smooth (with some mild genericity constraints) or a simple polygon, we prove that the human can always catch the puppy in finite time.Comment: Full version of a SOCG 2021 paper, 28 pages, 27 figure

Learning spatiotemporal patterns for monitoring smart cities and infrastructure

Recent advances in the Internet of Things (IoT) have changed the way we interact with the world. The ability to monitor and manage objects in the physical world electronically makes it possible to bring data-driven decision making to new realms of city infrastructure and management. Large volumes of spatiotemporal data have been collected from pervasive sensors in both indoor and outdoor environments, and this data reveals dynamic patterns in cities, infrastructure, and public property. In light of the need for new approaches to analysing such data, in this thesis, we propose present relevant data mining techniques and machine learning approaches to extract knowledge from spatiotemporal data to solve real-world problems. Many challenges and problems are under-addressed in smart cities and infrastructure monitoring systems such as indoor person identification, evaluation of city regions segmentation with parking events, fine collection from cars in violations, parking occupancy prediction and airport aircraft path map reconstruction. All the above problems are associated with both spatial and temporal information and the accurate pattern recognition of these spatiotemporal data are essential for determining problem solutions. Therefore, how to incorporate spatiotemporal data mining techniques, artificial intelligence approaches and expert knowledge in each specific domain is a common challenge. In the indoor person identification area, identifying the person accessing a secured room without vision-based or device-based systems is very challenging. In particular, to distinguish time-series patterns on high-dimensional wireless signal channels caused by different activities and people, requires novel time-series data mining approaches. To solve this important problem, we established a device-free system and proposed a two-step solution to identify a person who has accessed a secure area such as an office. Establishing smart parking systems in cities is a key component of smart cities and infrastructure construction. Many sub-problems such as parking space arrangements, fine collection and parking occupancy prediction are urgent and important for city managers. Arranging parking spaces based on historical data can improve the utilisation rate of parking spaces. To arrange parking spaces based on collected spatiotemporal data requires reasonable region segmentation approaches. Moreover, evaluating parking space grouping results needs to consider the correlation between the spatial and temporal domains since these are heterogeneous. Therefore, we have designed a spatiotemporal data clustering evaluation approach, which exploits the correlation between the spatial domain and the temporal domain. It can evaluate the segmentation results of parking spaces in cities using historical data and similar clustering results that group data consisting of both spatial and temporal domains. For fine collection problem, using the sensor instrumentation installed in parking spaces to detect cars in violation and issue infringement notices in a short time-window to catch these cars in time is significantly difficult. This is because most cars in violation leave within a short period and multiple cars are in violation at the same time. Parking officers need to choose the best route to collect fines from these drivers in the shortest time. Therefore, we proposed a new optimisation problem called the Travelling Officer Problem and a general probability-based model. We succeeded in integrating temporal information and the traditional optimisation algorithm. This model can suggest to parking officers an optimised path that maximise the probability to catch the cars in violation in time. To solve this problem in real-time, we incorporated the model with deep learning methods. We proposed a theoretical approach to solving the traditional orienteering problem with deep learning networks. This approach could improve the efficiency of similar urban computing problems as well. For parking occupancy prediction, a key problem in parking space management is with providing a car parking availability prediction service that can inform car drivers of vacant parking lots before they start their journeys using prediction approaches. We proposed a deep learning-based model to solve this parking occupancy prediction problem using spatiotemporal data analysis techniques. This model can be generalised to other spatiotemporal data prediction problems also. In the airport aircraft management area, grouping similar spatiotemporal data is widely used in the real world. Determining key features and combining similar data are two key problems in this area. We presented a new framework to group similar spatiotemporal data and construct a road graph with GPS data. We evaluated our framework experimentally using a state-of-the-art test-bed technique and found that it could effectively and efficiently construct and update airport aircraft route map. In conclusion, the studies in this thesis aimed to discover intrinsic and dynamic patterns from spatiotemporal data and proposed corresponding solutions for real-world smart cities and infrastructures monitoring problems via spatiotemporal pattern analysis and machine learning approaches. We hope this research will inspire the research community to develop more robust and effective approaches to solve existing problems in this area in the future

Data Gathering and Dissemination over Flying Ad-hoc Networks in Smart Environments

