Susan Lysecky

Enabling nonexpert construction of basic sensor-based systems

Technology trends have enabled deployment of low-cost sensor-based systems, but designing customized sensor-based systems to carry out specific tasks still requires costly engineering by experts. We briefly summarize eBlocks, a technology enabling nonexperts to quickly construct basic customized sensor-based systems, without requiring electronics or knowledge of programming languages. We describe experiments illustrating successful construction of Boolean sensor-based systems by novice users, focusing on intuitive logic and state block design. Additionally, we present preliminary experiments demonstrating usability of integer-based blocks and introduce a programmable block and the corresponding configuration methodology intended for nonexpert users.

Transaction-Level Modeling for Sensor Networks Using SystemC

As sensor networks are finding widespread use across many applications, designers increasingly must not only focus on application development, but also on sensor network optimizations. Given the complexities of sensor networks and the difficulty of analyzing the long-term effects of design changes within a deployed system, simulation is often the only feasible option for evaluating such optimizations. The Arizona Transaction-Level Simulator for Sensor Networks (ATLeS-SN) is a transaction-level modeling based sensor network simulation environment emphasizing modular design for modeling various components within sensor nodes and across the sensor network. We provide an overview of our proposed ATLeS-SN simulation framework and highlight the benefits of this framework for a building monitor application and a forest fire detection and propagation tracking application.

Educational Technologies for Precollege Engineering Education

Numerous efforts seek to increase awareness, interest, and participation in scientific and technological fields at the precollege level. Studies have shown these students are at a critical age where exposure to engineering and other related fields such as science, mathematics, and technology greatly impact their career goals. A variety of advanced learning technologies have emerged to enhance learning, promote hands-on experiences, and increase interest in engineering. However, creating and sustaining technology-infused learning environments at the precollege level is a challenging task, as many schools have limited resources and expertise. Moreover, while numerous technology solutions are available to support ambitious engineering-learning goals, choosing the right technology to align to program goals and resources may be a daunting task. In this work, we fill the gap between the applicability of educational implements and suitable teaching methods for precollege engineering. We present an overview of available hardware- and software-based technologies, and characterize these technologies based on criteria such as median price, the type of learning activities fostered, and the required users expertise levels. In addition, we outline how these technologies align with deductive and inductive teaching methods that emphasize direct-instruction, inquiry-, problem-, and project-based methods, as studies have shown these methods are effective for precollege engineering education.

Automated application-specific tuning of parameterized sensor-based embedded system building blocks

We previously developed building blocks to enable end-users to construct customized sensor-based embedded systems to help monitor and control a users environment. Because design objectives, like battery lifetime, reliability, and responsiveness, vary across applications, these building blocks have software-configurable parameters that control features like operating voltage, frequency, and communication baud rate. The parameters enable the same blocks to be used in diverse applications, in turn enabling mass-produced and hence low-cost blocks. However, tuning block parameters to an application is hard. We thus present an automated approach, wherein an end-user simply defines objectives using an intuitive graphical method, and our tool automatically tunes the parameter values to those objectives. The automated tuning improved satisfaction of design objectives, compared to a default general-purpose block configuration, by 40% on average, and by as much as 80%. The tuning required only 10-20 minutes of end-user time for each application.

Evaluation of Dynamic Profiling Methodologies for Optimization of Sensor Networks

To reduce the complexity associated with application-specific tuning of sensor-based systems, dynamic profiling enables an accurate view of the application behavior, such that the network can be reoptimized at runtime in response to changing application behavior or environmental conditions. However, dynamic profiling must be able to accurately capture application behavior without incurring significant runtime overheads. We present several profiling methods for dynamically monitoring sensor-based platforms and analyze the associated network traffic, energy, and code impacts.

Online algorithms for wireless sensor networks dynamic optimization

Technological advancements in wireless communications and embedded systems have led to the proliferation of wireless sensor network (WSN) applications, each with varying application requirements (i.e., lifetime, throughput, reliability, etc.). Sensor node tunable parameters enable WSN designers to specialize/tune a sensor node to meet application requirements, but however, parameter tuning is a challenging process that requires designer expertise to consider sensor node complexities and changing environmental stimuli. In this paper, we develop lightweight, online optimization algorithms for sensor node parameter tuning, which enables dynamic optimizations to meet application requirements and adapt to changing environmental stimuli. Results reveal that our online optimizations quickly converge to a near optimal solution using minimal computational and storage resources, and are thus amenable for implementation on resource and energy-constrained sensor nodes.

A lightweight dynamic optimization methodology for wireless sensor networks

Technological advancements in embedded systems due to Moores law have lead to the proliferation of wireless sensor networks (WSNs) in different application domains (e.g. defense, health care, surveillance systems) with different application requirements (e.g. lifetime, reliability). Many commercial-off-the-shelf (COTS) sensor nodes can be specialized to meet these requirements using tunable parameters (e.g. voltage, frequency) to specialize the operating state. Since a sensor nodes performance depends greatly on environmental stimuli, dynamic optimizations enable sensor nodes to automatically determine their operating state in-situ. However, dynamic optimization methodology development given a large design space and resource constraints (memory and computational) is a very challenging task. In this paper, we propose a lightweight dynamic optimization methodology that intelligently selects initial tunable parameter values to produce a high-quality initial operating state in one-shot for time-critical or highly constrained applications. Further operating state improvements are made using an efficient greedy exploration algorithm, achieving optimal or near-optimal operating states while exploring only 0.04% of the design space on average.

A First Step Towards Dynamic Profiling of Sensor-Based Systems

Application specific tuning has been shown to be beneficial for a variety of platforms, sensor-based systems are no exception. However, accurately capturing external stimuli or modeling application specific stimuli remains a challenge. Tuning a sensor-based system to erroneous or incomplete application information can limit the optimization achieved or even negatively impact the resulting system performance. We propose to obtain accurate application stimuli by dynamically observing the sensor-based system in the intended deployment location, storing application characteristics for later analysis or optimization.

Adapting the eBlock Platform for Middle School STEM Projects: Initial Platform Usability Testing

The benefits of project-based learning environments are well documented; however, setting up and maintaining these environments can be challenging due to the high cost and expertise associated with these platforms. To alleviate some of these roadblocks, the existing eBlock platform which is composed of fixed function building blocks targeted to enable nonexperts users to easily build a variety of interactive electronic systems is expanded to incorporate newly defined integer-based building blocks to enable a wider range of project possibilities for middle school STEM projects. We discuss various interface possibilities, including initial usability experiments, and summarize our overall experiences and observations in working with local middles school students utilizing the eBlock platform.

Non-expert construction of customized embedded systems to enhance STEM curricula

We present an interactive platform to enhance STEM education through project-based activities that complement the existing curriculum, while drawing on problem solving skills and team work. The addition of hands-on projects is intended not only to reinforce the various concepts learnt through normal class readings and discussion, but also make these topics multi-dimensional by enabling students to apply these principles in their everyday surroundings. The types of courses that can take advantage of the eBlock platform range from middle school math and science classes to university level engineering courses. In this paper we present the eBlocks platform which can be utilized to implement a wide variety of projects without requiring users to have programming or electronics experience. Specifically, we summarize the preliminarily usability results, discuss possible applications of the eBlock platform for various age groups, and introduce the initial curriculum development for middle school audiences.