When a fleet of mobile units is used for surveillance and response to potential events, the geographic area of interest is often partitioned into smaller regions, and a particular (subset of) unit(s) is assigned to each region. One of the main reasons is to put a bound on the travel time for a unit in charge of responding to a new event/request from the unit's current location to the location of the occurrence of that event. In practice, the area may contain obstacles (e.g., buildings that have to be circumvented by ground mobile units or no-fly zones in case of drones). Although the problems of navigating among obstacles and spatial partitioning have been studied in the past, in this work we take a step towards tackling the setting of partitioning a geographic area of interest with obstacles in it, for the purpose of bounding the worst-case response time to an event by a member of a fleet of mobile units. To this end, we introduce a novel data structure and present an algorithmic solution for its construction, enabling a distribution of a fleet of mobile units to disjoint regions of the area of interest in a manner that will ensure a bound on the worst case response time in each region. Our experiments over real and synthetic datasets demonstrate the benefits of the proposed methodology over adaptation of existing spatial partitioning techniques.

we present a system that enables testing the impacts of collaborative monitoring of geographical regions by a fleet of drones. Specifically, we consider the settings in which a simulation is executed, based on the properties of a particular approach, and we enable users to gather the basic statistics and compare the parameters of interest for different approaches under varying conditions, as well as observe a basic visualization of the flight paths. In particular, we can vary the number of drones in the fleet, their initial distribution, the occurrences of events of interests (e.g., a potential threat), and the transition of the drones (i.e., their trajectories) in response to a detection of an event. In addition to viewing and analyzing different values of interest, users can upload their collaboration algorithm, add/change the environmental factors, observe the discrepancies, and decide which algorithm is best for their application. Moreover, an authenticated user can save the algorithm and resulting metrics for subsequent retrieval. Our prototype is a web-based system using SpringBoot, a Java framework, relying on MySQL for data management and APIs to communicate with the front-end built on React, enabling various extensibilities (algorithms, environmental parameters, events, drones configuration).

The objective of this research is to develop novel models and algorithmic solutions for the surveillance of geo-graphic regions by a collaborative fleet of drones. More specifi-cally, we consider dividing the region of interest into responsibil-ity areas for an individual drone and allow for a certain number of, so called, extra-drones that can change their whereabouts in terms of responsibility areas. We then propose policies for assistance among drones in different regions. The global objective is to also satisfy certain constraints, such as ensuring optimal response time; ensuring minimization of uncovered areas; etc. The work in this thesis is conducted under a supervision of Prof. Goce Trajcevski (Dept. of ECE, Iowa State University), in collaboration with Prof. Ouri Wolfson (Dept. of CS, University of Illinois at Chicago) and Prof. Sushil Jajodia (Dept. of CS, George Mason University).

Traditionally, clustering of multivariate data aims at grouping objects described with multiple heterogeneous attributes based on a suitable similarity (conversely, distance) function. One of the main challenges is due to the fact that it is not straightforward to directly apply mathematical operations (e.g., sum, average) to the feature values, as they stem from heterogeneous contexts.

We consider surveillance of a geographic region by a collaborative system of drones. The drones assist each other in identifying and managing activities of interest on the ground. We also consider an adversary who can create both genuine and fake activities on the ground. The objective of the adversary is to use fake activities, in order to maximize the response time to genuine activities. We present two collaboration algorithms and analyze their response times, as well as the adversary's efforts in terms of the number of fake activities required to achieve a certain response time.

We present CACSE – a system for Context Aware Clustering of Stellar Evolution – for datasets corresponding to temporal evolution of stars, which are multivariate time series, usually with a large number of attributes (e.g., ≥ 40). Typically, the datasets are obtained by simulation and are relatively large in size (5 ∼ 10 GB per certain interval of values for various initial conditions). Investigating common evolutionary trends in these datasets often depends on the context – i.e., not all the attributes are always of interest, and among the subset of the context-relevant attributes, some may have more impact than others. To enable such context-aware clustering, our CACSE system provides functionalities allowing the domain experts to dynamically select attributes that matter, and assign desired weights/priorities. Our system consists of a PostgreSQL database, Python-based middleware with RESTful and Django framework, and a web-based user interface as frontend. The user interface provides multiple interactive options, including selection of datasets and preferred attributes along with the corresponding weights. Subsequently, the users can select a time instant or a time range to visualize the formed clusters. Thus, CACSE enables a detection of changes in the the set of clusters (i.e., convoys) of stellar evolution tracks. Current version provides two of the most popular clustering algorithms – k-means and DBSCAN.

In this paper, we present the CET-LATS (Compressing Evolution of TINs from Location Aware Time Series) system, which enables testing the impacts of various compression approaches on evolving Triangulated Irregular Networks (TINs). Specifically, we consider the settings in which values measured in distinct locations and at different time instants, are represented as time series of the corresponding measurements, generating a sequence of TINs. Different compression techniques applied to location-specific time series may have different impacts on the representation of the global evolution of TINs - depending on the distance functions used to evaluate the distortion. CET-LATS users can view and analyze compression vs. (im)precision trade-offs over multiple compression methods and distance functions, and decide which method works best for their application. We also provide an option to investigate the impact of the choice of a compression method on the quality of prediction. Our prototype is a web-based system using Flask, a lightweight Python framework, relying on Apache Spark for data management and JSON files to communicate with the front-end, enabling extensibility in terms of adding new data sources as well as compression techniques, distance functions and prediction methods.