Wireless Access Points Research Articles

Design and Implementation of a Smart Wireless Access Point for A Gas Station (SWAP-GS)

The gas distribution sector is a good fit for wireless communications, especially considering how often distribution facilities are located in distant areas. The underlying communications infrastructure needs to be extremely dependable, with a resilient system that links sensors and controls throughout a vast field area network, when working with accumulated and real-time data. The resulting detail assists managers in creating and modifying business processes, managing the station site fully, and increasing operational efficiency. A wireless gas station offers an architecture based on Wi-Fi standards for applications that track and collect data all the way down sto the sensor level. The goal of the research was to install a wireless infrastructure network at a service station. When used in a gas station, plant, enable users to access real-time information, exchange and share sensor readings from the access point, and develop a smart wireless access point system with higher secure encryption than the generic wireless network. The NodeMCU microcontroller, which broadcasts the real-time data to the clients, receives inputs from a temperature, pressure, and humidity sensor. This allows the system to do such activities. It was believed that the built wireless infrastructure will enhance the existing LAN infrastructures. A few basic prerequisites are taken into account. First and foremost, a microcontroller, a sensor unit, software design, and compliance with IEEE 802.11 standards were required.

Floor-Level Occupancy Estimation of a Multi-Story Building Using Coarse Wi-Fi Data

In recent years, there has been an extraordinary increase in wireless capable devices and network infrastructure, which spawned a corresponding rise in data produced from the interactions of these technologies. Mobile devices constantly roam, leading to a perpetual dialog between a mobile device and wireless access points. This dialogue generates a continuous stream of device-specific data, including but not limited to a device’s media access control address, time of access, and received signal strength. Given the knowledge of the access point’s location and received signal strength, it is possible to infer the position of user devices and estimate their mobility and occupancy. This article presents two methods for accurately measuring floor-level occupancy in a multi-story building at Texas State University using coarse Wi-Fi log data. The first method employs a static filter, while the second incorporates user-role data and user location to create a dynamic filter. Quantitative methods are used to evaluate these filters against field-collected reference data and existing internal people-counting sensors. Our results demonstrate that the dynamic filter, leveraging variable thresholds, provides a more accurate estimation of occupancy compared to the fixed 5-min static filter which consistently overestimated occupancy. This research sheds light on the potential of dynamic filters derived from user-role data for precise floor-level occupancy estimations, with implications for various applications.

Formal estimation of wireless network services for signal strength and electromagnetic wave association in an International Green University

The United Nations focuses on 17 urgent problems to call for action in all countries. Goal 7 of the 17 urgent problems is based on affordable and clean energy. Since 2017, National Taitung University (NTTU) has dedicated more time and effort to attain the wisdom, health, sustainability and aesthetics as an international green university. To accomplish this, we adhere and construct a safe radiofrequency and electromagnetic wave environment to achieve healthy and sustainable campus objectives. According to the UI GreenMetric World University Rankings, NTTU was ranked 74th in 2021, 67th in 2022 and 58th in 2023. In this study, we propose a formal estimation of wireless network services for classrooms or smart spaces to achieve the goal of safe radiofrequency and electromagnetic waves. Inside classrooms or smart spaces, better wireless signal strength and safer electromagnetic waves are achieved. Moreover, the proposed method can be used to determine the quantity of wireless access points for a given classroom or smart space to avoid unsafe electromagnetic waves and inappropriate energy consumption. The experimental results show that all benchmarks meet the wireless exposure limits of the WHO and physician safe technologies in the NTTU.

Enhancing security offloading performance in NOMA heterogeneous MEC networks using access point selection and meta-heuristic algorithm

