Mobile Access Points Research Articles

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.

Truthful mechanism for joint resource allocation and task offloading in mobile edge computing

In the context of mobile edge computing (MEC), the delay-sensitive tasks can achieve real-time data processing and analysis by offloading to the MEC servers. The objective is maximizing social welfare in an auction-based model. However, the distances between mobile devices and access points lead to differences in energy consumption. Unfortunately, existing works have not considered both maximizing social welfare and minimizing energy consumption. Motivated by this, we address the problem of joint resource allocation and task offloading in MEC, with heterogeneous MEC servers providing multiple types of resources for mobile devices (MDs) to perform tasks remotely. We split the problem into two sub-problems: winner determination and offloading decision. The first sub-problem determines winners granted the ability to offload tasks to maximize social welfare. The second sub-problem determines how to offload tasks among the MEC servers to minimize energy consumption. In the winner determination problem, we propose a truthful algorithm that drives the system into equilibrium. We then show the approximate ratios for single and multiple MEC servers. In the offloading decision problem, we propose an approximation algorithm. We then show it is a polynomial-time approximation scheme for a single MEC server. Experiment results show that our proposed mechanism finds high-quality solutions in changing mobile environments.

Message Passing Detectors for UAV-Based Uplink Grant-Free NOMA Systems

Utilizing unmanned aerial vehicles (UAVs) as mobile access points or base stations has emerged as a promising solution to address the excessive traffic demands in wireless networks. This paper investigates improving the detector performance at the unmanned aerial vehicle base stations (UAV-BSs) in an uplink grant-free non-orthogonal multiple access (GF-NOMA) system by considering the activity state (AS) temporal correlation of the different user equipments (UEs) in the time domain. The Bernoulli Gaussian-Markov chain (BG-MC) probability model is used for exploiting both the sparsity and slow change characteristic of the AS of the UE. The GAMP Bernoulli Gaussian-Markov chain (GAMP-BG-MC) algorithm is proposed to improve the detector performance, which can utilize the bidirectional message passing between the neighboring time slots to fully exploit the temporally correlated AS of the UE. Furthermore, the parameters of the BG-MC model can be updated adaptively during the estimation procedure with unknown system statistics. Simulation results show that the proposed algorithm can improve the detection accuracy compared to existing methods while keeping the same order complexity.

Selection of Signal Sources Influence at Indoor Positioning System

The indoor positioning task is an exciting problem because of the practical applications and the scientific challenges. This work emphasises the impact of signal source selection on the effectiveness of positioning models relying on Wi-Fi received signal strength data by eliminating unstable signal sources such as mobile access points. Two localisation algorithms, based on k-Nearest Neighbour and Random Forest, were applied to data from three academic buildings to test the effectiveness of various access points (APs) selection filters. Additionally, new hierarchical selection models were proposed. The methods divide building into zones to improve the characteristics of the positioning error by a local selection of APs. Hierarchical methods reduced the median number of AP to 67 in comparison to over 100 selected by existing methods, obtaining a statistically similar median error of 6.01 m.

Processing-in-Memory Development Strategy for AI Computing Using Main-Path and Doc2Vec Analyses

Processing-in-Memory (PiM), which combines a memory device with a Processing Unit (PU) into an integrated chip, has drawn special attention in the field of Artificial Intelligence semiconductors. Currently, in the development and commercialization of PiM’s technology, there are challenges in the hegemony competition between the PU and memory device industries. In addition, there are challenges in finding strategic partnerships rather than independent development due to the complexity of technological development caused by heterogeneous chips. In this study, patent Main Path Analysis (MPA) is used to identify the majority and complementary groups between PU and memory devices for PiM. Subsequently, Document-to-Vector (Doc2Vec) and similarity-scoring analyses are used to determine the potential partners for technical cooperation required for PiM technology development for the majority group identified. According to the empirical results, PiM core technology is evolving from PU to memory device with an ‘architecture-operation-architecture’ design pattern. The ten ASIC candidates are identified for strategic partnerships with memory device suppliers. Those partnership candidates include several mobile AP firms, implying PiM’s opportunities in the field of mobile applications. It suggests that memory device suppliers should prepare for different technology strategies for PiM technology development. This study contributes to the literature and high-tech industry via the proposed quantitative technology partnership model.

