Probabilistic and Information Theoretic Enabled Secret Key Generation in Wireless Ad Hoc Networks

Abstract

This paper targets wireless ad hoc networks (WANETs) as the main domain and focuses on the sub-domain of secret key generation (SKG) from wireless channels. We consolidate probabilistic and information-theoretic foundations with deployable physical-layer techniques that exploit channel reciprocity, including RSS/CSI/phase features, UWB links, and MIMO-OFDM (plus long-range LoRa), and evaluate them using key rate, key-error-rate, and entropy. We then cover special methods that strengthen SKG pipelines: reciprocity calibration for TDD/FDD, adaptive quantization, code-based reconciliation (polar/lattice), privacy amplification via universal hashing, deep-learning aids for feature extraction and reconciliation, and IRS-assisted links to boost randomness under mobility. Building on 120 papers, we structure the literature into four 30-paper chunks with summaries (techniques, pros/cons, limitations, applications) and per-chunk best-use-case guidance, supported by explicit inclusion/exclusion criteria. Finally, we propose a practical SKG workflow for WANETs: (i) channel measurement & reciprocity calibration, (ii) adaptive quantization, (iii) information reconciliation with capacity-approaching codes, (iv) privacy amplification to bound leakage, and (v) scenario-specific deployment for IoT/WBAN/VANET. This yields a concise playbook that links rigorous secrecy guarantees to fieldable designs.