The research delves into the intricate domain of security offloading within the context of non-orthogonal multiple access (NOMA) heterogeneous mobile edge computing (het-MEC) networks operating over Rayleigh fading channels. The investigation centers on a system model comprising a single antenna-equipped edge user, denoted as U, which strategically offloads computational tasks to two distinct heterogeneous wireless access points (APs): the far AP (AP1) and the near one (AP2), employing NOMA techniques. Notably, the research accounts for a passive eavesdropper (E) intending to intercept the U−AP2 transmission. A four-phase protocol is proposed to ensure the security offloading process, namely SAPS, which leverages wireless access point selection (APS) and physical layer security (PLS) techniques. The focus extends to derive a closed-form expression for a novel critical system performance metric: the secrecy successful computation probability (SSCP). Furthermore, an algorithm based on Ant Colony Optimization (ACO) within the continuous domain is introduced, which aims to enhance the SSCP by intelligently determining system parameters. The impact of critical factors such as transmit power, power allocation coefficient, bandwidth, CPU frequency, and task division ratio under the SAPS scheme is explored and compared to the conventional approach using pure NOMA. Remarkably, the algorithm in the proposed scheme demonstrates up to a 3% performance improvement. The validity and accuracy of the study findings are verified through Monte-Carlo simulations. The work contributes significantly to advancing secure offloading strategies in NOMA-based MEC networks, offering valuable insights for practical deployment and optimization.

A WKNN Indoor Fingerprint Localization Technique Based on Improved Discrimination Capability of RSS Similarity.

There are various indoor fingerprint localization techniques utilizing the similarity of received signal strength (RSS) to discriminate the similarity of positions. However, due to the varied states of different wireless access points (APs), each AP's contribution to RSS similarity varies, which affects the accuracy of localization. In our study, we analyzed several critical causes that affect APs' contribution, including APs' health states and APs' positions. Inspired by these insights, for a large-scale indoor space with ubiquitous APs, a threshold was set for all sample RSS to eliminate the abnormal APs dynamically, a correction quantity for each RSS was provided by the distance between the AP and the sample position to emphasize closer APs, and a priority weight was designed by RSS differences (RSSD) to further optimize the capability of fingerprint distances (FDs, the Euclidean distance of RSS) to discriminate physical distance (PDs, the Euclidean distance of positions). Integrating the above policies for the classical WKNN algorithm, a new indoor fingerprint localization technique is redefined, referred to as FDs' discrimination capability improvement WKNN (FDDC-WKNN). Our simulation results showed that the correlation and consistency between FDs and PDs are well improved, with the strong correlation increasing from 0 to 76% and the high consistency increasing from 26% to 99%, which confirms that the proposed policies can greatly enhance the discrimination capabilities of RSS similarity. We also found that abnormal APs can cause significant impact on FDs discrimination capability. Further, by implementing the FDDC-WKNN algorithm in experiments, we obtained the optimal K value in both the simulation scene and real library scene, under which the mean errors have been reduced from 2.2732 m to 1.2290 m and from 4.0489 m to 2.4320 m, respectively. In addition, compared to not using the FDDC-WKNN, the cumulative distribution function (CDF) of the localization errors curve converged faster and the error fluctuation was smaller, which demonstrates the FDDC-WKNN having stronger robustness and more stable localization performance.

An improved channel scheduling model for wireless access point in high density traffic using Cognitive Radio.An improved channel scheduling model for wireless access point in high density traffic using Cognitive Radio

An improved channel scheduling model for wireless access point in high density traffic using Cognitive Radio.An improved channel scheduling model for wireless access point in high density traffic using Cognitive Radio

Design and Development Considerations of a Cyber Physical Testbed for Operational Technology Research and Education.

Cyber-physical systems (CPS) are vital in automating complex tasks across various sectors, yet they face significant vulnerabilities due to the rising threats of cybersecurity attacks. The recent surge in cyber-attacks on critical infrastructure (CI) and industrial control systems (ICSs), with a 150% increase in 2022 affecting over 150 industrial operations, underscores the urgent need for advanced cybersecurity strategies and education. To meet this requirement, we develop a specialised cyber-physical testbed (CPT) tailored for transportation CI, featuring a simplified yet effective automated level-crossing system. This hybrid CPT serves as a cost-effective, high-fidelity, and safe platform to facilitate cybersecurity education and research. High-fidelity networking and low-cost development are achieved by emulating the essential ICS components using single-board computers (SBC) and open-source solutions. The physical implementation of an automated level-crossing visualised the tangible consequences on real-world systems while emphasising their potential impact. The meticulous selection of sensors enhances the CPT, allowing for the demonstration of analogue transduction attacks on this physical implementation. Incorporating wireless access points into the CPT facilitates multi-user engagement and an infrared remote control streamlines the reinitialization effort and time after an attack. The SBCs overwhelm as traffic surges to 12 Mbps, demonstrating the consequences of denial-of-service attacks. Overall, the design offers a cost-effective, open-source, and modular solution that is simple to maintain, provides ample challenges for users, and supports future expansion.