Joint online 3D trajectory optimisation and power allocation of UAVs as mobile access points in a downlink distributed multi-user MIMO system

Unmanned aerial vehicles (UAVs) are introduced as one of the key-enablers for 5G beyond networks due to their low cost and flexible deployment, and mobility degrees-of-freedom. Moreover, low sensitivity to blockage and uncorrelated channels in distributed multiple-input multiple-output (D-MIMO) systems, and their extension which is called cell-free massive MIMO, have made them an attractive research field. The location and number of access points (APs) in D-MIMO systems highly influence the system performance in terms of energy and spectral efficiency that can be handled by UAVs mobility and flexible deployment. In this paper, joint power allocation, and 3D trajectory design in a D-MIMO system for a downlink scenario where multiple UAVs serve as mobile APs (MbAPs) is investigated. Contrary to the existing works in multi-mobile base stations, we introduce space-division multiple access (SDMA) for the online trajectory design as a multi-access technology which does not suffer from the non-casual channel property in traditional trajectory designs. In the proposed design, the smallest user’s ergodic rate lower bound is maximised by jointly optimising MbAPs’ 3D trajectory and transmit power. The block coordinate descent (BCD) algorithm is deployed to break the main problem into two sub-problems and iteratively optimise over the two. Considering the non-convex nature of both of sub-problems, successive convex approximation is also exploited. For performance analysis, complexity and convergence property of the proposed algorithm are investigated. Finally, numerical results validate the improved performance of the proposed system in a practical scenario, where users can connect and disconnect to and from the network.

Throughput Maximization of Wireless-Powered Communication Network With Mobile Access Points

In order to mitigate the <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">double near-far effect</i> , we focus on a mobile wireless-powered communication network (WPCN), where sensor nodes harvest energy from the radio frequency (RF) signal of the mobile energy access point (EAP), and transmit data to the mobile data access point (DAP) by using the harvested energy. Only the sensor nodes with energy larger than a threshold, which is mainly determined by the energy consumption of one transmission, have opportunities to transmit data. Due to the mobility of the EAP and DAP, the distance between the EAP and DAP changes over time. When the DAP moves into the operation region of the EAP, the EAP and DAP could not work simultaneously due to the severe interference, and an energy harvesting probability is employed to denote the probability that the EAP works in this scenario. The purpose of this paper is to identify the optimal transmission policy, i.e., the optimal pairing of the energy consumption of one transmission and the energy harvesting probability, that maximizes the throughput of the WPCN under an energy causality constraint. By analyzing the energy causality constraint, we show that the WPCN could be divided into an energy-sufficient state and an energy-limited state by the pairing of the energy consumption of one transmission and the energy harvesting probability. Since the energy consumption of one transmission and the energy harvesting probability are jointly intertwined with the energy causality constraint, making the joint optimization problem intractable, we divide the throughput maximization problem into two layers. In the inner problem, we investigate the optimal energy consumption of one transmission with a given energy harvesting probability. In the outer problem, we derive the optimal energy harvesting probability based on the obtained optimal energy consumption of one transmission. According to the aforementioned investigations, we propose a two-layer algorithm to obtain the specific solution. Numerical results are conducted to validate the theoretical results and the efficiency of the proposed two-layer algorithm.

Deep learning-based energy harvesting with intelligent deployment of RIS-assisted UAV-CFmMIMOs

