Entropy-guided access point selection slashes Wi-Fi fingerprint size by up to 95 percent

Every modern building is saturated with Wi-Fi signals. Walk through an office tower, a hospital, or a university campus, and your smartphone silently detects dozens, sometimes hundreds, of wireless access points broadcasting from routers mounted on ceilings and hidden behind walls. For years, engineers have exploited this ambient radio landscape to solve one of the most stubborn problems in mobile computing: figuring out where a person is standing when satellite signals cannot reach indoors. Now a research team from Mansoura University and the Military Technical College in Egypt has shown that the vast majority of those detected signals are dead weight, and that a clever application of a classic information-theory concept can throw away up to 95 percent of them without sacrificing positioning accuracy.

The technique, described in Scientific Reports, targets Wi-Fi fingerprinting, the dominant approach to indoor localization. Fingerprinting works by treating the pattern of received signal strength indicator (RSSI) values from all detectable access points as a unique signature of a physical location, much like a radio shadow cast by the surrounding infrastructure. A device measures which routers it can hear and how strongly, then matches that pattern against a pre-built database to estimate its position. The method requires no additional hardware and runs on commodity phones, which is precisely why it remains the most deployment-ready positioning technology available. But it carries a hidden cost: modern environments expose tens to hundreds of access points, producing high-dimensional, sparse fingerprints riddled with missing values that burden mobile processors, slow down scanning, and can degrade generalization when redundant or unstable signals creep into the training data.

The Egyptian team, led by Ahmed Y. Bahy El-Din, attacked this dimensionality problem with Shannon entropy, the foundational measure of information content introduced by Claude Shannon in 1948. For each access point, the researchers computed the entropy of its empirical RSSI distribution, discretizing observed signal values into bins and calculating how spread out the probability mass is. The logic is elegant: a router whose signal strength varies richly across different physical locations carries discriminative spatial information, while a router broadcasting a near-constant profile everywhere yields almost no positioning cues and merely injects noise into the fingerprint matrix. High-entropy access points earn their place in the feature set; low-entropy ones get pruned.

What distinguishes this work from earlier entropy-based filtering is its adaptivity. Previous schemes applied static percentile thresholds or fixed global cutoffs that ignored the reality that signal statistics differ dramatically from floor to floor. A conservative threshold preserves accuracy on sparse floors but fails to compress well-conditioned ones, leaving redundancy untouched. An aggressive threshold compresses stable floors but catastrophically discards vital spatial cues elsewhere. The researchers solved this by linking the selection percentile to a global coefficient of variation computed over the non-missing RSSI values on each floor, allowing the threshold to shift automatically according to measured signal dispersion. The scaling factor governing this shift was chosen through a minimum-sufficient-shift criterion, requiring that worst-case accuracy degradation across all floors stay below one percent while guaranteeing meaningful compression everywhere. Crucially, all entropy estimates and thresholds were computed exclusively on training folds during cross-validation, preventing any information leakage from held-out test samples.

The evaluation used the JUIndoorLoc dataset, a corpus of Wi-Fi fingerprints collected over 31 days from four Android devices across a three-floor building, spanning 172 distinct access points and multiple contextual conditions including open versus closed rooms and human presence versus absence. The team trained Random Forest classifiers with stratified five-fold cross-validation and swept static percentile thresholds from 10 to 95 percent to map the accuracy-dimensionality trade-off before engaging the adaptive rule. The results are striking. On Floor F3, the framework selected just 17 access points, a 90.11 percent reduction, with an accuracy change of only minus 0.41 percent. On Floor F4, it retained a mere 8 access points, compressing the feature space by 95.34 percent while losing just 0.51 percent accuracy. On Floor F5, 26 access points sufficed, an 84.88 percent reduction with a 0.42 percent accuracy loss. In every case, the adaptive threshold landed in a narrow operating region that fixed thresholds missed entirely.

The sensitivity analysis revealed why the adaptive approach matters. Floor F4, whose entropy distribution was highly concentrated, exhibited a sharp cliff effect: increasing the scaling factor from 5 to 40 collapsed accuracy from 0.9697 to 0.4378, a catastrophic 55 percent drop, because even small threshold shifts eliminated critical routers in that compact signal environment. Floors F3 and F5 degraded gradually by contrast. The chosen scaling factor of 5 proved Pareto-optimal, achieving the highest worst-case accuracy across all floors while delivering non-trivial compression on every one. Comparisons against standard entropy baselines with fixed 85 and 95 percent cutoffs, and against an Otsu-based adaptive threshold, confirmed the advantage: the conservative static threshold selected all 172 access points on F3 with zero compression, while the aggressive one caused a 15.35 percent accuracy collapse on that floor. The proposed method outperformed PCA-based dimensionality reduction on all three floors despite using dramatically fewer features, and unlike PCA, it preserved direct one-to-one correspondence with physical routers, enabling network administrators to identify which specific devices are critical for localization.

