The use of examples in idea generation is a common practice intended to provide inspiration from existing products to the designing of new ones. Examples can be taken from the Internet, engineering textbooks, analogical databases, literature, a company’s prior designs, or from a competitor company, prior work by the designer, and many other sources. These examples are represented in various ways, such as hand sketches, pictures, computer-aided designs (CAD), physical models, activity diagrams, shape grammars, text descriptions, etc. Design representations can also be broken down by function in the form of functional models and decompositions. The use of these visual or physical examples allows engineers to get a clearer picture of how a design or component works and enables them to have a better understanding of the overall design and function. Each representation has inherent advantages and disadvantages in the way that they portray a design. Examples are sources for analogies. Analogies from nature, where biological organisms have solved challenging problems in novel ways, are very useful in engineering idea generation and solution retrieval. This process is called biologically inspired design. Engineers often use biologically inspired design to solve problems while increasing creativity and expanding the solution space. Using this method, engineers are able to learn from nature and apply biological principles to real world engineering problems to make effective designs and produce innovative solutions. It is important to have a clearer understanding of how the use of the representations and characteristics of examples as external stimuli affect the idea generation process in engineering design. Understanding these processes will be invaluable in offering guidelines for how engineering design should be done and what types of external stimuli should be used to allow for innovation and creativity to be enhanced. This dissertation presents four studies that focus on understanding ways that examples can be used to improve the idea generation process. Three of these studies focus on how the representation of externally imposed examples, which may be used as analogues, influences creativity during idea generation while also minimizing design fixation, which occurs when designers adhere to the features of their own initial design solutions or to features of existing examples. The fourth study focuses on the use of examples as sources for analogical mapping and how these examples produce innovative solutions during idea generation. The first study compares CAD, sketch, and photograph representation presented individually. The second study compares CAD and sketch representation presented together, and the third study examines function tree and sketch representations. The fourth study looks at the real-world context and impact of examples used as sources for analogical mapping to inspire innovative solutions. The results of the studies show that CAD representations of good examples are effective in allowing engineers to identify the key working principles of a design and help to develop higher quality design concepts. CAD representations also cause more fixation to the example’s features. Function trees do not cause nor break fixation compared to a control condition, but do reduce fixation compared with sketches. Biological examples can be successfully used as analogues during engineering idea generation to create novel and effective design solutions to relevant and real-world engineering problems.

The essence of smart pervasive Cyber-Physical Environments (CPEs) is to enhance the dependability, security and efficiency of their encompassing systems and infrastructures and their services. In CPEs, interactive information resources are integrated and coordinated with physical resources to better serve human users. To bridge the interaction gap between users and the physical environment, a CPE is instrumented with a large number of small devices, called sensors, that are capable of sensing, computing and communicating. Sensors with heterogeneous capabilities should autonomously organize on-demand and interact to furnish real-time, high fidelity information serving a wide variety of user applications with dynamic and evolving requirements. CPEs with their associated networked sensors promise aware services for smart systems and infrastructures with the potential to improve the quality of numerous application domains, in particular mission-critical infrastructure domains. Examples include healthcare, environment protection, transportation, energy, homeland security, and national defense. To build smart CPEs, Networked Sensor Environments (NSEs) are needed to manage demand-driven sharing of large-scale federated heterogeneous resources among multiple applications and users. We informally define NSE as a tailorable, application agnostic, distributed platform with the purpose of managing a massive number of federated resources with heterogeneous computing, communication, and monitoring capabilities. We perceive the need to develop scalable, trustworthy, cost-effective NSEs. A NSE should be endowed with dynamic and adaptable computing and communication services capable of efficiently running diverse applications with evolving QoS requirements on top of federated distributed resources. NSEs should also enable the development of applications independent of the underlying system and device concerns. To our knowledge, a NSE with the aforementioned capabilities does not currently exist. The large scale of NSEs, the heterogeneous node capabilities, the highly dynamic topology, and the likelihood of being deployed in inhospitable environments pose formidable challenges for the construction of resilient shared NSE platforms. Additionally, nodes in NSE are often resource challenged and therefore trustworthy node cooperation is required to provide useful services. Furthermore, the failure of NSE nodes due to malicious or non-malicious conditions represents a major threat to the trustworthiness