WiFi Access Points Research Articles

RFCam

As cameras and Wi-Fi access points are widely deployed in public places, new mobile applications and services can be developed by connecting live video analytics to the mobile Wi-Fi-enabled devices of the relevant users. To achieve this, a critical challenge is to identify the person who carries a device in the video with the mobile device's network ID, e.g., MAC address. To address this issue, we propose RFCam, a system for human identification with a fusion of Wi-Fi and camera data. RFCam uses a multi-antenna Wi-Fi radio to collect CSI of Wi-Fi packets sent by mobile devices, and a camera to monitor users in the area. With low sampling rate CSI data, RFCam derives heterogeneous embedding features on location, motion, and user activity for each device over time, and fuses them with visual user features generated from video analytics to find the best matches. To mitigate the impacts of multi-user environments on wireless sensing, we develop video-assisted learning models for different features and quantify their uncertainties, and incorporate them with video analytics to rank moments and features for robust and efficient fusion. RFCam is implemented and tested in indoor environments for over 800 minutes with 25 volunteers, and extensive evaluation results demonstrate that RFCam achieves real-time identification average accuracy of 97.01% in all experiments with up to ten users, significantly outperforming existing solutions.

WePos

On-demand delivery is an emerging business in recent years where accurate indoor locations of Gig couriers play an important role in the order dispatch and delivery process. To cater to this need, WiFi-based indoor positioning methods have become an alternative method for on-demand delivery thanks to extensive WiFi deployment in the indoor environment. Existing WiFi-based indoor localization and positioning methods are not suitable for large-scale on-demand delivery scenarios due to high costs (e.g., high labor cost to collect fingerprints) and limited coverage due to limited labeled data. In this work, we explore (i) massive crowdsourced WiFi data collecting from wearable or mobile devices of couriers with little extra effort and (ii) natural manual reports data in the delivery process as two opportunities to perform merchant-level indoor positioning in a weak-supervised manner. Specifically, we proposed WePos, an end-to-end weak-supervised-based merchant-level positioning framework, which consists of the following three parts: (i) a Bidirectional Encoder Representations from Transformers (BERT) based pre-training module to learn latent embeddings of WiFi access points, (ii) a contrastive label self-generate module to produce pseudos for WiFi scanning lists by matching similarity embedding clustering results and couriers' reporting behaviors. (iii) a deep neural network-based classifier to fine-tune the whole training process and conduct online merchant-level position inference. To evaluate the performance of our system, we conduct extensive experiments in both a large-scale public crowdsourcing dataset with over 50 GB of WiFi signal records and a real-world WiFi crowdsourced dataset collected from Eleme, (i.e., one of the largest on-demand delivery platforms in China) in four multi-floor malls in Shanghai. Experimental results show that WePos outperforms state-of-the-art baselines in the merchant-level positioning performance, offer up to 91.4% in positioning accuracy.

Sensor-Aided Learning for Wi-Fi Positioning With Beacon Channel State Information

Because each indoor site has its own radio propagation characteristics, a site survey process is essential to optimize a Wi-Fi ranging strategy for range-based positioning solutions. This study examines an unsupervised learning technique that autonomously learns an optimal ranging strategy for each site using Wi-Fi and sensor data accumulated while users access a positioning application. Using the collected sensor data, the device trajectory is regenerated, and a Wi-Fi ranging module is optimized to generate the shape of the estimated trajectory using Wi-Fi, similar to that obtained from sensors. In this process, the ranging module learns the way to identify the channel conditions from each Wi-Fi access point (AP) and produces ranging results accordingly. Furthermore, we collect the channel state information (CSI) from beacon frames to investigate the benefit of using CSI in addition to received signal strength measurements. With the CSI, the ranging module can identify diverse channel conditions from each AP and more accurately generate the reliability of each distance estimate to achieve accurate positioning results. The effectiveness of the proposed learning technique is verified using a real-time positioning application implemented on a PC platform.