The ever-evolving internet of things (IoT) has spawned hundreds of wireless sensors that communicate via the internet infrastructure. The lifetime and self-sustainability of these sensors are pivotal factors dictating the performance of respective application infrastructure. Radio frequency energy harvesting (RFEH) technology has exhibited the capability of effectively augmenting the battery lifetime of these sensors. In this work, we introduce a novel framework called CURe, which combines the advantages of cell-free massive multiple-input multiple-output (CFmMIMO) and reconfigurable intelligent surfaces (RISs) to provide uninterrupted energy harvesting for IoT devices through RFEH. CFmMIMO integrates the advantages of distributed systems and massive MIMO, while RIS improves the signal strength of the information transfer and RFEH via its passive reflection capabilities. Moreover, we consider unmanned aerial vehicles (UAVs) equipped with CFmMIMO as mobile access points (APs) to better serve the moving sensory devices. To further enhance RFEH, we propose DeNCE, a channel estimation technique based on deep learning (DL) that eliminates the need for traditional closed-form equation-based channel estimation methods. Through evaluation, we first validate the performance of CURe by comparing it with the modified bisection search for max–min fairness (MBS-MMF) algorithm and later corroborate that DeNCE significantly improves the performances of both models. Finally, to optimize the UAV deployment and ensure continuous RFEH coverage, we propose dARL, a deep reinforcement learning (DRL)-based scheduling framework that enables UAV-CFmMIMO swarms to perform continuous energy harvesting in the coverage area collaboratively.

FlyReflect: Joint Flying IRS Trajectory and Phase Shift Design Using Deep Reinforcement Learning

Aerial access infrastructures have been considered a compulsory component of the sixth-generation (6G) networks, where airborne vehicles play the role of mobile access points to service ground users (GUs) from the sky. In this scenario, intelligent reflecting surface (IRS) is one of the promising technologies associated with airborne vehicles for coverage extensions and throughput improvements, a.k.a., flying IRS (F-IRS). This study considers a multiuser multiple-input single-output (MISO) F-IRS system, where the F-IRS reflects downlink signals from ground base stations (BSs) to users located at underserved areas where direct communications are unavailable. To achieve the system sum-rate maximization, we proposed a deep reinforcement learning (DRL) algorithm named <monospace xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">FlyReflect</monospace> to jointly optimize the flying trajectory and IRS phase shift matrix. First, end-to-end communications from a BS to its GUs via the F-IRS are analyzed to identify environmental and operational factors that impact achievable system sum rate. Subsequently, the system is transformed into a DRL model, which is resolvable by the deep deterministic policy gradient (DDPG) algorithm. To improve the action decision accuracy of the DDPG algorithm, we proposed a mapping function to guarantee that all constraints are satisfied regardless of noise additions in the exploration process. Simulation results showed that our proposed algorithm outperforms state-of-the-art algorithms in multiple scenarios.

Energy-Efficient Trajectory Planning for Smart Sensing in IoT Networks Using Quadrotor UAVs.

Quadrotor unmanned aerial vehicles (UAVs) are widely used as flexible and mobile access points and information carriers for the future Internet of Things (IoT). This work studies a quadrotor UAV-assisted IoT network, where the UAV helps to collect sensing data from a group of IoT users. Our goal is to optimize the UAV's overall energy consumption required to complete the sensing task. Firstly, we propose a more accurate and mathematically tractable model to characterize the UAV's real-time energy consumption, which accounts for the UAV's dynamics, brushless direct current (BLDC) motor dynamics and aerodynamics. Then, we can show that the UAV's circular flight based on the proposed energy-consumption model consumes less energy than that of hover flight. Therefore, a fly-circle-communicate (FCC) trajectory design algorithm, adopting Dubins curves for circular flight, is proposed and derived to save energy and increase flight duration. Employing the FCC strategy, the UAV moves to each IoT user and implements a circular flight in the sequence solved by the travelling-salesman-problem (TSP) algorithm. Finally, we evaluate the efficiency of the proposed algorithm in a mobile sensing network by comparing the proposed algorithm with the conventional hover-communicate (HC) algorithm in terms of energy consumption. Numerical results show that the FCC algorithm reduces energy consumption by 1-10% compared to the HC algorithm, and also improves the UAV's flight duration and the sensing network's service range.

