ABSTRACT Software‐Defined Networking (SDN) has developed as a suitable solution for complex networks with a flexible and programmable networking paradigm. SDN infrastructure is facing critical challenges in efficient routing under dynamic and congestion‐prone conditions. This research paper proposes an efficient Improved Graph Neural Network (IGNN)‐based routing optimization mechanism designed for achieving congestion‐aware traffic management in SDN environments. The proposed model uses the inherent graph structure of network topology by representing switches as nodes and communication links as edges, and this mode is utilizing real‐time network metrics such as bandwidth, delay, queue length, and packet loss as feature inputs. A deep GNN framework is used for learning topology‐aware node by incorporating through message passing, and this will enable accurate prediction of optimal routing paths under varying traffic conditions. Unlike conventional GNN‐based routing approaches that primarily learn topology‐aware node representations through graph message passing, the proposed IGNN integrates topology representation with congestion prediction and multi‐objective routing optimization considering throughput, packet delay, packet loss, and congestion utilization, enabling congestion‐aware adaptive path selection. The suggested model makes use of congestion‐aware cost functions for low‐latency and high‐throughput routing and dynamically updates SDN flow rules using GNN predictions. The experimental evaluation conducted using a simulated SDN environment demonstrates significant performance improvements compared to traditional routing protocols and baseline machine learning approaches. The proposed method achieves approximately 23–31% reduction in end‐to‐end delay, 18–22% decrease in packet loss, and 27–33% improvement in throughput. The IGNN approach presented is a good solution to optimize SDN routing through congestion awareness.
ABSTRACT Epistemic orphaning, responsibility drift, loss of institutional memory, and non‐stationarity are not new problems in infrastructure governance—they have accompanied long‐life infrastructure throughout its history. What artificial intelligence changes is not the existence of these problems but their scale, opacity, and the structural difficulty of recognizing them once they have taken hold. This paper argues that AI in long‐life infrastructure should be understood not primarily as a source of new risk but as a magnifier of existing governance vulnerabilities, demanding a more deliberate and durable framework for stewardship than the field has previously required. Using a double bow‐tie governance framework applied to an integrated urban subway system, the paper develops three distinct governance regimes: a construction regime concerned with knowledge creation, a stewardship transition regime concerned with knowledge continuity, and a long‐term operations regime concerned with knowledge interpretation and renewal. Construction and operations are two fundamentally different regimes, not two phases of one system, connected by a stewardship transition that is the highest governance risk point in the lifecycle. Three interlocking concepts structure the analysis: normalization of plausibility , the characteristic failure mode of construction, in which AI outputs appear credible even when their underlying assumptions are wrong; epistemic orphaning , the failure mode of the stewardship transition, in which records and AI outputs survive but the context required to interpret them is permanently lost; and normalization of deviance , the failure mode of long‐term operations, in which gradual drift from design conditions goes unrecognized because each step looks acceptable relative to the last. The framework introduces two governance innovations absent from current infrastructure practice: the AI Steward , an organizational role responsible for the interpretability and accountability of AI‐generated knowledge across the asset's operational life; and model reconciliation and arbitration , a formal function for resolving conflicts between construction‐phase AI predictions and operational observations. Together these constitute a stewardship architecture designed to keep accountability aligned with AI influence across the infrastructure lifecycle. Long‐life infrastructure is not simply built and operated; it is inherited. In the age of artificial intelligence, governing what is inherited may prove more important than governing how it is created.
ABSTRACT Addressing the global challenge of climate change is a priority for all countries, including developing countries. Within this context, this study aims to examine the effects of transport infrastructure, ICT, and industrialization on carbon emissions in the Southern African region. Our analysis covers 16 countries over the period 2004–2023, with a key focus on Sustainable Development Goal 13. To this end, we employ the PSCE model as the primary model and the FGLS estimator for robustness analysis. The empirical results highlight a positive impact of transport infrastructure, ICT, and industrialization on CO 2 emissions. The causality test reveals a bidirectional causal link running from transport infrastructure and ICT to CO 2 emissions. From a policy perspective, SADC countries are encouraged to strengthen their environmental policies and promote low‐carbon transport modes aligned with sustainable development objectives.
ABSTRACT The pervasive embedding of Artificial Intelligence (AI) in urban infrastructures (producing embedded, socio‐technical systems that intermediate knowledge, agency, and material flows) poses substantial challenges to accountability, equity, and ecological sustainability. This theoretical article argues that existing governance architectures are ill‐equipped to address the epistemic risks (e.g., algorithmic opacity, embedded biases, marginalization of local knowledges) inherent in such AI‐intermediated urban systems. We build a polycentric governance framework grounded in the principles of epistemic justice by design, environmental sustainability as the primary objective, polycentric accountability, infrastructural public ownership and stewardship, and anticipatory governance reflexivity. This architecture is proposed to assist urban designers, policymakers, and other stakeholders in steering the embedding of AI towards health equity‐enabling and environmentally sustainability‐enabling outcomes.
