Simulation of attacks for security in wireless sensor network

The increasing complexity and low-power constraints of current Wireless Sensor Networks (WSN) require efficient methodologies for network simulation and embedded software performance analysis of nodes. In addition, security is also a very important feature that has to be addressed in most WSNs, since they may work with sensitive data and operate in hostile unattended environments. In this paper, a methodology for security analysis of Wireless Sensor Networks is presented. The methodology allows designing attack-aware embedded software/firmware or attack countermeasures to provide security in WSNs. The proposed methodology includes attacker modeling and attack simulation with performance analysis (node?s software execution time and power consumption estimation). After an analysis of different WSN attack types, an attacker model is proposed. This model defines three different types of attackers that can emulate most WSN attacks. In addition, this paper presents a virtual platform that is able to model the node hardware, embedded software and basic wireless channel features. This virtual simulation analyzes the embedded software behavior and node power consumption while it takes into account the network deployment and topology. Additionally, this simulator integrates the previously mentioned attacker model. Thus, the impact of attacks on power consumption and software behavior/execution-time can be analyzed. This provides developers with essential information about the effects that one or multiple attacks could have on the network, helping them to develop more secure WSN systems. This WSN attack simulator is an essential element of the attack-aware embedded software development methodology that is also introduced in this work.This work has been funded by the Spanish MICINN under the TEC2011-28666-C04-02 and TEC2014-58036-C4-3-R project

A framework for the near-real-time optimization of integrated oil & gas midstream processing networks

The oil and gas industry plays a key role in the world’s economy. Vast quantities of crude oil, their by-products and derivatives are produced, processed and distributed every day. Indeed, producing and processing significant volumes of crude oil requires connecting to wells in different fields that are usually spread across large geographical areas. This crude oil is then processed by Gas Oil Separation Plants (GOSPs). These facilities are often grouped into clusters that are within approximate distance from each other and then connected laterally via swing lines which allow shifting part or all of the production from one GOSP to another. Transfer lines also exist to allow processing intermediate products in neighbouring GOSPs, thereby increasing complexity and possible interactions. In return, this provides an opportunity to leverage mathematical optimization to improve network planning and load allocation. Similarly, in major oil producing countries, vast gas processing networks exist to process associated and non-associated gases. These gas plants are often located near major feed sources. Similar to GOSPs, they are also often connected through swing lines, which allow shifting feedstock from some plants to others. GOSPs and gas plants are often grouped as oil and gas midstream plants. These plants are operated on varied time horizons and plant boundaries. While plant operators are concerned with the day-to-day operation of their facility, network operators must ensure that the entire network is operated optimally and that product supply is balanced with demand. They are therefore in charge of allocating load to individual plants, while knowing each plants constraints and processing capabilities. Network planners are also in charge of producing production plans at varied time-scales, which vary from yearly to monthly and near-real time. This work aims to establish a novel framework for optimizing Oil and Gas Midstream plants for near-real time network operation. This topic has not been specifically addressed in the existing literature. It examines problems which involve operating networks of GOSPs and gas plants towards an optimal solution. It examines various modelling approaches which are suited for this specific application. It then focuses at this stage of the research on the GOSP optimization problem where it addresses optimizing the operation of a complex network of GOSPs. The goal is to operate this network such that oil production targets are met at minimum energy consumption, and therefore minimizing OpEx and Greenhouse Gas Emissions. Similarly, it is often required to operate the network such that production is maximized. This thesis proposes a novel methodology to formulate and solve this problem. It describes the level of fidelity used to represent physical process units. A Mixed Integer Non-Linear Programming (MINLP) problem is then formulated and solved to optimize load allocation, swing line flowrates and equipment utilization. The model demonstrates advanced capabilities to systematically prescribe optimal operating points. This was then applied to an existing integrated network of GOSPs and tested at varying crude oil demand levels. The results demonstrate the ability to minimize energy consumption by up to 51% in the 50% throughput case while meeting oil production targets without added capital investment.Open Acces

