Ivan G. Guardiola

RSSI and LQI Data Clustering Techniques to Determine the Number of Nodes in Wireless Sensor Networks

With the rapid proliferation of wireless sensor networks, different network topologies are likely to exist in the same geographical region, each of which is able to perform its own functions individually. However, these networks are prone to cause interference to neighbor networks, such as data duplication or interception. How to detect, determine, and locate the unknown wireless topologies in a given geographical area has become a significant issue in the wireless industry. This problem is especially acute in military use, such as spy-nodes detection and communication orientation systems. In this paper, three different clustering methods are applied to classify the RSSI and LQI data recorded from the unknown wireless topology into a certain number of groups in order to determine the number of active sensor nodes in the unknown wireless topology. The results show that RSSI and LQI data are capable of determining the number of active communication nodes in wireless topologies.

A Nonparametric Method for Detecting Unintended Electromagnetic Emissions

Unintended electromagnetic emissions of RF transceivers are signals that are emitted by all RF devices and often lie within the noise band. The identification of such signals becomes an important issue as the identification, localization, and recognition of malicious wireless devices could result in a passive means to render such devices useless. In this paper, we present a new nonparametric method to distinguish signals from noise based in a morphological trimming of the data complemented with an analysis of the shape of the sequence of curves in which the data series can be decomposed. The method takes its tools from the mathematical morphology and multivariate statistics. The good performance of this methodology is illustrated with a set of real data coming from three small RF transceivers such as two-way talk radios, which are commonly used in improvised explosive devices.

Using University-Funded Research Projects to Teach System Design Processes and Tools

This paper highlights the development of a new course structure and an associated knowledge support system, deployed as a pilot program at the senior undergraduate and first-semester graduate level. These two new courses, run consecutively, allow the injection of funded research projects to be used as a means to teach topics related to systems engineering. During the two courses, the students analyzed a real Department of Defense (DoD) problem. Specifically, the courses sought to teach students how to apply systems engineering processes, tools, and analysis to engineering design problems. These two courses are described in depth, and details are given of the knowledge support system, educational structure, and assessment of this pilot program. Student perception of this new educational pilot program was assessed by surveys and is reported here.

Development of an immersive training vest

Teams of students across four semesters have serially designed subsystems to be integrated into a cohesive immersive training vest system for military applications. The vest enhances the ability to provide realistic and real-time feedback on both battlefield effects (e.g. weapons fire) as well as performance information (e.g. correct/incorrect cultural response). The integrated system provides improved capabilities for real time training performance feedback though the following technology development efforts: robust zigbee wireless mesh networking, composite plate-mounted tactor motors for haptic feedback, hand/arm gesture tracking, and indoor location tracking. These technologies have been developed and tested as subsystems and are being integrated into a mock training facility at Missouri S&T for up to 15 individual trainees. The system development has been undertaken by an interdisciplinary student and faculty team relying on expertise in Systems Engineering, Electrical and Computer Engineering, Computer Science, and Mechanical and Aerospace Engineering. The system was primarily developed as an applied exercise for a series of two introductory graduate-level courses for Systems Engineering. Thus students followed a comprehensive design process; from requirements derivation, functional analysis, guided trade studies, interface control, etc. towards a final detailed design. The students were personally mentored by faculty and PhD students, as well as industry mentors from the Boeing Company through a structured three phase design review process. Students applied analytical tools for system optimization and simulation to derive design parameters and estimate system performance against technical requirement thresholds. Currently, the subsystems have been prototyped and tested to meet functional requirements and the full integrated system is expected to be completed by May 2012 and will begin design validation at that time.

System for Detection of Malicious Wireless Device Patterns

The research within presents the use of Hidden Markov Models (HMM) for the detection of wireless devices in highly noisy environments using their unintended electromagnetic emissions (UEE). All electromagnetic devices emit such radiation that is unique to the electronics, housing, and other device attributes. This pattern recognition system can provide continuous detection analysis and can provide ideal information regarding the distance to an unknown device. An experiment was performed where UEE of a device was detected by a spectrum analyzer. Experimental result shows that our model can accurately detect if there is a device nearby emitting UEE or not.