Indoor positioning based on tightly coupling of PDR and one single Wi-Fi FTM AP

ABSTRACT The indoor positioning system is now an important technique as part of the Internet-of-Things (IoT) ecosystem. Among indoor positioning techniques, multiple Wi-Fi Access Points (APs)-based positioning systems have been researched a lot. There is a lack of research focusing on the scene where only one Wi-Fi AP is available. This work proposes a hybrid indoor positioning system that takes advantage of the Fine-Timing Measurements (FTM) technique that is part of the IEEE 802.11mc standard, introduced back in 2016. The system uses one single Wi-Fi FTM AP and takes advantage of the built-in inertial sensors of the smartphone to estimate the device’s position. We explore both Loosely Coupled (LC) and Tightly Coupled (TC) integration schemes for the sensors’ data fusion. Experimental results show that the proposed methods can achieve an average positioning accuracy of about 1 m without knowing the initial position. Compared with the LC integration method, the median error accuracy of the proposed TC fusion algorithm has improved by more than 52% and 67%, respectively, in the two experiments we set up.

Differentially Private Occupancy Monitoring from WiFi Access Points.

Accurately monitoring the number of individuals inside a building is vital to limiting COVID-19 transmission. Low adoption of contact tracing apps due to privacy concerns has increased pervasiveness of passive digital tracking alternatives. Large arrays of WiFi access points can conveniently track mobile devices on university and industry campuses. The CrowdMap system employed by the University of Southern California enables such tracking by collecting aggregate statistics from connections to access points around campus. However, since these devices can be used to infer the movement of individuals, there is still a significant risk that even aggregate occupancy statistics will violate the location privacy of individuals. We examine the use of Differential Privacy in reporting statistics from this system as measured using point and range count queries. We propose discretization schemes to model the positions of users given only user connections to WiFi access points. Using this information we are able to release accurate counts of occupants in areas of campus buildings such as labs, hallways, and large discussion halls with minimized risk to individual users' privacy.

Multi-Floor Indoor Localization Based on Multi-Modal Sensors.

High-precision indoor localization is growing extremely quickly, especially for multi-floor scenarios. The data on existing indoor positioning schemes, mainly, come from wireless, visual, or lidar means, which are limited to a single sensor. With the massive deployment of WiFi access points and low-cost cameras, it is possible to combine the above three methods to achieve more accurate, complete, and reliable location results. However, the existing SLAM rapidly advances, so hybrid visual and wireless approaches take advantage of this, in a straightforward manner, without exploring their interactions. In this paper, a high-precision multi-floor indoor positioning method, based on vision, wireless signal characteristics, and lidar is proposed. In the joint scheme, we, first, use the positioning data output in lidar SLAM as the theoretical reference position for visual images; then, use a WiFi signal to estimate the rough area, with likelihood probability; and, finally, use the visual image to fine-tune the floor-estimation and location results. Based on the numerical results, we show that the proposed joint localization scheme can achieve 0.62 m of 3D localization accuracy, on average, and a 1.24-m MSE for two-dimensional tracking trajectories, with an estimation accuracy for the floor equal to 89.22%. Meanwhile, the localization process takes less than 0.25 s, which is of great importance for practical implementation.

An Unsupervised Detection Method for Multiple Abnormal Wi-Fi Access Points in Large-Scale Wireless Network

ABSTRACT The probability of a single access point (AP) failure is very small. In addition, APs communicate with each other; therefore, it is considered that these failures have little impact on the wireless network. Only when a large number of APs are abnormal offline, do we consider that the wireless network is faulty and needs to be recovered immediately. Network breakdown, network congestion, and AP management software shutdown may cause numerous APs in aborted status. In this article, we utilize DBSCAN algorithm to detect abnormal Wi-Fi APs. Compared with other research works, our proposed unsupervised method can distinguish between normal and abnormal offline APs. This study proposes a new date dimension to calculate the number of online APs together with the time dimension, and it provides new insights to set up thresholds of online APs automatically. Experimental results show that this 3-D model based on date and time is more accurate than the traditional 2-D model only based on time. With regard to the sampling method of random forest, this paper carries out repetitive random sampling to form small sample sets and finally to obtain the mean feature plane, which can reduce the interference of abnormal points to our algorithm.