The Design and Implementation of an Extensible Brain-Computer Interface

An implantable brain computer interface: BCI) includes tissue interface hardware, signal conditioning circuitry, analog-to-digital conversion: ADC) circuitry and some sort of computing hardware to discriminate desired waveforms from noise. Within an experimental paradigm the tissue interface and ADC hardware will rarely change. Recent literature suggests it is often the specific implementation of waveform discrimination that can limit the usefulness and lifespan of a particular BCI design. If the discrimination techniques are implemented in on-board software, experimenters gain a level of flexibility not currently available in published designs. To this end, I have developed a firmware library to acquire data sampled from an ADC, discriminate the signal for desired waveforms employing a user-defined function, and perform arbitrary tasks. I then used this design to develop an embedded BCI built upon the popular Texas Instruments MSP430 microcontroller platform. This system can operate on multiple channels simultaneously and is not fundamentally limited in the number of channels that can be processed. The resulting system represents a viable platform that can ease the design, development and use of BCI devices for a variety of applications

Computer Science & Technology Series : XXI Argentine Congress of Computer Science. Selected papers

CACIC’15 was the 21thCongress in the CACIC series. It was organized by the School of Technology at the UNNOBA (North-West of Buenos Aires National University) in Junín, Buenos Aires. The Congress included 13 Workshops with 131 accepted papers, 4 Conferences, 2 invited tutorials, different meetings related with Computer Science Education (Professors, PhD students, Curricula) and an International School with 6 courses. CACIC 2015 was organized following the traditional Congress format, with 13 Workshops covering a diversity of dimensions of Computer Science Research. Each topic was supervised by a committee of 3-5 chairs of different Universities. The call for papers attracted a total of 202 submissions. An average of 2.5 review reports werecollected for each paper, for a grand total of 495 review reports that involved about 191 different reviewers. A total of 131 full papers, involving 404 authors and 75 Universities, were accepted and 24 of them were selected for this book.Red de Universidades con Carreras en Informática (RedUNCI

Computer Science & Technology Series : XXI Argentine Congress of Computer Science. Selected papers

CACIC’15 was the 21thCongress in the CACIC series. It was organized by the School of Technology at the UNNOBA (North-West of Buenos Aires National University) in Junín, Buenos Aires. The Congress included 13 Workshops with 131 accepted papers, 4 Conferences, 2 invited tutorials, different meetings related with Computer Science Education (Professors, PhD students, Curricula) and an International School with 6 courses. CACIC 2015 was organized following the traditional Congress format, with 13 Workshops covering a diversity of dimensions of Computer Science Research. Each topic was supervised by a committee of 3-5 chairs of different Universities. The call for papers attracted a total of 202 submissions. An average of 2.5 review reports werecollected for each paper, for a grand total of 495 review reports that involved about 191 different reviewers. A total of 131 full papers, involving 404 authors and 75 Universities, were accepted and 24 of them were selected for this book.Red de Universidades con Carreras en Informática (RedUNCI

Modeling, Simulation and Control of Very Flexible Unmanned Aerial Vehicle

This dissertation presents research on modeling, simulation and control of very flexible aircraft. This work includes theoretical and numerical developments, as well as experimental validations. On the theoretical front, new kinematic equations for modeling sensors are derived. This formulation uses geometrically nonlinear strain-based finite elements developed as part of University of Michigan Nonlinear Aeroelastic Simulation Toolbox (UM/NAST). Numerical linearizations of both the flexible vehicle and the sensor measurements are developed, allowing a linear time invariant model to be extracted for control analysis and design. Two different algorithms to perform sensor fusion from different sensor sources to extract elastic deformation are investigated. Nonlinear least square method uses geometry and nonlinear beam strain-displacement kinematics to reconstruct the wing shape. Detailed information such as material properties or loading conditions are not required. The second method is the Kalman filter, implemented in a multi-rate form. This method requires a dynamical system representation to be available. However, it is more robust to noise corruption in sensor measurements. In order to control maneuver loads, Model Predictive Control is applied to maneuver load alleviation of a representative very flexible aircraft (X-HALE). Numerical studies are performed in UM/NAST for pitch up and roll maneuvers. Both control and state constraints are successfully enforced, while reference commands are still being tracked. MPC execution is also timed and current implementation is capable of almost real-time operation. On the experimental front, two aeroelastic testbed vehicles (ATV-6B and RRV-6B) are instrumented with sensors. On ATV-6B, an extensive set of sensors measuring structural, flight dynamic, and aerodynamic information are integrated on-board. A novel stereo-vision measurement system mounted on the body center looking towards the wing tip measures wing deformation. High brightness LEDs are used as target markers for easy detection and to allow each view to be captured with fast camera shutter speed. Experimental benchmarks are conducted to verify the accuracy of this methodology. RRV-6B flight test results are presented. System identification is applied to the experimental data to generate a SISO description of the flexible aircraft. System identification results indicate that the UM/NAST X-HALE model requires some tuning to match observed dynamics. However, the general trends predicted by the numerical model are in agreement with flight test results. Finally, using this identified plant, a stability augmentation autopilot is designed and flight tested. This augmentation autopilot utilizes a cascaded two-loop proportional integral control design, with the inner loop regulating angular rates and outer loop regulating attitude. Each of the three axes is assumed to be decoupled and designed using SISO methodology. This stabilization system demonstrates significant improvements in the RRV-6B handling qualities. This dissertation ends with a summary of the results and conclusions, and its main contribution to the field. Suggestions for future work are also presented.PHDAerospace EngineeringUniversity of Michigan, Horace H. Rackham School of Graduate Studieshttps://deepblue.lib.umich.edu/bitstream/2027.42/144019/1/pziyang_1.pd