The advent of the Internet of Things (IoT) has laid the foundations for new possibilities in our life. The ability to communicate with any electronic device has become a fascinating opportunity. Particularly interesting are UAVs (Unmanned Airborne Vehicles), autonomous or remotely controlled flying devices able to operate in many contexts thanks to their mobility, sensors, and communication capabilities. Recently, the use of UAVs has become an important asset in many critical and common scenarios; thereby, various research groups have started to consider UAVs’ potentiality applied on smart environments. UAVs can communicate with each other forming a Flying Ad-hoc Network (FANET), in order to provide complex services that requires the coordination of several UAVs; yet, this also generates challenging communication issues. This dissertation starts from this standpoint, firstly focusing on networking issues and potential solutions already present in the state-of-the-art. To this aim, the peculiar issues of routing in mobile, 3D shaped ad-hoc networks have been investigated through a set of simulations to compare different ad-hoc routing protocols and understand their limits. From this knowledge, our work takes into consideration the differences between classic MANETs and FANETs, highlighting the specific communication performance of UAVs and their specific mobility models. Based on these assumptions, we propose refinements and improvements of routing protocols, as well as their linkage with actual UAV-based applications to support smart services. Particular consideration is devoted to Delay/Disruption Tolerant Networks (DTNs), characterized by long packet delays and intermittent connectivity, a critical aspect when UAVs are involved. The goal is to leverage on context-aware strategies in order to design more efficient routing solutions. The outcome of this work includes the design and analysis of new routing protocols supporting efficient UAVs’ communication with heterogeneous smart objects in smart environments. Finally, we discuss about how the presence of UAV swarms may affect the perception of population, providing a critical analysis of how the consideration of these aspects could change a FANET communication infrastructure

Who Do You Think You\u27re Border Patrolling? : Negotiating Multiracial Identities and Interracial Relationships

Research on racial border patrolling has demonstrated how people police racial borders in order to maintain socially constructed differences and reinforce divisions between racial groups and their members. Existing literature on border patrolling has primarily focused on white/black couples and multiracial families, with discussions contrasting “white border patrolling” and “black border patrolling,” in terms of differential motivations, intentions, and goals (Dalmage 2000). In my dissertation research, I examined a different type of policing racial categories and the spaces in-between these shifting boundaries. I offer up “multiracial interracial border patrolling” as a means of understanding how borderism impacts the lives of “multiracial” individuals in “interracial” relationships. In taking a look at how both identities and relationships involve racial negotiations, I conducted 60 in-depth, face-to-face qualitative interviews with people who indicated having racially mixed parentage or heritage. Respondents shared their experiences of publicly and privately managing their sometimes shifting preferred racial identities; often racially ambiguous appearance; and situationally in/visible “interracial” relationships in an era of colorblind racism. This management included encounters with border patrolling from strangers, significant others, and self. Not only did border patrolling originate from these three sources, but also manifested itself in a variety of forms, including benevolent (positive, supportive); beneficiary (socially and sometimes economically or materially beneficial); protective, and malevolent (negative, malicious, conflictive). Throughout, I discussed the border patrolling variations that “multiracial” individuals in “interracial” relationships face. I also worked to show how people’s participation in border patrolling encouraged their production of colorblind discourses as a strategy for masking their racial attitudes and ideologies about “multiracial” individuals in “interracial” relationships

The Beacon, September 23, 2004

Vol. 17, Issue 8, 12 pageshttps://digitalcommons.fiu.edu/student_newspaper/1016/thumbnail.jp

Cultivating suspicion: an ethnography of corporeal strategies deployed against vulnerability to crime in Observatory, Cape Town

This ethnographic study explores how people deal with suspicion and navigate the fear of crime in the Observatory suburb of Cape Town, South Africa. The study grapples with the question of how the neighbourhood watch, as a recently revived institution, operates. It analyses the institution and relationships within and around it as an alternative source of trust to the state in combatting crime and its wider impact on lived sociality in the suburb and, perhaps, beyond. The focus of the study lies in understanding the strategies people employ habitually in order to create a sense of security in a context where the anticipation of violence permeates various everyday routines. In analysing strategies of living through insecurities, I focus on examining material and highly visible security measures, such as patrol cars and barbed wires, and engage with the body as a site of social and political memory and struggle, while considering the roles it takes on in the face of perceived precariousness. This dissertation offers an insight in to how the body is deployed as an instrument or buffer to deal with insecurity and crime vulnerability. The quality of public life becomes compromised through embodied strategies of (in)security and vulnerability as employed by the neighbourhood watch. The capacity of a constantly perceived presence of criminal violence in shaping individual and institutional bodies and strategies constitutes the main focus of this study. While the study does not identify the roots of crime as is currently practice with related studies of crime in South Africa, it illuminates the engagement with its perceived presence and thus moves away from a fixed victim-perpetrator dichotomy that has dominated the public discourse