A Study of Speed Aware Routing for Mobile Ad Hoc Networks

The flexibility of movement for the wireless ad hoc devices, referred to as node mobility, introduces challenges such as dynamic topological changes, increased frequency of route disconnections and high packet loss rate in Mobile Ad hoc Wireless Network MANET routing. This research proposes a novel on-demand routing protocol, Speed-Aware Routing Protocol SARP to mitigate the effects of high node mobility by reducing the frequency of route disconnections in a MANET. SARP identifies a highly mobile node which forms an unstable link by predicting the link expiration time LET for a transmitter and receiver pair. NS2 was used to implement the SARP with ad hoc on-demand vector AODV as the underlying routing algorithm. Extensive simulations were then conducted using Random Waypoint Mobility model to analyze the performance of SARP. The results from these simulations demonstrated that SARP reduced the overall control traffic of the underlying protocol AODV significantly in situations of high mobility and dense networks; in addition, it showed only a marginal difference as compared to AODV, in all aspects of quality-of-service QOS in situations of low mobility and sparse networks.

Detection of RF devices based on their unintended electromagnetic emissions using Principal Components Analysis

Radio Frequency devices produce Unintended Electromagnetic Emissions (UEEs). These emissions have been found to be unique from device to device due to small differences in the physical components that make up the device. The property of uniqueness of UEE has been used to detect and identify the device producing the emission. However, UEEs are low power signals often buried within the noise band, which makes them difficult to detect. In this paper, we present a novel approach of the application of Principal Component Analysis (PCA) in detecting UEEs. UEE samples are collected from two RF devices at three different distances of 3 feet, 6 feet and 10 feet using spectrum analyzer. Our approach can detect if these low power signals are UEEs or noise. A decision table based on PCA parameters to detect UEE signals is also proposed.

Mobility and fast-fading impact study on wireless ad hoc networks

A mobile ad hoc network (MANET) protocol is affected by various environmental factors like mobility and fading, which depreciate the performance of the protocol. Though there is a commendable amount of work with different MANET protocol comparisons, seldom has there been a comparative study based on realistic simulation scenarios in order to examine the effects of network density, mobility and fading. Hence, this paper outlines a comparative study on the performance of three extensively employed reactive MANET protocols – AODV, DSR and DYMOUM. They are simulated in an environment effected by the three parameters and then thoroughly evaluated through a numerical study. The paper results in providing design considerations for the developing speed-aware routing protocol (SARP).

A Functional Data Analysis Approach to Traffic Volume Forecasting

Traffic volume forecasts are used by many transportation analysis and management systems to better characterize and react to fluctuating traffic patterns. Most current forecasting methods do not take advantage of the underlying functional characteristics of the time series to make predictions. This paper presents a methodology that uses functional principal components analysis to create high-quality online traffic volume forecasts. The methodology is validated with a data set of 1755 days of 15 min aggregated traffic volume time series. Compared with 365 randomly selected days, the functional forecasts are found to outperform traditional seasonal autoregressive integrated moving average-based methods in both count deviation and root mean squared error. In addition, through the functional data analysis approach the full exploitation of the continuous nature of the data can be achieved.

Determining optimum number of geotechnical testing samples using Monte Carlo simulations

Knowing how many samples to test to adequately characterize soil and rock units is always challenging. A large number of tests decrease the uncertainty and increase the confidence in the resulting values of design parameters. Unfortunately, this large value also adds to project costs. This paper presents a method to determine the number of samples as a function of the coefficient of variation. If, as in the case of a reliability-based design, the resistance factors are a function of the coefficient of variation of the measurements, then lowering the coefficient of variation (COV) can result in lowering of the resistance factor resulting in a less conservative design. In this study, laboratory samples were isolated by specific unified soil classification system soil type and general site location. A distribution was fitted for each of the geotechnical parameters measured. For each scenario, groups of 2, 3, 4, 5, 10, 15, 20, 30, 50, and 100 random samples were generated by using Monte Carlo simulations from the fitted distributions. For each group, the variability was calculated in terms of the COV. In all cases, the COV decreased as the sample size increased. However, the rate of decrease for the COV was the greatest at a low number of samples; it becomes increasingly smaller at a higher number of samples.