Emily M. Craparo

Throughput Optimization in Mobile Backbone Networks

This paper describes new algorithms for throughput optimization in a mobile backbone network. This hierarchical communication framework combines mobile backbone nodes, which have superior mobility and communication capability, with regular nodes, which are constrained in mobility and communication capability. An important quantity of interest in mobile backbone networks is the number of regular nodes that can be successfully assigned to mobile backbone nodes at a given throughput level. This paper develops a novel technique for maximizing this quantity in networks of fixed regular nodes using mixed-integer linear programming (MILP). The MILP-based algorithm provides a significant reduction in computation time compared to existing methods and is computationally tractable for problems of moderate size. An approximation algorithm is also developed that is appropriate for large-scale problems. This paper presents a theoretical performance guarantee for the approximation algorithm and also demonstrates its empirical performance. Finally, the mobile backbone network problem is extended to include mobile regular nodes, and exact and approximate solution algorithms are presented for this extension.

Measuring and modeling behavioral decision dynamics in collective evacuation

Identifying and quantifying factors influencing human decision making remains an outstanding challenge, impacting the performance and predictability of social and technological systems. In many cases, system failures are traced to human factors including congestion, overload, miscommunication, and delays. Here we report results of a behavioral network science experiment, targeting decision making in a natural disaster. In a controlled laboratory setting, our results quantify several key factors influencing individual evacuation decision making in a controlled laboratory setting. The experiment includes tensions between broadcast and peer-to-peer information, and contrasts the effects of temporal urgency associated with the imminence of the disaster and the effects of limited shelter capacity for evacuees. Based on empirical measurements of the cumulative rate of evacuations as a function of the instantaneous disaster likelihood, we develop a quantitative model for decision making that captures remarkably well the main features of observed collective behavior across many different scenarios. Moreover, this model captures the sensitivity of individual- and population-level decision behaviors to external pressures, and systematic deviations from the model provide meaningful estimates of variability in the collective response. Identification of robust methods for quantifying human decisions in the face of risk has implications for policy in disasters and other threat scenarios, specifically the development and testing of robust strategies for training and control of evacuations that account for human behavior and network topologies.

Optimization of mobile backbone networks: Improved algorithms and approximation

This paper presents new algorithms for throughput optimization in mobile backbone networks. This hierarchical sensing approach combines mobile backbone nodes, which have superior mobility and communication capability, with regular nodes, which are constrained in mobility and communication capability but which can sense the environment. An important quantity of interest in mobile backbone networks is the number of regular nodes that can be successfully assigned to mobile backbone nodes at a given throughput level. This paper develops a novel technique for optimizing this quantity using mixed-integer linear programming (MILP). The MILP- based algorithm provides a significant reduction in computation time compared to existing methods and is computationally tractable for problems of moderate size. An approximation algorithm is also developed that is appropriate for large- scale problems. This approximation algorithm has a theoretical performance guarantee and is demonstrated to perform well in practice.

Natural Language Processing in the Control of Unmanned Aerial Vehicles

This paper addresses the opportunities and challenges involved in applying natural language processing techniques to the control of unmanned aerial vehicles (UAVs). The problem of controlling an unmanned aircraft via natural language inputs is formulated as a feedback control problem. Two implementations of such systems are described. The phraseology of the existing air traffic control language is used as a base command set, and knowledge of air traffic control and airport operations, combined with existing natural language processing techniques, is used to achieve a higher recognition success rate than a traditional natural language processor designed for a more general domain of discourse would. This is the first known attempt at formalizing air traffic control phraseology for use in an unmanned system, and the first known flight of a vehicle controlled by natural language inputs. Outstanding problems and possible directions for future research are described.

Evaluating the direct blast effect in multistatic sonar networks using Monte Carlo simulation

Multistatic sonar networks generalize traditional sonar networks by allowing sources and receivers to occupy different physical locations. Although there are many advantages to a multistatic approach, there are also additional analytic challenges. One such challenge involves the direct blast effect, which can cause targets to go undetected even if they are within the nominal detection range of a sonar network. Previous work has considered the problem of optimally provisioning and deploying a multistatic sonar network while neglecting to consider the blind zone. In this paper, we conduct Monte Carlo simulations to evaluate the impact of the direct blast effect on the performance of such a network. We find that for large pulse lengths, the direct blast effect can significantly decrease the performance of a multistatic network. Moreover, the optimal deployment policy can differ substantially when the direct blast effect is taken into account.