An Analysis of the Law Enforcement Chain in the Eastern Tropical Pacific Seascape, April 2010

The main objective of this study is to identify and evaluate the critical factors required for effective law enforcement in each MPA of the Seascape.The specific objectives are:1. To determine the main strengths and weaknesses of the law enforcement chain in each MPA.2. To prioritize a series of recommendations to improve the enforcement chain in each MPA.3. To identify regional initiatives to strengthen cooperation between member states; in particular regarding the conservation of migratory species

An Analysis of the Law Enforcement Chain in the Eastern Tropical Pacific Seascape

The main objective of this study is to identify and evaluate the critical factors required for effective law enforcement in each MPA of the Seascape. The specific objectives are:1. To determine the main strengths and weaknesses of the law enforcement chain in each MPA.2. To prioritize a series of recommendations to improve the enforcement chain in each MPA.3. To identify regional initiatives to strengthen cooperation between member states; in partcular regarding the conservation of migratory species

New Behavioral Insights Into Home Range Orientation of the House Mouse (Mus musculus)

Home-range orientation is a necessity for an animal that maintains an area of daily activity. The ability to navigate efficiently among goals not perceived at the starting point requires the animal to rely on place recognition and vector knowledge. These two components of navigation allow the animal to dynamically update its current position and link that position with the locomotor distance and direction needed to reach a goal. In order to use place knowledge and vector knowledge the animal must learn and remember relevant spatial information obtained from the environment and from internal feedback. The research in this dissertation focuses on behavioral components of topographic orientation, using the house mouse as a model species. Specifically, this research made important discoveries in three main areas: 1) locomotor exploration behavior, 2) the use of learned spatial information for compass orientation, and 3) testable hypotheses based on the controversial cognitive map. In Chapter 1, I used a radial arm maze to find a systematic locomotor component to exploration behavior, which is typically described as random movement. Exploration refers to the learning process that occurs as an animal acquires relevant spatial information for home-range orientation. I predicted that this process must have a systematic component; and the results revealed that in a radial arm maze, mice avoided exploring a place explored one and two visits prior. Therefore, locomotor exploration does have a systematic component. In Chapter 2, I trained mice to navigate to their home within a circular arena, with access to a visual beacon and an enriched visual background. The mice showed that to navigate home, they preferred to rely on the extra-arena (background) cues for compass direction. However, when these extra-arena cues became unreliable, the mice showed flexibility in their preference by ignoring the visual background and instead relying on the visual beacon to locate home. This flexibility in cue use negates a popular theory, called the snapshot theory, which does not allow for such flexibility in navigation. To further study the use of compass cues in mice, in Chapter 3, I utilized a plus-maze to manipulate both allothetic (environmental) and idiothetic (internal) cues. The purpose was to determine which cue type predominated the directional choice of mice at the maze intersection while both leaving and returning home. Previous studies have ignored the potential difference in cue use during the complete roundtrip an animal would make within its home range. The results show that mice relied on different cues for the outward path and the homing path of a familiar complex roundtrip. Finally, I developed two testable hypotheses and a valid experimental design that can be used to test house mice, and other animals, for the so-called cognitive map. An animal that has a cognitive map would be able to compute a novel shortcut to a goal relying exclusively on the flexibility of such a map, and not from the other two options of novel shortcutting: guidance orientation or path integration. Thus by designing my experiments to eliminate the potential for the mice to rely on a guiding cue to direct them home, and by eliminating the ability to compute a shortcut by summing the vectors previously walked, I was able to test mice for a truly novel, map-based shortcut home. These two hypotheses were named viewpoint extrapolation and viewpoint interpolation and require pure visual exploration to acquire the necessary place and vector knowledge. Both experiments showed that mice were not capable of using pure visual exploration and therefore these studies provide no evidence that mice have a cognitive map. Overall, my research provides evidence that mice do have a mental route-based map and to build such a mental map, locomotor exploration is necessary and sufficient for acquiring relevant spatial knowledge to later use to efficiently navigate