As the Internet of Things (IoT) has brought upon new communication patterns and challenges, Information-Centric Networking (ICN) has been touted as a potential solution. To confirm that hypothesis, the fundamental issue of routing and forwarding in the ICN-IoT must be addressed. This thesis investigates this topic across the IoT architecture.First, a scheme to securely forward ICN interests packets based on geographic coordinates is proposed for low-power wireless sensor networks (WSN). Its efficiency is compared to an optimized flooding-based scheme similar to current ICN-WSN approaches in terms of deployability and scalability using an analytical model. Realistic data for the model is derived from a mixture of simulation, literature study, and experiments on state-of-the-art sensor boards. Geographic forwarding is shown to halve the memory footprint of the ICN stack on reference deployments and to yield significant energy savings, especially for dynamic topologies. Second, ICN is used to enhance admission control (AC) to fixed-capacity Edge-computing platforms to guarantee request-completion time for latency-constrained applications. The LRU-AC, a request-aware AC strategy based on online learning of the request popularity distribution through a Least-Recently-Used (LRU) filter, is proposed. Using a queueing model, the LRU-AC is shown to decrease the number of requests that must be offloaded to the Cloud. An implementation of the LRU-AC on FPGA hardware is then proposed, using Ageing Bloom Filters (ABF) to provide a compact memory representation. The validity of using ABFs for the LRU-AC is proven through analytical modelling. The implementation provides high throughput and low latency.Finally, the management and virtualization of ICN-IoT networks are considered.vICN (virtualized ICN), a unified intent-based framework for network configuration and management that uses recent progress in resource isolation and virtualization techniques is introduced. It offers a single, flexible and scalable platform to serve different purposes, ranging from reproducible large-scale research experimentation to demonstrations with emulated and/or physical devices and network resources and to real deployments of ICN in existing IP networks.