of NSEs. Applications should be able to survive failure of nodes and change their runtime structure while preserving their operational integrity. It is also worth noting that the decoupling of application programming concerns from system and device concerns has not received the appropriate attention in most existing wireless sensor network platforms. In this dissertation, we present a Biologically-inspired Secure Elastic Networked Sensor Environment (BioSENSE) that synergistically integrates: (1) a novel bio-inspired construction of adaptable system building components, (2) associative routing framework with extensible adaptable criteria-based addressing of resources, and (3) management of multi-dimensional software diversity and trust-based variant hot shuffling. The outcome is that an application using BioSENSE is able to allocate, at runtime, a dynamic taskforce, running over a federated resource pool that would satisfy its evolving mission requirements. BioSENSE perceives both applications and the NSE itself to be elastic, and allows them to grow or shrink based upon needs and conditions. BioSENSE adopts Cell-Oriented-Architecture (COA), a novel architecture that supports the development, deployment, execution, maintenance, and evolution of NSE software. COA employs mission-oriented application design and inline code distribution to enable adaptability, dynamic re-tasking, and re-programmability. The cell, the basic building block in COA, is the abstraction of a mission-oriented autonomously active resource. Generic cells are spontaneously created by the middleware, then participate in emerging tasks through a process called specialization. Once specialized, cells exhibit application specific behavior. Specialized cells have mission objectives that are being continuously sought, and sensors that are used to monitor performance parameters, mission objectives, and other phenomena of interest. Due to the inherent anonymous nature of sensor nodes, associative routing enables dynamic semantically-rich descriptive identification of NSE resources. As such, associative routing presents a clear departure from most current network addressing schemes. Associative routing combines resource discovery and path discovery into a single coherent role, leading to significant reduction in traffic load and communication latency without any loss of generality. We also propose Adaptive Multi-Criteria Routing (AMCR) protocol as a realization of associative routing for NSEs. AMCR exploits application-specific message semantics, represented as generic criteria, and adapts its operation according to observed traffic patterns. BioSENSE intrinsically exploits software diversity, runtime implementation shuffling, and fault recovery to achieve security and resilience required for mission-critical NSEs. BioSENSE makes NSE software a resilient moving target that : 1) confuses the attacker by non-determinism through shuffling of software component implementations; 2) improves the availability of NSE by providing means to gracefully recover from implementation flaws at runtime; and 3) enhances the software system by survival of the fittest through trust-based component selection in an online software component marketplace. In summary, BioSENSE touts the following advantages: (1) on-demand, online distribution and adaptive allocation of services and physical resources shared among multiple long-lived applications with dynamic missions and quality of service requirements, (2) structural, functional, and performance adaptation to dynamic network scales, contexts and topologies, (3) moving target defense of system software, and (4) autonomic failure recovery. / Ph. D.

Biologically inspired design (BID) is a widespread and growing movement in modern design, pulled in part by the need for environmentally sustainable design and pushed partly by rapid advances in biology and the desire for creativity and innovation in design. Yet, our current understanding of cognition in BID is limited and at present there are few computational methods or tools available for supporting its practice. In this dissertation, I develop a cognitive model of BID, build computational methods and tools for supporting its practice, and describe results from deploying the methods and the tools in a Georgia Tech BID class. One key and novel finding in my cognitive study of BID is the surprisingly large degree to which biological analogues influence problem formulation and understanding in addition to generation of design solutions. I call the process by which a biological analogue influences the evolution of the problem formulation analogical problem evolution. I use the method of grounded theory to develop a knowledge schema called SR.BID (for structured representations for biologically inspired design) for representing design problem formulations. I show through case study analysis that SR.BID provides a useful analytic framework for understanding the two-way interaction between problems and solutions. I then develop two tools based on the SR.BID schema to scaffold the processes of problem formulation and analogue evaluation in BID. I deployed the two tools, the four-box method of problem specification and the T-chart method of analogical evaluation, in a Georgia Tech BID class. I show that with minimal training, the four-box method was used by students to complete design problem specifications in 2011 and 2012 with 75% of students achieving better than 80% accuracy. Finally I describe a web-based application for interactively supporting BID practice including problem formulation and analogue evaluation. Thus, my dissertation develops a cognitive model of analogical problem evolution in BID, a knowledge schema for representing problem formulations, a computational technique for evaluating biological analogues, and an interactive web-based tool for supporting BID practice. Through a better cognitive understanding of BID and computational methods and tools for supporting its practice, it also contributes to computational creativity.