On the Use and Construction of Wi-Fi Fingerprint Databases for Large-Scale Multi-Building and Multi-Floor Indoor Localization: A Case Study of the UJIIndoorLoc Database.

Large-scale multi-building and multi-floor indoor localization has recently been the focus of intense research in indoor localization based on Wi-Fi fingerprinting. Although significant progress has been made in developing indoor localization algorithms, few studies are dedicated to the critical issues of using existing and constructing new Wi-Fi fingerprint databases, especially for large-scale multi-building and multi-floor indoor localization. In this paper, we first identify the challenges in using and constructing Wi-Fi fingerprint databases for large-scale multi-building and multi-floor indoor localization and then provide our recommendations for those challenges based on a case study of the UJIIndoorLoc database, which is the most popular publicly available Wi-Fi fingerprint multi-building and multi-floor database. Through the case study, we investigate its statistical characteristics with a focus on the three aspects of (1) the properties of detected wireless access points, (2) the number, distribution and quality of labels, and (3) the composition of the database records. We then identify potential issues and ways to address them using the UJIIndoorLoc database. Based on the results from the case study, we not only provide valuable insights on the use of existing databases but also give important directions for the design and construction of new databases for large-scale multi-building and multi-floor indoor localization in the future.

Uncovering Cybersecurity Vulnerabilities: A Kali Linux Investigative Exploration Perspective

Abstract This research exploration presents a comprehensive methodology for conducting penetration testing for networking security protocols and vulnerabilities on the Wi-Fi networks using Kali Linux, an open-source penetration testing platform. The methodology also encompasses four main stages which are Preparation, Information Gathering, Simulated Attack, Reporting. In the Preparation Stage, the scope of the penetration test is defined, authorization is obtained, and within the testing environment the experimentation is prepared. The Information Gathering Stage involves scanning for associated nearby wireless access points (APs), identifying encryption modes, and assessing network coverage. The Simulated Attack Phase verifies the types of vulnerabilities through password cracking, infrastructure penetration tests, and client-side attacks. Finally, the Reporting Phase entails compiling of a very detailed test report with results visualized, findings and recommendations with directions. Experimental results validate the overall effectiveness of the methodology in identifying and mitigating Wi-Fi network vulnerabilities. Through systematic testing and analysis, Kali Linux facilitates proactive security measures to enhance Wi-Fi network defenses.

L-NORM: Learning and Network Orchestration at the Edge for Robot Connectivity and Mobility in Factory Floor Environments

Robotic factory floors will revolutionize the future of manufacturing and the service industry by automating tasks. However, to fully supplement human effort, these robots will need low-latency, reliable connectivity throughout the work zone through links established by wireless access points (APs). This will allow the robot to assuredly respond to programming directives that rely on the real-time relaying of robot-generated sensor data to the Mobile Edge Computing (MEC) server. In this paper, we propose L-NORM, a multi-AP and multi-robot coordination framework, as a multi-tiered solution for such autonomous edge networks. First, multi-robot motion planning through reinforcement learning occurs at the MEC, using as input multi-modal robot sensor data. Second, multi-AP resource orchestration is performed using another reinforcement learning-based method that maps a subset of available APs to each robot toward meeting their sensor data delivery requirements. Furthermore, we suggest diversity combination of uplink channels with the 802.11ax scheduled access mode that will (i) support high reliability of multi-robot uplink sensor packets and (ii) enable multi-AP coordination, for optimized resource utilization. Through extensive simulation studies, we show that the probability of robot deviation to remain within 0.5 m from its optimal path, is 19% more in L-NORM compared to classical 802.11ax based edge network solution, considering <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$\sim$</tex-math></inline-formula> 1 MB of sensor data per robot.

