Wireless Local Area Networks (WLANs), particularly Wi-Fi, serve as the backbone of modern connectivity, supporting billions of devices globally and forming a critical component in Internet of Things (IoT) ecosystems. However, the increasing ubiquity of WLANs also presents an expanding attack surface for adversaries—especially Advanced Persistent Threats (APTs), which operate with high levels of sophistication, resources, and long-term strategic objectives. This paper provides a holistic security analysis of WLANs under the lens of APT threat models, categorizing APT actors by capability tiers and examining their ability to compromise WLANs through logical attack surfaces. The study identifies and explores three primary attack surfaces: Radio Access Control interfaces, compromised insider nodes, and ISP gateway-level exposures. A series of empirical experiments—ranging from traffic analysis of ISP-controlled routers to offline password attack modeling—evaluate the current resilience of WLANs and highlight specific vulnerabilities such as credential reuse, firmware-based leakage, and protocol downgrade attacks. Furthermore, the paper demonstrates how APT resources significantly accelerate attacks through formal models of computational scaling. It also incorporates threat modeling frameworks, including STRIDE and MITRE ATT&CK, to contextualize risks and map adversary tactics. Based on these insights, this paper offers practical recommendations for enhancing WLAN resilience through improved authentication mechanisms, network segmentation, AI-based anomaly detection, and open firmware adoption. The findings underscore that while current WLAN implementations offer basic protections, they remain highly susceptible to well-resourced adversaries, necessitating a shift toward more robust, context-aware security architectures.

The demand for effective indoor positioning and navigation systems has surged in response to the increasing reliance on accurate location services within environments where traditional GPS is ineffective. This paper presents a comprehensive overview of the current landscape of indoor localization technologies, highlighting their applications across various industries. Despite significant advancements, challenges such as signal interference and dynamic obstacles persist, hindering the accuracy and efficiency of existing systems. To address these issues, we propose an algorithm that integrates advanced indoor mapping techniques with an iterative A* pathfinding approach. Our Algorithm utilizes Received Signal Strength Indicator (RSSI) values to ascertain user location through trilateration, while a user-friendly graphical interface allows for intuitive barrier placement and dynamic checkpoint selection. This framework not only improves navigation accuracy in complex indoor environments but also enhances operational efficiency by facilitating optimal pathfinding. Through empirical validation, our approach demonstrates a significant reduction in search times and an increase in productivity, underscoring its potential to transform indoor navigation practices. The findings contribute to the ongoing discourse on indoor positioning technologies, offering a robust solution to the challenges faced in navigating intricate indoor spaces.