Distributed heterogeneous embedded systems are increasingly prevalent in numerous applications, including automotive, avionics, smart and connected cities, Internet of Things, etc. With pervasive network access within these systems, security is a critical design concern. This dissertation presents a modeling and optimization framework for distributed, reconfigurable, and heterogeneous embedded systems. Distributed embedded systems consist of numerous interconnected embedded devices, each composed of different computing resources, such single core processors, asymmetric multicore processors, field-programmable gate arrays (FPGAs), and various combinations thereof. A dataflow-based modeling framework for streaming applications integrates models for computational latency, mixed cryptographic implementations for inter-task and intra task communication, security levels, communication latency, and power consumption. For the security model, we present a level-based modeling of cryptographic algorithms using mixed cryptographic implementations, including both symmetric and asymmetric implementations. We utilize a multi-objective genetic optimization algorithm to optimize security and energy consumption subject to latency and minimum security level constraints. The presented methodology is evaluated using a video-based object detection and tracking application and several synthetic benchmarks representing various application types. Experimental results for these design and optimization frameworks demonstrate the benefits of mixed cryptographic algorithm security model compared to single cryptographic algorithm alternatives. We further consider several distributed heterogeneous embedded systems architectures.