Intelligent Reflecting Surface Assisted Secure Computation of Wireless Powered MEC System

The integration of mobile edge computing (MEC) and wireless power transfer (WPT) can effectively improve the computing ability and energy sustainability of energy-constrained wireless devices in the Internet of Things (IoT) networks. Intelligent reflecting surface (IRS) has recently emerged as an effective technique to improve the performance of wireless systems by intelligently reconfiguring wireless environments. This paper studies the exploitation of IRS to improve the secure computation performance of WPT-MEC systems with a passive eavesdropper. A wireless access point (AP) first charge multiple users with the emitted energy signals, and then the users perform local computing and partial offloading to complete their computation tasks with the harvested energy in the presence of an eavesdropper, where the local computing can be executed during the whole process of WPT and offloading. Meanwhile, deploying IRS can improve the energy capture and secure offloading performance of the users. We maximize the secure computation task bits of users by jointly optimizing the AP energy transmit beamforming, the IRS phase shifts, the transmit power, users' offloading time, and the local computation frequency of users, which are tangled with each other. An iterative optimal algorithm is developed to solve this non-convex problem by combining Taylor expansion method, semidefinite relaxation (SDR) algorithm, the Lagrange duality theory and Karush-Kuhn-Tucker (KKT) conditions. The numerical results show that the proposed scheme can effectively increase the secure computation task bits compared with other benchmark schemes, especially for the maximum transmit power of AP, the improvement is above 45 <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$\%$</tex-math></inline-formula> .

Exploiting channel state information of WiFi signal for human activity detection: an experimental study

Ubiquitous computing aims to seamlessly integrate computing into our daily lives, and requires reliable information on human activities and state for various applications. In this paper, we propose a device-free human activity recognition system that leverages the rich information behind WiFi signals to detect human activities in indoor environments, including walking, sitting, and standing. The key idea of our system is to use the dynamic features of activities, which we carefully examine and analyze through the characteristics of channel state information. We evaluate the impact of location changes on WiFi signal distribution for different activities and design an activity detection system that employs signal processing techniques to extract discriminative features from wireless signals in the frequency and temporal domains. We implement our system on a single off-the-shelf WiFi device connecting to a commercial wireless access point and evaluate it in laboratory and conference room environments. Our experiments demonstrate the feasibility of using WiFi signals for device-free human activity recognition, which could provide a practical and non-intrusive solution for indoor monitoring and ubiquitous computing applications.

Rancang Bangun Sistem Presensi Biometrik Sidik Jari Berbasis IoT dengan Arduino Node MCU

Presence is a procedure or process that needs to be carried out by all employees or members of a company or agency. The attendance register is a reference and benchmark for assessing, measuring, and determining the quality and quantity of each member or employee of the company or agency. Presentations carried out manually are considered less effective and efficient in terms of time and usage. So it is necessary to build an intelligent attendance system, one of which is by creating an intelligent attendance system that uses IoT-based fingerprint recognition technology with an Arduino controller and outputs to an Internet or intranet server. Based on the results obtained in the research, administrators can float their side servers freely. The time for sending data from the presence device to the server uses the POST method, with an average time of 8 seconds on an intranet network with a maximum distance of 25 meters from the wireless router or access point. By creating this attendance system, it makes it easier for the Surya Pematangsiantar Education Foundation to record the attendance of teachers and staff, especially SMK units.

A Novel Adaptive Indoor Positioning Using Mobile Devices with Wireless Local Area Networks

In this paper, mobile devices were used to estimate the received signal strength indicator (RSSI) of wireless channels with three wireless access points (APs). Using the RSSI, the path loss exponent (PLE) was adapted to calculate the estimated distance among the test points (TPs) and the APs, through the root mean square error (RMSE). Moreover, in this paper, the proposed adaptive PLE (APLE) of the TPs was obtained by minimizing the positioning errors of the PLEs. The training samples of RSSI were measured by TPs for 6 days, and different surge processing methods were used to obtain APLE and to improve the positioning accuracy. The surge signals of RSSI were reduced by the cumulated distribution function (CDF), hybrid Kalman filter (KF), and threshold filtering methods, integrating training samples and APLE. The experimental results show that with the proposed APLE, the position accuracy can be improved by 50% compared to the free space model for six TPs. Finally, dynamic real-time indoor positioning was performed and measured for the performance evaluation of the proposed APLE models. The experimental results show that, the minimum dynamic real-time positioning error can be improved to 0.88 m in a straight-line case with the hybrid method. Moreover, the average positioning error of dynamic real-time indoor positioning can be reduced to 1.15 m using the four methods with the proposed APLE.