Biologically inspired design is used as an approach for sustainable engineering. Taking a biologically inspired approach, one abstracts ideas and principles from nature, an inherently sustainable system, and uses them in engineering applications with the goal of producing environmentally superior designs. One such biological idea with potential environmental benefits for engineering is microscale and nanoscale surface roughness found on the Lotus plant and many other surfaces in nature. These surfaces repel water and aid in contaminant removal; this self-cleaning phenomenon is called the "Lotus Effect," in honor of the plant first observed to exhibit it. The structures responsible for the Lotus Effect inspired research and development of many technologies capable of creating hydrophobic, self-cleaning surfaces, and many potential self-cleaning surface applications exist beyond nature's intended application of cleaning. While statements have been made about the environmental benefits of using a self-cleaning surface, only limited scientific data exist. Artificial self-cleaning surfaces are successfully cleaned using fog or mist. This shows that such surfaces can be cleaned with less energy and water intensive methods than the more conventional methods used to clean regular surfaces, such as spray or solvent cleaning. This research investigates the potential environmental burden reductions associated with using these surfaces on products. A life cycle assessment is performed to determine the environmental burdens associated with manufacturing a self-cleaning surface, for three production methods: a chemical coating, a laser ablated steel template, and an anodized aluminum template. The environmental benefits and burdens are quantified and compared to those of more conventional cleaning methods. The results indicate that self-cleaning surfaces are not necessarily the environmentally superior choice.

Biological organisms, phenomena and strategies, herein referred to as biological systems, provide a rich set of analogies that can be used to inspire engineering innovation. Biologically-inspired, or biomimetic, designs are publicly viewed as creative and novel solutions to human problems. Moreover, some biomimetic designs have become so commonplace that it is hard to image life without them (e.g. velcro, airplanes). Although the biologically- inspired solutions are innovative and useful, the majority of inspiration taken from nature has happened by chance observation, dedicated study of a specific biological entity (e.g., gecko), or asking a biologist to explain the biology in simple terms. This reveals a fundamental problem of working across the engineering and biological domains. The effort and time required to become a competent engineering designer creates significant obstacles to becoming sufficiently knowledgeable about biological systems (the converse can also be said). This research aims to remove the element of chance, reduce the amount of time and effort required to developing biologically-inspired solutions, and bridge the seemingly immense disconnect between the engineering and biological domains. To facilitate systematic biologically-inspired design, a design methodology that relies on a framework of tools and techniques that bridge the two domains is established. The design tools and techniques that comprise the framework achieve: Identification of relevant biological solutions based on function; translation of identified biological systems of interest; functional representation of biological information such that it can be used for engineering design activities; and conceptualization of biomimetic engineering designs. Using functional representation and abstraction to describe biological systems presents the natural designs in an engineering context and allows designers to make connections between biological and engineered systems. Thus, the biological information is accessible to engineering designers with varying biological knowledge, but a common understanding of engineering design methodologies. This work has demonstrated the feasibility of using systematic design for the discovery of innovative engineering designs without requiring expert-level knowledge, but rather broad knowledge of many fields. / Graduation date: 2011