Pushing the Boundaries of Spacecraft Autonomy and Resilience with a Custom Software Framework and Onboard Digital Twin

This research addresses the high CubeSat mission failure rates caused by inadequate software and overreliance on ground control. By applying a reliable design methodology to flight software development and developing an onboard digital twin platform with fault prediction capabilities, this study provides a solution to increase satellite resilience and autonomy, thus reducing the risk of mission failure. These findings have implications for spacecraft of all sizes, paving the way for more resilient space missions

Online state of charge estimation for the aerial lithium-ion battery packs based on the improved extended Kalman filter method.

An effective method to estimate the integrated state of charge (SOC) value for the lithium-ion battery (LIB) pack is proposed, because of its capacity state estimation needs in the high-power energy supply applications, which is calculated by using the improved extended Kalman filter (EKF) method together with the one order equivalent circuit model (ECM) to evaluate its remaining available power state. It is realized by the comprehensive estimation together with the discharging and charging maintenance (DCM) process, implying an accurate remaining power estimation with low computational calculation demand. The battery maintenance and test system (BMTS) equipment for the aerial LIB pack is developed, which is based on the proposed SOC estimation method. Experimental results show that, it can estimate SOC value of the LIB pack effectively. The BMTS equipment has the advantages of high detection accuracy and stability and can guarantee its power-supply reliability. The SOC estimation method is realized on it, the results of which are compared with the conventional SOC estimation method. The estimation has been done with an accuracy rate of 95% and has an absolute root mean square error (RMSE) of 1.33% and an absolute maximum error of 4.95%. This novel method can provide reliable technical support for the LIB power supply application, which plays a core role in promoting its power supply applications

AI-Driven Security Constrained Unit Commitment Using Predictive Modeling And Eigen Decomposition

Security Constrained Unit Commitment (SC-UC) is a complex large scale mix integer constrained optimization problem solved by Independent System Operators (ISOs) in the daily planning of the electricity markets. After receiving offers and bids, ISOs have only few hours to clear the day-ahead electricity market. It requires a lot of computational effort and a reasonable time to solve a large-scale SC-UC problem. However, exploiting the fact that a UC problem is solved several times a day with only minor changes in the system data, the computational effort can be reduced by learning from the historical data and identifying the patterns in the historical data using data mining techniques. In this research study, two data driven approaches based on predictive modeling techniques are proposed to solve a SC-UC problem in a day ahead electricity market which can be used as alternative backup methods for solving a SC-UC problem. In the first approach, the SC-UC is partially modeled using predictive modeling techniques to enhance the computational speed of the problem, while in the second approach, the optimization problem is completely replaced by data driven predictive models to further enhance the computational efficiency, however, at the cost of some optimality loss. The proposed approaches are validated through numerical simulations on different IEEE case studies to demonstrate and study the effectiveness of the developed approaches. The results obtained from the proposed approaches are compared with those obtained from commercial optimization solvers e.g., IBM CPLEX MIQP and GUROBI MIQP solvers