HyTra: Hyperclass Transformer for WiFi Fingerprinting-based Indoor Localization

The emerging demand for a variety of novel Location-based Services (LBS) by consumers and industrial users is driven by the rapid and extensive proliferation of mobile smart devices. Sensors embedded in smart devices or machines provide wireless connectivity and Global Positioning System (GPS) capability, and are co-utilized to acquire location-linked data which are algorithmically transformed into reliable and accurate location estimates. GPS is a mature and reliable technology for outdoor localization but indoor localization in a complex multi-storey building environment remains challenging due to fluctuations in wireless signal strength arising from multipath fading. Location-linked data from wireless access points (WAPs) such as received signal strength (RSS) are acquired as numerical sequences. By conceptualizing a fixed order sequence of WAP measurements as a sentence where the RSS from each WAP are words, we may leverage on recent advances in artificial intelligence for natural language processing (NLP) to enhance localization accuracy and improve robustness against signal fluctuations. We propose the hyper-class Transformer (HyTra), an encoder-only Transformer neural network which learns the relative positions of wireless access points (WAPs) through multiple learnable embeddings. We propose a second network, HyTra-HF, which improves upon HyTra by applying a hierarchical relationship between location classes. We test our proposed networks on public and private datasets varying in sizes. HyTra-HF outperforms existing deep learning solutions by obtaining 96.7\% accuracy for the floor classification task on the UJIIndoorloc dataset. HyTra-HF is amenable to deep model compression and achieves accuracy of 95.95\% with over ten-fold reduction in model size using Sparsity Aware Orthogonal (SAO) initialization and has the best-in-class accuracy for the sparse model.

6G Goal-Oriented Communications: How to Coexist with Legacy Systems?

6G will connect heterogeneous intelligent agents to make them natively operate complex cooperative tasks. When connecting intelligence, two main research questions arise to identify how artificial intelligence and machine learning models behave depending on (i) their input data quality, affected by errors induced by interference and additive noise during wireless communication; (ii) their contextual effectiveness and resilience to interpret and exploit the meaning behind the data. Both questions are within the realm of semantic and goal-oriented communications. With this paper, we investigate how to effectively share communication spectrum resources between a legacy communication system (i.e., data-oriented) and a new goal-oriented edge intelligence one. Specifically, we address the scenario of an enhanced Mobile Broadband (eMBB) service, i.e., a user uploading a video stream to a radio access point, interfering with an edge inference system, in which a user uploads images to a Mobile Edge Host that runs a classification task. Our objective is to achieve, through cooperation, the highest eMBB service data rate, subject to a targeted goal effectiveness of the edge inference service, namely the probability of confident inference on time. We first formalize a general definition of a goal in the context of wireless communications. This includes the goal effectiveness, (i.e., the goal achievability rate, or the probability of achieving the goal), as well as goal cost (i.e., the network resource consumption needed to achieve the goal with target effectiveness). We argue and show, through numerical evaluations, that communication reliability and goal effectiveness are not straightforwardly linked. Then, after a performance evaluation aiming to clarify the difference between communication performance and goal effectiveness, a long-term optimization problem is formulated and solved via Lyapunov stochastic network optimization tools to guarantee the desired target performance. Finally, our numerical results assess the advantages of the proposed optimization and the superiority of the goal-oriented strategy against baseline 5G-compliant legacy approaches, under both stationary and non-stationary communication (and computation) environments.

Positioning by floors based on WiFi fingerprint

WiFi-based indoor positioning technology has gradually become a hot research topic in the field of indoor positioning, but the development of this technology has been facing the challenge of susceptibility to environmental interference. Therefore, in this paper, the kernel function method (KFM) with stronger interference resistance is used for positioning, and the adaptive algorithm is proposed for the time-consuming and laborious problem of manual parameter tuning, which incorporates the ideas of cross-validation and iteration. In addition, too many wireless access points (APs) mean higher computational cost and longer positioning time, so it is necessary to choose reasonable APs for positioning. In this paper, we use the random forest (RF) algorithm to assess the importance of APs and filter out a small number of APs with high importance. Considering the obvious differences in the WiFi signals received on different floors, a system framework for positioning by floors based on WiFi fingerprints is proposed. In the offline phase, the fingerprint library is first divided according to floors, and then perform separately AP selection and parameter tuning for each sub-fingerprint library. In the online phase, support vector machine is used to discriminate the floors first, and then KFM is used for planar positioning. Experiments are conducted on the public dataset, and the results show that the proposed algorithm has higher positioning accuracy, more robustness, and less time-consuming compared to several common algorithms.

