Optimized topology control for large-scale IoT networks using graph-based localization

Abstract Internet of Things (IoT) is increasingly realized through large scale deployments of heterogeneous devices and gateways operating under strict energy budgets and interference limited links, which motivates reliability aware topology control and end to end communication performance objectives. As IoT deployments grow to massive scales and incorporate highly heterogeneous devices, designing and controlling network topology in a reliable and energy-efficient manner becomes a fundamental challenge. In particular, poor link quality, interference, and localization uncertainty severely limit the effectiveness of traditional topology-control approaches. In this paper, we address this challenge by introducing IoTNTop, a novel and unified graph-based framework for joint localization, graph embedding, and topology control in large-scale, resource-constrained IoT networks. Unlike conventional methods that decouple localization from topology design, IoTNTop embeds both end-nodes and gateways into a globally consistent spatial structure using partial and noisy distance measurements, and directly couples this geometry with communication-aware topology optimization. IoTNTop adopts an error-centric topology-control objective that explicitly minimizes end-to-end (E2E) error probability while enforcing practical code-rate and transmit-power constraints. The framework jointly optimizes link activation, transmit power, and data transmission code rate, and employs a scalable sub-graph stitching pipeline based on eigenvector synchronization (EVS), landmark alignment (LA), and semidefinite programming (SDP) refinement. A greedy signal-to-noise-ratio (SNR)–guided edge selection strategy with convergence checking further ensures computational efficiency. Comprehensive numerical analysis and network-level simulations show IoTNTop retains approximately 60–80% of the initial per-node energy budget while maintaining symbol error probability below 15% for the majority of nodes. At the same time, it converges in fewer iterations than Genetic Algorithm (GA) and brute-force baselines and sustains higher achievable code rates at lower transmit power levels. These performance gains remain consistent across the tested signal-to-noise ratio regimes and network sizes.