Software-defined networking (SDN) has received a great deal of attention from both academia and industry in recent years. Studies on SDN have brought a number of interesting technical discussions on network architecture design, along with scientific contributions. Researchers, network operators, and vendors are trying to establish new standards and provide guidelines for proper implementation and deployment of such novel approach. It is clear that many of these research efforts have been made in the southbound of the SDN architecture, while the northbound interface still needs improvements. By focusing in the SDN northbound, this paper surveys the body of knowledge and discusses the challenges for developing SDN software. We investigate the existing solutions and identify trends and challenges on programming for SDN environments. We also discuss future developments on techniques, specifications, and methodologies for programmable networks, with the orthogonal view from the software engineering discipline.

Software-Defined Networking (SDN) has been receiving a great deal of attention from both academic and industry communities. One reason to this interest is that SDN enables the network programmability, through an external controller, which supports applications and policies built from SDN programming languages, thus breaking the traditional bind between control and data plane. Nevertheless, the application development in this context is still complex for such recent technology. Moreover, there is a strong need for methodologies and tools that explore the abstraction levels potentials supported by SDN. This paper presents a new approach based on the Model-Driven Engineering (MDE) paradigm, called Model-Driven Networking (MDN). MDN relies on a Domain-Specific Modelling Language (DSML) to create SDN applications. We argue that MDN raises the level of abstraction on development, thus reducing the complexity to implement SDN applications and avoiding inconsistent policies. In order to show the relevance and the technological viability of our proposal, we have specified a DSML and have built a tool for creating SDN applications using the MDN approach.

The accuracy of machine learning models in plant disease detection significantly relies on large volumes of knowledge-based labeled data; the acquisition of annotation remains a significant bottleneck in domain-specific research such as plant disease detection. While unsupervised learning alleviates the need for labeled data, its effectiveness is constrained by the intrinsic separability of feature clusters. These limitations underscore the need for approaches that enable supervised early disease detection without extensive annotation. To this end, we propose a self-supervised learning (SSL) framework for the early detection of soybean’s sudden death syndrome (SDS) using hyperspectral data acquired from an unmanned aerial vehicle (UAV). The methodology employs a novel distance-based spectral pairing technique that derives intermediate labels directly from the data. In addition, we introduce an adapted contrastive loss function designed to improve cluster separability and reinforce discriminative feature learning. The proposed approach yields an 11% accuracy gain over agglomerative hierarchical clustering and attains both classification accuracy and F1 score of 0.92, matching supervised baselines. Reflectance frequency analysis further demonstrates robustness to label noise, highlighting its suitability in label-scarce settings.

Software-Defined Networking (SDN) enables applications running on its control plane. The Northbound API allows programmers to develop SDN applications for a number of policy-based network management tasks. However, there is still a clear need for supporting the development of controller-agnostic modeled applications. In this paper, we show how the Model-Driven Networking (MDN), a framework composed of CASE tool and Domain-Specific Modeling Language (DSML), can be a feasible solution to create applications independent from controllers and to enable proper verification of SDN applications. Our evaluation demonstrates that MDN framework is viable for using in real scenarios and independent from SDN controllers. Moreover, our performance tests show that: (i) MDN's code generation is two times faster than other approaches; and (ii) it can validate several constraints and complex topologies at millisecond-timescale.

Geospatial Artificial Intelligence (GeoAI) and data processing techniques have significantly advanced object detection, prediction, and classification tasks. However, the availability of machine learning-ready, labeled data for specific applications such as plant disease detection remains the major challenge for the broader adoption of GeoAI. For instance, collecting temporal UAV imagery of agricultural crops to track disease emergence and progress requires substantial human labor and resources, which is often limited to a small spatial scale. Recognizing the pivotal role of temporal data in pattern recognition, object detection, and scene reconstruction, we introduce an innovative approach to augment multispectral temporal datasets: the Geospatial Time Machine. Our proposed methodology combines Graph Neural Network (GNN) and Generative Adversarial Network (GAN) architectures to generate comprehensive synthetic temporal data encompassing multivariate time series. The results demonstrate that imagery generated through backcasting can enhance the accuracy of downstream classification tasks by up to 53% in plant disease detection, particularly in the initial stages of analyzing a crop growth using multispectral and multitemporal datasets.