Simultaneous placement and assignment for exploration in mobile backbone networks

This paper presents new algorithms for conducting cooperative sensing using a mobile backbone network. This hierarchical sensing approach combines backbone nodes, which have superior mobility and communication capability, with regular nodes, which are constrained in mobility and communication capability but which can sense the environment. In the framework of a cooperative exploration problem, a technique is developed for simultaneous placement and assignment of regular and mobile backbone nodes. This method, a generalization of existing techniques that only consider stationary regular nodes, optimally solves the simultaneous placement and assignment problem in computationally tractable time for problems of moderate size. For large-scale instances of this problem, a polynomial-time approximation algorithm is developed. This algorithm carries the benefit of a theoretical performance guarantee and also performs well in practice. Finally, the simultaneous placement and assignment technique is incorporated into a cooperative exploration algorithm, and its performance is shown to compare favorably with that of a benchmark based on existing assignment algorithms for mobile backbone networks.

Antenna scanning techniques for estimation of spacecraft position

Scanning movements are added to tracking antenna trajectory to estimate the true spacecraft position. The scanning movements are composed of harmonic axial movements of an antenna. This scanning motion produces power variations of the received signal, which are used to estimate spacecraft position. Three different scanning patterns (conical scan, Lissajous scan, and rosette scan) are presented and analyzed in this paper. The analysis includes the evaluation of the estimation errors due to random or harmonic variation of the antenna position and due to random and harmonic variations of the power level. Typically, the estimation of the spacecraft position is carried out after completing a full scanning cycle. In this paper sliding window scanning is introduced, wherein the spacecraft position estimation is carried out in an almost continuous manner, and it reduces estimation time by half.

Optimizing source and receiver placement in multistatic sonar networks to monitor fixed targets

Abstract Multistatic sonar networks consisting of non-collocated sources and receivers are a promising development in sonar systems, but they present distinct mathematical challenges compared to the monostatic case in which each source is collocated with a receiver. This paper is the first to consider the optimal placement of both sources and receivers to monitor a given set of target locations. Prior publications have only considered optimal placement of one type of sensor, given a fixed placement of the other type. We first develop two integer linear programs capable of optimally placing both sources and receivers within a discrete set of locations. Although these models are capable of placing both sources and receivers to any degree of optimality desired by the user, their computation times may be unacceptably long for some applications. To address this issue, we then develop a two-step heuristic process, Adapt-LOC, that quickly selects positions for both sources and receivers, but with no guarantee of optimality. Based on this, we also create an iterative approach, Iter-LOC, which leads to a locally optimal placement of both sources and receivers, at the cost of larger computation times relative to Adapt-LOC. Finally, we perform computational experiments demonstrating that the newly developed algorithms constitute a powerful portfolio of tools, enabling the user to slect an appropriate level of solution quality, given the available time to perform computations. Our experiments include three real-world case studies.

Selection of a planning horizon for a hybrid microgrid using simulated wind forecasts

Hybrid microgrids containing renewable energy sources represent a promising option for organizations wishing to reduce costs while increasing energy security and islanding time. A prime example of such an organization is the U.S. military, which often operates in isolated areas and whose reliance on a fragile commercial electric grid is seen as a security risk. However, incorporating renewable sources into a microgrid is difficult due to their typically intermittent and unpredictable nature. We use simulation techniques to investigate the performance of a hypothetical hybrid microgrid containing both wind turbines and fossil fuel based power sources. Our simulation model produces realistic weather forecast scenarios, which we use to exercise our optimization model and predict optimal grid performance. We perform a sensitivity analysis and find that for day-ahead planning, longer planning horizons are superior to shorter planning horizons, but this improvement diminishes as the length of the planning horizon increases.

Constrained node placement and assignment in mobile backbone networks

This paper describes new algorithms for mobile backbone network optimization. In this hierarchical communication framework, mobile backbone nodes (MBNs) are deployed to provide communication support for regular nodes (RNs). While previous work has assumed that MBNs are unconstrained in position, this work models constraints in MBN location. This paper develops an exact technique for maximizing the number of RNs that achieve a threshold throughput level, as well as a polynomial-time approximation algorithm for this problem. The approximation algorithm carries a performance guarantee of 1 over 2 and we demonstrate that this guarantee is tight in some problem instances.