NQRELoc: AP Selection via Nonuniform Quantization RSSI Entropy for Indoor Localization

Received signal strength indicator (RSSI) of WiFi access point (AP) is a primary sensor data used for indoor fingerprint localization. User sends its online RSSI to server to estimate its position by matching with RSSI fingerprints database which built in the offline phase. An important goal of indoor fingerprint localization is to increase the accuracy while reduce the storage cost. Meanwhile, APs perform different effects on target estimation and mapping metric in RSSI fingerprint localization. In this paper, a novel localization model based on the nonuniform quantization RSSI entropy (NQRELoc) is proposed to address these problems. First, to select the APs that contribute more to the localization, the nonuniform quantization RSSI entropy (NQRE) is introduced to quantify AP’s discernibility and select APs whose signals show sufficient differentiation to construct an offline fingerprints database. Then, the entropy-weighted euclidean distance (EWED) is used as a metric to measure the similarity of online RSSI vectors and offline RPs fingerprints. Finally, NQRELoc locates the target by the improved entropy-weighted K nearest neighbor (IEWKNN) algorithm, which takes the APs effect into target estimation. To validate the proposed algorithm, a large scale of experiments and simulations are implemented. The results demonstrate that NQRELoc can not only reduce the storage overhead but also improve the positioning accuracy compared to the other existing techniques.

AP DETECTOR: CROWDSOURCING-BASED APPROACH FOR SELF-LOCALIZATION OF WI-FI FTM STATIONS

Abstract. The acquisition of locations of Wi-Fi access points (APs) in urban buildings plays an important role in smart city applications, such as indoor navigation and social media data mining. This paper proposes a crowdsourcing-based approach for self-localization of Wi-Fi APs with the assistance of indoor pedestrian network (AP Detector). The features extracted from local opportunity signals are adopted for floor identification, and the crowdsourced indoor trajectories are segmented and matched with extracted indoor pedestrian network for the further trajectory calibration. In addition, the iteration unscented Kalman filter is applied for the location and bias estimation of local Wi-Fi FTM stations using the constructed Wi-Fi ranging model. The experimental results indicate that the proposed AP Detector can realize accurate location estimation of Wi-Fi APs, which also provides an effective way for autonomous construction of indoor navigation database and hybrid localization.

Indoor Localization Using Uncooperative Wi-Fi Access Points.

Indoor localization using fine time measurement (FTM) round-trip time (RTT) with respect to cooperating Wi-Fi access points (APs) has been shown to work well and provide 1–2 m accuracy in both 2D and 3D applications. This approach depends on APs implementing the IEEE 802.11-2016 (also known as IEEE 802.11mc) Wi-Fi standard (“two-sided” RTT). Unfortunately, the penetration of this Wi-Fi protocol has been slower than anticipated, perhaps because APs tend not to be upgraded as often as other kinds of electronics, in particular in large institutions—where they would be most useful. Recently, Google released Android 12, which also supports an alternative “one-sided” RTT method that will work with legacy APs as well. This method cannot subtract out the “turn-around” time of the signal, and so, produces distance estimates that have much larger offsets than those seen with two-sided RTT—and the results are somewhat less accurate. At the same time, this method makes possible distance measurements for many APs that previously could not be used. This increased accessibility can compensate for the decreased accuracy of individual measurements. We demonstrate here indoor localization using one-sided RTT with respect to legacy APs that do not support IEEE 802.11-2016. The accuracy achieved is 3–4 m in cluttered environments with few line-of-sight readings (and using only 20 MHz bandwidths). This is not as good as for two-sided RTT, where 1–2 m accuracy has been achieved (using 80 MHz bandwidths), but adequate for many applications A wider Wi-Fi channel bandwidth would increase the accuracy further. As before, Bayesian grid update is the preferred method for determining position and positional accuracy, but the observation model now is different from that for two-sided RTT. As with two-sided RTT, the probability of an RTT measurement below the true distance is very low, but, in the other direction, the range of measurements for a given distance can be much wider (up to well over twice the actual distance). We describe methods for formulating useful observation models. As with two-sided RTT, the offset or bias in distance measurements has to be subtracted from the reported measurements. One difference is that here, the offsets are large (typically in the 2400–2700 m range) because of the “turn-around time” of roughly 16 μs (i.e., about two orders of magnitude larger than the time of flight one is attempting to measure). We describe methods for estimating these offsets and for minimizing the effort required to do so when setting up an installation with many APs.

Longitudinal study of exposure to radio frequencies at population scale.

