Jon Wade

Citizens as sensors: The cognitive city paradigm

Intelligent/smart cities have been touted as one of the solutions to the challenges of ever increasing complexities faced by present and future urban environments. Leveraging information technology to allow better demand and supply management of key urban infrastructure system offer immense opportunities for efficiency and quality of service improvements in urban services. In this paper, we argue that implementing sensor networks are a necessary but not sufficient approach to improving urban living. Building on the concept of the cognitive city discussed elsewhere by the authors, this paper proposed an architectural process approach that allows city decision-makers and service providers to integrate cognition into urban processes. Cognition is the ability of a system to learn from previous experiences and adapt its behavior based on them. A cognitive system is able to sense, perceive and respond to changes in the environment and can therefore improve a systems performance by increasing its adaptive capacity.

Designing an Experiential Learning Environment for Logistics and Systems Engineering

Systems engineering increasingly addresses the system lifecycle, as opposed to its more traditional role focusing on design and development. This new situation results in part from the recognition that upstream design and deployment decisions have potentially significant cost and performance implications post-deployment. For military systems, the role that typically addresses post-deployment issues is the logistician. Over the system lifecycle, it is important that the traditional roles of systems engineer and logistician understand issues faced by one another, as well as joint cost and performance implications. This paper presents the design of a role-based experiential learning environment for logisticians involved in military sustainment. This design leverages the generic components of an existing single-learner technology base, the Experience Accelerator, for presenting and controlling the learner experience, plus simulating program outcomes resulting from learner decisions. This technology base has been used to create a learning experience for a lead systems engineer in charge of designing and developing a new unmanned aerial vehicle (UAV) system. In this new environment, the logistician learner interacts with systems engineers during UAV system acquisition and sustainment, learns about systems engineering issues and their effect on logistics, tries to influence upstream systems engineering decisions, and also performs logistics functions.

Year one of the systems engineering experience accelerator

Abstract Systems engineering educators are struggling to meet the workforce development demand for senior systems engineers. Systems engineers are critical for addressing a broad set of increasingly complex systems problems faced by industry and government. However, the discipline is experiencing an outflux of senior systems engineers reaching retirement age with no ready source of systems engineers available to replace them, at a time when the demand for systems engineers is increasing (NDIA, 2010). The workforce challenge is to shorten the time it takes for a systems engineer to reach the senior level. The Systems Engineering Experience Accelerator (SEEA) research project was conceived as a critical response to these needs and challenges. The SEEA focuses on a solution that leverages technology to create an experience intended to accelerate the learning of systems engineering related competencies. This paper summarizes the operation of the preliminary version of a prototype SEEA simulator after the first year of the SEEA project. A review of the plans for future research is also included.

Ecosystem requirements for composability and reuse: An investigation into ecosystem factors that support adoption of composable practices for engineering design

Composability is a systems architecture and design concept focusing on composing new systems from known components, designs, product lines, and reference architectures as opposed to focusing on “blank sheet” designs based on requirements decomposition alone. The concept of composability has been a goal of the US Department of Defense (DoD) for many years, most recently taking the form of Platform-based Engineering. Despite this focus, the goal of effective modularity and design reuse has been somewhat elusive in the aerospace and defense sectors. This paper describes an ecosystem construct which incorporates market factors, business practices, and trends that occur in industries where composability and reuse have taken hold in order to identify a path forward for effective adoption of composability in the aerospace and defense marketplace. A number of examples of composable design are described, followed by proposals for necessary changes within the DoD ecosystem to facilitate its support.

Applying Composable Architectures to the Design and Development of a Product Line of Complex Systems

This paper investigates a composable design methodology leveraging SysML to manage mission flexible product lines, and reviews the application of this methodology to a spacecraft product line. This methodology extends the SysML language with a mathematical and Boolean constraint language allowing for the capture of product line rules as an alternative to a more traditional variation tree. Finally, this paper reviews future work underway to extend this methodology.