Strategies to increase catch-up vaccination among migrants: a qualitative study and rapid review

Abstract Background WHO’s Immunization Agenda 2030 has placed renewed focus on catch-up vaccination across the life course to meet global targets for reduction in vaccine-preventable diseases through increased vaccine coverage, including among migrant groups who may require catch-up vaccination to align them with host country vaccination schedules. Methods We did a global rapid review (01/2010 to 04/2022) to explore drivers of vaccine hesitancy among migrants followed by an in-depth qualitative study (semi-structured, telephone interviews) among recently arrived adult migrants (foreign-born, &amp;gt;18 years old, residing in the UK &amp;lt; 10 years). Interviews explored views on routine vaccination including accessibility, confidence and awareness. Data were analysed iteratively using thematic analysis. Results 63 papers were included in the rapid review, including data from 22 countries/regions. Multiple factors driving under-immunisation and hesitancy in migrants were reported, including language barriers, low health literacy, social exclusion, low cultural competency and accessibility in healthcare systems. Our qualitative study recruited 40 migrants (mean age: 36.7 years; 62.5% female) resident in the UK (6 refugees, 19 asylum-seekers, 8 undocumented, 7 labour migrants). Major barriers to catch-up vaccination included a lack of provider recommendation and low awareness, with vaccination viewed as only relevant to children. Hesitancy around specific vaccines, such as MMR, was often influenced by misinformation. Participants suggested that novel strategies such as walk-in or mobile access points, consistent provider recommendations, and translation of information into relevant languages, may enhance accessibility and uptake of routine vaccinations. Conclusions Targeted and tailored information campaigns, versatile and proactive access pathways and education for healthcare staff on cultural competency will be needed to ensure uptake of catch-up vaccination among marginalised migrant groups. Key messages • Newly arrived adult migrants face barriers to catch-up vaccination in host countries, which may hinder immunisation coverage and increase the risk of vaccine-preventable disease outbreaks. • Health systems must develop novel mechanisms to proactively offer culturally competent and accessible catch-up vaccination services to adult migrants on and after arrival.

Spatial-Temporal Value-of-Information Maximization for Mobile Crowdsensing in Wireless Sensor Networks

Mobile crowdsensing is considered as a promising technology to exploit the computing and sensing capabilities of the decentralized wireless sensor nodes. Typically, the quality of information obtained from crowdsensing is largely affected by various factors, such as the diverse requirements of crowdsensing tasks, the varying quality of information across different crowd workers, and the dynamic changes of channels conditions and the sensing environment. In this paper, considering the dynamics’ of the crowd workers, we focus on a spatial-temporal crowdsensing model and aim to maximize the value of information at the point of interest, by optimizing the recruiting range and time duration for the crowd workers. In particular, the crowdsensing system includes a mobile access point (MAP) and a set of wireless sensor nodes. As the information requester, the MAP can broadcast its crowdsensing task and then estimate the value of information by collecting the responses from the sensing nodes. Each sensing node in the crowdsensing task will receive a payment from the MAP. We aim to maximize the utility of the information requester by optimizing the recruiting range and waiting time for the sensing nodes. We firstly define a set of value metrics to characterize the MAP’s value of information. The optimal recruiting range can be obtained in closed-form expressions. Furthermore, considering the aging effect, we propose a gradient-based method to maximize the spatial-temporal value of information. Specifically, we first determine the optimal recruiting time for the requester and then choose the optimal recruiting range within each time slot. Via simulation, we first compare the sum, max, and min values of information at the requester, and then verify the effectiveness of the gradient-based method to optimize the recruiting time and range to maximize the value of information.

Caching of popular content at F‐RAN for mmWave new radio

SummaryFog computing allows for local data processing at the edge of the network. This allows for reduced link delays between mobile users and access points. Here, fog nodes are collocated with radio remote heads (RRHs) to provide local processing capabilities, that is, creating fog radio access network (F‐RAN) that can support ultralow latency for cellular networks that operate on millimeter wave (mmWave) bands. Here, mobile stations (MS) demand various network functions (NFs) that correspond to different service requests. Hence, it is critical to study function popularity and allow content caching at the F‐RAN. Given the limited resources at the fog nodes, it is important to efficiently manage the resources to improve network operations and enhance the capacity at reduced delays and cost. Therefore, caching in mmWave F‐RAN requires node allocation that can accommodate the highest number of cached functions. Hence, this paper proposes a novel node placement scheme that leverages Nelder–Mead meta‐heuristic. Results show that the proposed scheme yields in reduced delay, cost, and power and energy consumption.