Evaluating exposure to radio frequencies (RF) at population-scale is important for conducting sound epidemiological studies about possible health impact of RF radiations. Numerous studies reported population exposure to RF radiations used in wireless telecommunication technologies, but used very small population samples. In this context, the real exposure of the population at scale remains poorly understood. Here, to the best of our knowledge, we report the largest crowd-based measurement of population exposure to RF produced by cellular antennas, Wi-Fi access points, and Bluetooth devices for 254,410 unique users in 13 countries from January 2017 to December 2020. First, we present methods to assess the population exposure to RF radiations using smartphone measurements obtained using the ElectroSmart Android app. Then, we use these methods to evaluate and characterize the evolution of RF exposure. We show that total exposure has been multiplied by 2.3 in the four-year period considered, with Wi-Fi as the largest contributor. The cellular exposure levels are orders of magnitude lower than regulation limits and are not correlated to national regulation policies. The population tends to be more exposed at home; for half of the study subjects, personal Wi-Fi routers and Bluetooth devices contributed to more than 50% of their total exposure. In this work, we showcase how crowdsource-based data allow large-scale and long-term assessment of population exposure to RF radiations.

DHCLoc: A Device-Heterogeneity-Tolerant and Channel-Adaptive Passive WiFi Localization Method Based on DNN

Passive WiFi localization refers to determining the location of WiFi-enabled mobile devices by deploying dedicated WiFi access points to sniff WiFi packets transmitted by these mobile devices and measure the corresponding received signal strengths (RSSs) for the use in localization. However, most existing studies fail to consider the effect of multiple channels where WiFi packets are transmitted and sniffed. The problem is further exacerbated by device heterogeneity occurring across various mobile devices. In this article, we present a unified deep neural network (DNN)-based solution, termed DHCLoc, to address these two challenges. To be specific, a Cramer–Rao lower bound (CRLB)-based analysis reveals that utilizing multichannel information will benefit localization, motivating us to include channel information into DHCLoc. Moreover, a novel maximum likelihood estimation (MLE)-based localization framework is introduced by incorporating a new variable to characterize the RSS measurement offsets caused by device heterogeneity, inspiring us to apply adversarial training to adopt such offsets against device heterogeneity. Extensive experiments using two real-world data sets are conducted, and show that, in comparison with several existing methods, DHCLoc can improve the localization accuracy by at least 25.2% and 25.8%, respectively.

Analysis of wireless network access logs for a hierarchical characterization of user mobility

This paper presents a method that generates a hierarchical user mobility model from the analysis of the data available from Wi-Fi connections. The data obtained from the Wi-Fi infrastructure is defined in terms of the coverage areas of the access points that the users move through. These access points are recursively grouped into different levels of granularity based on their geospatial features. The track of a user is defined as a sequence of Wi-Fi access points, which is enough to simulate user mobility in, for example, fog scenarios. The hierarchical definition of the region under study is proposed to reduce the complexity of the model in high-scale scenarios and to increase the adaptability between scenarios with different geospatial features. The model creation is based on a user profiling method that uses a clustering algorithm and each user type is defined with a transition matrix between coverage areas and a time length vector for the areas. The method is applied to the case of the campus of the University of the Balearic Islands. From the analysis of the mean square error of the results, we determined that the proposed method obtains good results for the transition matrices, but that the time vector definition should be improved. The results also show lower complexity in the case of the hierarchical model, with one area for each building and three levels, in regard to a non-hierarchical model, with only one area and one level for the whole campus.

Crowd Monitoring in Smart Destinations Based on GDPR-Ready Opportunistic RF Scanning and Classification of WiFi Devices to Identify and Classify Visitors’ Origins

Crowd monitoring was an essential measure to deal with over-tourism problems in urban destinations in the pre-COVID era. It will play a crucial role in the pandemic scenario when restarting tourism and making destinations safer. Notably, a Destination Management Organisation (DMO) of a smart destination needs to deploy a technological layer for crowd monitoring that allows data gathering in order to count visitors and distinguish them from residents. The correct identification of visitors versus residents by a DMO, while privacy rights (e.g., Regulation EU 2016/679, also known as GDPR) are ensured, is an ongoing problem that has not been fully solved. In this paper, we describe a novel approach to gathering crowd data by processing (i) massive scanning of WiFi access points of the smart destination to find SSIDs (Service Set Identifier), as well as (ii) the exposed Preferred Network List (PNL) containing the SSIDs of WiFi access points to which WiFi-enabled mobile devices are likely to connect. These data enable us to provide the number of visitors and residents of a crowd at a given point of interest of a tourism destination. A pilot study has been conducted in the city of Alcoi (Spain), comparing data from our approach with data provided by manually filled surveys from the Alcoi Tourist Info office, with an average accuracy of 83%, thus showing the feasibility of our policy to enrich the information system of a smart destination.