Multi-criteria simulation of program outcomes

Programs that develop and deploy complex systems typically have multiple criteria by which they are judged to be successful. Categories of such criteria include schedule, cost, technical system performance, quality and customer expectations. Criteria are operationalized via particular metrics, and often there are complex relationships between metrics, e.g., correlations or trade-offs. In an acquisition program, it is critical that systems engineers understand the implications of their actions and decisions with respect to these metrics, since the metrics are used to report the performance and eventual outcome of the program. However, such understanding usually takes many years of on-the-job experience. This paper describes an approach to simulation modeling of program behavior and performance whereby program outputs expressed in these metrics can be studied by systems engineers. An example program simulation model is presented that currently is used in an educational technology system for training systems engineers. The decisions and actions that can be taken by a systems engineer are described, and the impacts of various actions and decisions on program metrics and metric relationships are illustrated. The model is validated via subject matter experts with extensive experience in the program domain.

Future Systems Engineering Research Directions

This paper describes a program organized by the INCOSE Academic Council to determine future directions in systems engineering (SE) research. This program, consisting of three collaborative workshops, uses a framework coupling societal needs to systems challenges and then to gaps in the capabilities of SE, which inform the direction of future SE research. The results of the first workshop are presented including a description of the Grand Challenges in five selected areas, namely, societal needs, problem definitions, desired results, obstacles, and related research questions. This paper concludes with a summary and description of the future work for this program.

SEEA: Accelerated Learning and Learning Assessment for Systems Engineering Education

The Experience Accelerator is a new approach to developing the systems engineering and technical leadership workforce, aimed at accelerating experience assimilation through immersive, simulated learning situations where learners solve realistic problems. A prototype technology infrastructure and experience content has been developed, piloted, and evaluated. This paper discusses the use of the technology in the systems engineering education domain. An evaluation of the learning potential is presented utilizing the data collected from a pilot application of the prototype in an undergraduate course on project management. Finally, a summary is provided with a description of future work.

Self-Organizing Cooperative Dynamics in Government Extended Enterprises

This paper presents the results of our research into cooperation in Government Extended Enterprises, a type of system of systems. The effort proposed and evaluated a novel theory that these decisions are the result of the interaction of four canonical forces—Sympathy, Trust, Fear, and Greed. A computational simulation involving the Stag Hunt game examined information sharing decisions in a series of key decision points in three large case studies. For the five hypotheses tested, exploratory data analysis and nonparametric statistical testing show strong support for three of the hypotheses (cooperation is positively correlated with actors’ levels of Sympathy and Trust and negatively correlated with actors’ levels of Fear) and moderate support for the fourth (cooperation is negatively correlated with actors’ levels of Greed). Indications are that the fifth hypothesis (cooperation is correlated with history of behavior) is not needed to explain observed behavior. Multiple correspondence analysis showed significant interactions both among pairs of forces and when a force is paired with decision making strategies. These results can form the basis for: 1) analysis of additional case studies; 2) development of an agent-based simulation; and 3) creation of training programs for current and future organizational leaders.

Dynamic Complexity Measures for Use in Complexity-Based System Design

Difficulty predicting system behaviors introduces a certain level of complexity to a system design. The INCOSE systems engineering handbook indicates that system complexity is one of the seven key challenges influencing development when engineering a system of systems. The scope of this paper is first to survey systems engineering relevant definitions of complexity for latter application to the complexity evaluation framework. The literature search also includes state-of-the-art works on system complexity measurement. Before proposing new techniques, the current complexity-based system, and interface measurement and design techniques are explored. As the state-of-the-art only includes static/structural complexity quantification, entropy-based measures for dynamic complexity quantification are proposed. A sample system is evaluated using the proposed dynamic complexity measures and the results are discussed. The methods proposed herein provide a first step in the path to an enhanced system/interface complexity evaluation framework using dynamic complexity measures.