Embedded systems are increasingly defined by their networked and data-driven, computationally-intensive behavior. This includes Cyber-physical systems (CPS) and the Internet of Things (IoT), where edge computing has emerged as a way to provide scalability, latency and privacy by processing data near its source or sink. These are all examples of networks-of-systems (NoS) in which computations are distributed over clusters of resource-constrained edge devices that communicate over often open, public networks. Beyond traditional system-level design, this creates new challenges to tackle optimized mapping, offloading and scheduling of distributed applications while providing real-time, performance and other correctness guarantees. In this talk, we present our on-going work in this new era of network-level design and design automation. This first and foremost requires novel modeling approaches, including both formal models of computation and communication (MoCC) for network-level specification as well as NoS performance models and simulators. We will present a novel Reactive and Adaptive Dataflow (RADF) MoCC that addresses the problem of how to soundly express, analyze and realize deterministic real-time computation over unreliable and unpredictable networks. We will further discuss our work on fast yet accurate simulation of NoS and IoT systems. Such models can then drive automated synthesis and design space exploration of NoS applications and architectures, where we have developed associated compilers and runtime systems. We have applied such network-level design methods to specifically achieve an optimized, distributed deployment of deep learning applications on tightly constrained IoT edge clusters, which is enabled by novel partitioning and fusing methods and an adaptive work stealing middleware for dynamic workload distribution and load balancing. We will present our DeepThings framework and demonstrate results showing scalable CNN inference on a cluster of Raspberry Pi devices with less than 20MB of memory each.