An ablation study addressed a subtle concern: Random Forest performs its own internal feature selection during node splitting, so was the observed benefit merely the classifier doing the pruning work? The researchers repeated the experiments with K-nearest neighbors, a classifier with no intrinsic feature-selection mechanism, and found that entropy-selected subsets preserved most performance despite reducing features from 172 to 17, 8, and 26 access points, with accuracy drops limited to 2.5, 0.5, and 0.4 percentage points respectively. Comparing entropy ranking against Random Forest’s own Gini importance ranking showed comparable or superior subset effectiveness across floors, establishing Shannon entropy as a classifier-agnostic, lightweight alternative to model-dependent importance measures.

Robustness testing under realistic degradation revealed a nuanced, floor-dependent trade-off. Under synthetic Gaussian RSSI noise, the entropy-selected subsets frequently equaled or surpassed the full feature space on Floors F3 and F5. On Floor F5, at noise levels of 1, 2, and 3 decibels, the selected subset achieved accuracies of 0.8472, 0.7748, and 0.7070, versus 0.7674, 0.7084, and 0.6659 for using all access points, demonstrating that pruning unstable signals actively filters noise. On Floor F3, the advantage held at mild noise but reversed under severe perturbation, where the redundancy of the full set acted as a buffer. Floor F4 told the opposite story: the extremely compact 8-access-point subset degraded far more rapidly under noise than the full set, whose redundancy provided vital resilience. Under random access point dropouts simulating infrastructure outages, however, the selected subsets proved exceptionally resilient everywhere, with Floor F3’s subset outperforming the full set by 3.2 percentage points at 30 percent dropout and Floor F4’s compact subset losing only 0.5 percentage points even under extreme feature compression.

The practical implications extend beyond accuracy. Benchmarking showed that pruning the feature space shrank model storage from 3.82 megabytes to between 0.24 and 0.73 megabytes, small enough for edge microcontrollers. Online inference latency on a simulated commodity smartphone dropped from 1.45 milliseconds to under 0.27 milliseconds per sample. Most remarkably, because the method isolates a compact subset of routers, mobile devices can execute targeted scans on only the channels where selected access points broadcast, cutting radio active time from roughly 300 milliseconds to about 60 milliseconds and yielding an estimated 80 percent reduction in scanning power consumption, a direct extension of battery life during active navigation.

The study also exposed honest limitations. Zero-shot transfer across environmental contexts failed almost completely, with near-zero accuracy across the vast majority of context pairs regardless of whether the full or selected feature set was used, revealing a fundamental distributional shift in RSSI under changing room configuration and occupancy rather than an artifact of feature selection. Yet a simple few-shot calibration protocol using just five target-domain samples per class restored accuracy to between 0.76 and 0.99 across all tested pairs, confirming that the selected subsets preserve the essential spatial structure needed for domain transfer. Device-specific entropy rankings showed high correlation with the pooled ranking, with mean Spearman correlation of 0.784 on the best-covered floor, suggesting that the most informative access points are identified consistently regardless of the measuring device. The authors caution that their evaluation rests on a single dataset from one building and that the optimal scaling factor may require recalibration elsewhere. Still, the core message stands: high-dimensional radio fingerprints are not always necessary for competitive indoor localization, and adaptive, information-theoretic filtering offers a scalable pathway toward deployable Wi-Fi positioning systems that are lighter, faster, and more robust than the brute-force alternative.

Subject of Research: Engineering

Article Title: Adaptive entropy-guided access point selection for robust multi-floor Wi-Fi fingerprinting

Article References: Bahy El-Din, A. Y., Nafea, H. B., Samra, A. S., & Abozied, M. A. H. (2026). Adaptive entropy-guided access point selection for robust multi-floor Wi-Fi fingerprinting. Scientific Reports, 16(1), 32706. https://doi.org/10.1038/s41598-026-72185-8

Image Credits: AI Generated

DOI: 10.1038/s41598-026-72185-8

Keywords: Wi-Fi fingerprinting, indoor localization, Shannon entropy, access point selection, adaptive thresholding, robustness, domain shift, Scientific Reports, Adaptive, entropy-guided, access, point