Incentive-Based Schema Using Game Theory in 5/6G Cellular Network for Sustainable Communication System

Due to the technological advancement in cellular networks, massive data traffic appends to the existing digital technologies. These emerging digital technologies face quality of service (QoS) challenges, particularly when it comes to maintaining the tradeoffs between customers and service providers. The cellular service providers are trying to meet the needs of end users by handling four substantially different types of data, i.e., Real Time, Video, Audio, and Text, with each type having its own requirements. To achieves an efficient QoS, different incentive-based algorithms were proposed. However, these schemes do not ensure a fair distribution of profit among the mobile network operator and access points. Furthermore, these schemes do not provide efficient QoS to the end user and cannot ensure a fair distribution of channels in crowning time. We propose an incentive-based scheme using game theory and two-stage Stackelberg approach for integrated data, offloading the decision-making process in a heterogeneous network. A single mobile base station and some integrated access points in a crowded metropolitan area are modeled in our proposed scheme. This station offers an economic incentive based on traffic types, and access points compete with each other to earn incentives for offload traffic. A mathematical game is derived to analyze the real-world scenario through simulation. The experimental method is applied to validate the numerical outcomes by comparing the results with other models.

Subspace Differential Privacy

Many data applications have certain invariant constraints due to practical needs. Data curators who employ differential privacy need to respect such constraints on the sanitized data product as a primary utility requirement. Invariants challenge the formulation, implementation, and interpretation of privacy guarantees. We propose subspace differential privacy, to honestly characterize the dependence of the sanitized output on confidential aspects of the data. We discuss two design frameworks that convert well-known differentially private mechanisms, such as the Gaussian and the Laplace mechanisms, to subspace differentially private ones that respect the invariants specified by the curator. For linear queries, we discuss the design of near-optimal mechanisms that minimize the mean squared error. Subspace differentially private mechanisms rid the need for post-processing due to invariants, preserve transparency and statistical intelligibility of the output, and can be suitable for distributed implementation. We showcase the proposed mechanisms on the 2020 Census Disclosure Avoidance demonstration data, and a spatio-temporal dataset of mobile access point connections on a large university campus.

CROWDSOURCED WIFI FINGERPRINT LOCALIZATION IN URBAN CANYON

Abstract. Although Global Navigation Satellite System (GNSS) has achieved success in outdoor localization, it does not often work well in urban canyon, which is due to the weak signals and the loss of satellites. WiFi technology is widely used at present, and the crowdsourced WiFi data has the advantages of rich sources and low cost. Therefore, utilizing the crowdsourced WiFi data for localization may effectively improve the deficiency of GNSS in the urban canyon. In this paper, we propose a novel method of crowdsourced WiFi fingerprint localization in urban canyon. Considering that the crowdsourced data is noisy, discontinuous and unstable, we carry out pre-processes for data refining, and grid-based statistical method for noise smoothing. Then in order to quickly locate the terminals in large-scale area, the AP coverage intersection method is proposed, in which the coverage range, centers and density of all APs are inferred, and the personal hotspots as well as the mobile APs are removed. To further enhance the positioning accuracy, the fine localization is carried out, which is based on the iterative KWNN algorithm. Extensive field tests are carried out in a typical urban canyon, results show that the average positioning error of our method is 16.82 m, which shows the effectiveness of the proposed method for crowdsourced positioning in urban canyon.

An UAV-assisted mobile edge computing offloading strategy for minimizing energy consumption

Owing to the high economic benefits, flexible deployment, and controllable maneuverability, unmanned aerial vehicles (UAVs) have been envisioned as promising and potential technologies for dispensing wireless communication services. This paper investigates a mobile edge computing (MEC) system assisted by multiple access points (APs) and an UAV, in which APs may not be able to straightly establish wireless communications with terrestrial Internet of Thing devices (IoT) due to ground signal blockage. Consequently, an UAV is dispatched as a mobile AP to serve a group of users and render the air-to-ground channel. In this scenario, we contemplate dividing the computing tasks of IoTDs into three parts: either be calculated locally, or offloaded to the UAV for processing, or accomplished on AP through relaying. This work attempts to minimize the weighted sum of communication consumption, calculation consumption, and the UAV’s flight consumption over a finite UAV mission duration by jointly optimizing calculation task allocation ratio, power distribution as well as the UAV’s trajectory. However, the resulting problem we put forward is demonstrated to be highly non-convex and challenging to solve. To tackle this issue, we decompose the original problem into two sub-problems hinging on the block coordinate descent (BCD) method. We settle the two sub-problems iteratively through the Lagrangian duality method and succession convex approximation (SCA) technique. The simulation results further reveal that the proposed approach is superior to other comparison baselines.