VContact: Private WiFi-Based IoT Contact Tracing With Virus Lifespan

Covid-19 is primarily spread through contact with the virus, which may survive on surfaces with a lifespan of hours or even days if not sanitized. To curb its spread, it is hence of vital importance to detect those who have been in contact with the virus for a sustained period of time, the so-called <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">close contacts</i> . Most of the existing digital approaches for contact tracing focus only on direct face-to-face contacts. There has been little work on detecting indirect environmental contact, which is to detect people coming into a contaminated area with the live virus, i.e., an area last visited by an infected person within the virus lifespan. In this work, we study automatic Internet of Things (IoT) contact tracing when the virus has a lifespan, which may depend on the disinfection frequency at a location. Leveraging the ubiquity of WiFi signals, we propose vContact, a novel, private, pervasive, and fully distributed WiFi-based IoT contact tracing approach. Users carrying an IoT device (phone, wearable, dongle, etc.) continuously scan WiFi access points (APs) and store their hashed IDs. Given a confirmed case, the signals are then uploaded to a server for other users to match in their local IoT devices for virus exposure notification. vContact is not based on device pairing, and no information of other users is stored locally. The confirmed case does not need to have the device for it to work properly. As WiFi data are sampled sporadically and asynchronously, vContact uses novel and effective signal processing approaches and a similarity metric to align and match signals at any time. We conduct extensive indoor and outdoor experiments to validate vContact performance. Our results demonstrate that vContact is effective and accurate for contact detection. The precision, recall, and F1-score of contact detection are high (up to 90%) for close contact proximity (2 m). Its performance is robust against AP numbers, AP changes, and phone heterogeneity. Having implemented vContact as an Android software development kit and installed it on phones and smart watches, we present a case study to demonstrate the validity and implementability of our design in notifying its users about their exposure to the virus with a specific lifespan.

Fault-Tolerant indoor localization based on speed conscious recurrent neural network using Kullback-Leibler divergence.

IoT services are the basic building blocks of smart cities, and some of such crucial services are provided by smart buildings. Most of the services like smart meters, indoor navigation, lighting control, etc., which contribute to smart buildings, need the locations of people or objects within the building. This gave rise to Indoor Localization, where only the infrastructure of the building has to be used for localization as accessing the Global Positioning System is difficult in indoor environments. Many approaches have been proposed to predict locations based on the infrastructure available indoors, and some of such techniques use Wi-Fi access points. Still, unfortunately, very few studies have concentrated on tolerating faults while being cost-effective. This work discusses hardware implementation of indoor localization. It then proposes a learning algorithm SRNN (Speed Conscious Recurrent Neural Network) that uses the RSSI (Received Signal Strength Indicator) values of available Wi-Fi access points in the building and predicts the location. Also, fault-tolerant approaches termed nearest RSSI and the most recent RSSI using Kullback–Leibler Divergence have been proposed to improve the location accuracy when access points go down and are prone to faults. Both the proposed approaches nearest RSSI and most recent RSSI along with SRNN improve the location accuracy by 4% and 2.1%, respectively, over existing techniques and contribute to optimizing predicted location's accuracy in Indoor Localization an IoT service for smart buildings.Supplementary informationThe online version contains supplementary material available at 10.1007/s12083-022-01301-y.

Human Silhouette and Skeleton Video Synthesis Through Wi-Fi Signals.

The increasing availability of wireless access points (APs) is leading toward human sensing applications based on Wi-Fi signals as support or alternative tools to the widespread visual sensors, where the signals enable to address well-known vision-related problems such as illumination changes or occlusions. Indeed, using image synthesis techniques to translate radio frequencies to the visible spectrum can become essential to obtain otherwise unavailable visual data. This domain-to-domain translation is feasible because both objects and people affect electromagnetic waves, causing radio and optical frequencies variations. In the literature, models capable of inferring radio-to-visual features mappings have gained momentum in the last few years since frequency changes can be observed in the radio domain through the channel state information (CSI) of Wi-Fi APs, enabling signal-based feature extraction, e.g. amplitude. On this account, this paper presents a novel two-branch generative neural network that effectively maps radio data into visual features, following a teacher-student design that exploits a cross-modality supervision strategy. The latter conditions signal-based features in the visual domain to completely replace visual data. Once trained, the proposed method synthesizes human silhouette and skeleton videos using exclusively Wi-Fi signals. The approach is evaluated on publicly available data, where it obtains remarkable results for both silhouette and skeleton videos generation, demonstrating the effectiveness of the proposed cross-modality supervision strategy.