High Speed Dual‐Band Photodetector for Dual‐Channel Optical Communications in Wavelength Division Multiplexing and Security Enhancement

AbstractThe forthcoming wireless networks must integrate to a high density of end‐user devices while ensuring a superior quality of service. It is imperative to devise solutions that reduce the density of wireless access points and associated costs, while simultaneously ensuring security and privacy throughout propagation. Herein, a novel dual‐band photodetector with high selective responsivities in distinct visible and near‐infrared (NIR) band is developed as an optical signal receiver. The receiver has an unprecedented fast switch speed &gt; 500 kHz between different operation modes. Leveraging this fast switch speed, a high‐speed Multiple‐Input‐Single‐Output (MISO) system is developed, fusing spectrum resources from visible to infrared bands. The system achieves a data rate of 600 Kbps in the visible channel and 500 Kbps in the NIR channel, with a sum rate of 1.1 Mbps. This is the highest single‐channel data rate and sum rate among reported dual‐band photodetectors. Additionally, two previously overlooked mechanisms, the Subtraction and Addition modes, are studied in detail. The four operation modes are developed to establish a novel physical encryption method in terms of arithmetic relation. The dual‐channel coupled link can support a data rate of 200 Kbps, significantly enhancing communication security.

Clustering Performance Analysis of the K-Medoids Algorithm for Improved Fingerprint-Based Localization

Fingerprint-based localization, which uses received signal strength (RSS) measurements from spatially deployed wireless access points (APs), is a popular technique for indoor positioning. The size of the fingerprint database has a significant impact on the accuracy of localization. The higher the density of the fingerprint database, the more accurate the localization, but the longer the localization time. Clustering is one of the techniques used such systems to improve localization accuracy and reduce localization time. To cluster fingerprints, the majority of clustering techniques employ a distance-based fingerprint similarity metric. However, the choice of distance metric has a significant impact on the performance of the clustering algorithm. Using four publicly available RSS-based fingerprint databases, this paper investigates the clustering performance of the k-medoids algorithm using six distance metrics, namely Euclidean, Manhattan, cosine, Mahalanobis, Chebyshev, and Canberra distance. Using the silhouette score as a performance metric, the cosine and Euclidean distance metrics outperform the others, with the highest silhouette score values of about 0.38, 0.43, 0.34, and 0.31 on the SEUG_IndoorLoc, IIRC_IndoorLoc, MSI_IndoorLoc, and IPIN_2019_PIEP_UM databases, respectively. It demonstrates that on these four databases, using Euclidean distance as well as the angle between fingerprint measurement vectors is the best option for generating efficient clusters that will result in high localization accuracy and low localization time.

Spectrum-Energy-Economy Efficiency Analysis of B5G Wireless Communication Systems With Separated Indoor/Outdoor Scenarios

In this paper, we study the spectrum efficiency (SE), energy efficiency (EE), and economic efficiency (ECE) for a heterogeneous cellular architecture that separates the indoor and outdoor scenarios for beyond 5G (B5G) wireless communication systems. For outdoor scenarios, massive multiple-input-multiple-output (MIMO) technologies and distributed antenna systems (DASs) at sub-6 GHz frequency bands are used for long-distance communications. For indoor scenarios, millimeter-wave (mmWave) and beamforming communication technologies are deployed at wireless indoor access points (IAPs) to provide high-speed short-range services to indoor users. Mathematical expressions for the system capacity, SE, EE, and ECE are derived using a proposed realistic power consumption model. The results shed light on the fact that the proposed network architecture is able to improve SE and EE by more than three times compared to those conventional network architectures. The analysis of system performance in terms of SE, EE, ECE, and their trade-off results in the observation that the proposed network architecture offers a promising solution for future B5G communication systems.