Integrated Solutions for Deployment of 6G Mobile Networks

The promise of future generations of mobile communication, both 5.5 and 6G, is that of continued increases in capacity and coverage of the wireless communication network together with novel applications demanding higher levels of performance between the mobile devices and their applications on servers in the cloud. In addition, the growing shift towards virtualization of network function itself offers perhaps the most stringent requirements on the network. To continue to offer ever higher capacities and speeds, future networks will be required to further densify addressing capacity and coverage demands through the use of small cells or new radio spectral bands, such as 7-20GHZ or sub-THZ, with inherently shorter range. Therein lies the dilemma. A denser and ever more complex network of access points, with higher speeds and performance, but continued pressure on the deployment and costs. To address this challenge solutions which address all aspects of the End to End network must be considered together &#x2013; the mobile access points, transport networks, deployment options, and edge cloud all contribute equally to the success of the new consumer and industrial applications promised for next generation networks as well as the operation of the networks themselves. In this paper we will outline the expected requirements of next generation networks as well as new technologies and methods which address the simultaneous challenges of higher capacity, lower costs, and reliable deterministic performance.

A Survey on Reinforcement Learning-Aided Caching in Heterogeneous Mobile Edge Networks

Mobile networks experience a tremendous increase in data volume and user density due to the massive number of coexisting users and devices. An efficient technique to alleviate this issue is to bring the data closer to the users by exploiting cache-aided edge nodes, such as fixed and mobile access points, and even user devices. Meanwhile, the fusion of machine learning and wireless networks offers new opportunities for network optimization when traditional optimization approaches fail or incur high complexity. Among the various machine learning categories, reinforcement learning provides autonomous operation without relying on large sets of historical data for training. In this survey, reinforcement learning-aided mobile edge caching solutions are presented and classified, based on the networking architecture and optimization target. As sixth generation (6G) networks will be characterized by high heterogeneity, fixed cellular, fog, cooperative, vehicular, and aerial networks are studied. The discussion of these works reveals that there exist reinforcement learning-aided caching schemes with varying complexity that can surpass the performance of conventional policy-based approaches. Finally, several open issues are presented, stimulating further interest in this important research field.

Indoor Localization System Based on Mobile Access Point Model MAPM Using RSS With UWB-OFDM

Indoor tracking is one of the most attractive topics in communication and information technology. Indoor localization mechanisms can help people for navigating in complex environments. Those with complex structures such as airports, shopping malls, hospitals, and others have a massive population (Crowdsourcing) that blocks the visibility (NLOS) between the access points and the users. Accordingly, we present the mobile access point model MAPM as a new algorithm for positioning users indoors based on the mathematical model of received signal strength RSS using UWB-OFDM. MAPM participates in crowdsourcing indoors by using users as mobile access points (MAP). That may decrease the use of several static access points (SAP) indoors and increase the localization precision. First, we use ultra-wideband UWB with orthogonal frequency division multiplexing OFDM as a communication gadget between the users and the system. Those have good quality in terms of accuracy, time response, energy consumption, and efficiency against interference. Second, we measure the received signal strength then, we estimate the distance between users and the access points using the mathematical model of RSS. These can be more adapted to changing environments and device heterogeneity than RSSI fingerprints. Third, we calculate the position information by using the Euclidean distance formula. In this way, we can reduce the effect of most of the problems on the indoor positioning system. We simulate the proposed algorithm in a platform based on three tools, MATLAB for system information processing, MYSQL as a system database, and a control interface coded in Java. In the given simulation results, we find that the average MAPM error in the case of 30 users is 10.19 cm, and the detection rate has a value of 86.66%.