Securing Hybrid Architecture of Cloudlet Computing in 5G Networks Enabling IoT and Mobile Wireless Devices

Aims: This research aims to secure and support mobile devices and IoTs enabled in WLAN and 5G Multi-Access Edge Computing (MEC) infrastructures. Background: Currently, wireless network access gains increasing potential in today's networks. At the same time, such ongoing wireless network access also raises the risk of network attacks. The 5G technology is expected to empower people inter-communications by integrating wireless networking technologies in all kinds of networks. Additionally, the emerging 5G technologies put forward many new requirements for RF characteristics and techniques regarding bandwidth and power issues, in addition to supporting high connectivity capacity for the emerging IP-based IoT applications. Objective: The objective is to provide a hybrid architecture of the proposed framework for a 5G network with cloudlet computing. The purpose that we are focusing on it is to support and monitor wireless networks under attack. Methods: The solution is done through a hybrid architecture. In this architecture, the cloudlet and Wi-Fi access points (AP) are integrated for providing an integrated Wi-Fi-enabled cloudlet. On the other hand, we have specified a solution for detecting unauthorized APs by using authentication and authorization techniques for securing communications among endpoint devices such as IoT devices or mobile devices, cloudlet, and the main cloud. A framework of this architecture has been developed to face the issue of securing IoT devices and mobile wireless devices. Results: The traffic sniffing and traffic filtering of the endpoints are done. Therefore, the required actions that define the organization's policy are applied through the permitted access list. In addition to a wireless network location, the management components use more than one authorized attribute value to generate the authorized list, such as SSID, MAC, BSSID …etc. Finally, Traffic analysis provides the user to get reports, statistics, and analysis charts of secure endpoints activities. Conclusion: We are focusing on one of the critical security issues of wireless networks. Namely, the issue of unauthorized access becomes more critical due to getting access to a network without subscriber permission. Moreover, the proposed solution allows managing access control permissions to allow or block access to resources for the user of a mobile device.

Wi-Fi Access Point Design Concept Targeting Indoor Positioning for Smartphones and IoT.

Indoor positioning systems (IPS) have been regarded as essential for many applications, particularly for smartphones, during the past decade. With the internet of things (IoT), and especially device-to-device (D2D) cases, the client is supposed to have a very simple structure and low cost. It is also desirable that the client contains minimal software modules specifically for IPS purposes. This study proposes a new IPS technique that satisfies these conditions. The evaluation of the technique was previously executed based on a manual procedure. This technique utilizes Wi-Fi technology in addition to a new design of two orthogonal phased antenna arrays. This paper provides a complete design of a Wi-Fi access point (AP), considered as the proof of concept of a commercial AP. For the system to be fully automatic, the proposed architecture is based on a Raspberry Pi, external Wi-Fi modules, a powered universal serial bus (USB) hub, and two orthogonal phased antenna arrays. The phases of each antenna array are governed by extra-phase circuits as well as a radio frequency (RF) switch. Extensive design parameters have been chosen through parametric sweeps that satisfy the design conditions. Software testing results for the antenna arrays are included in this paper to show the feasibility and suitability of the proposed antenna array for IPS.

Application of ant colony optimization metaheuristic on set covering problems

Ant Colony Optimization (ACO) metaheuristic is a multi-agent system in which the behaviour of each ant is inspired by the foraging behaviour of real ants to solve optimization problem. Set Covering Problems (SCP), on the other hand, deal with maximizing the coverage of every subset while the weight nodes used must be minimized. In this paper, ACO was adapted and used to solve a case of Set Covering Problem. The adapted ACO for solving the SCP was implemented as a computer program using SciLab 5.4.1. The problem of determining the optimal location of Wi-Fi Access Points using the 802.11n protocol in the UP Los Banos Math Building was solved using this metaheuristic. Results show that in order to have 100% coverage of the MB, 7 access points are required. Methodology of the study can be adapted and results of the study can be used by decision makers on related optimization problems.