Location Of Access Points Research Articles

Multi-AP and Test Point Accuracy of the Results in WiFi Indoor Localization.

WiFi-based indoor positioning has attracted intensive research activities. While localization accuracy is steadily improving due to the application of advanced algorithms, the factors that affect indoor localization accuracy have not been sufficiently understood. Most localization algorithms used in changing indoor spaces are Angle-of-Arrival (AoA) based, and they deploy the conventional MUSIC algorithm. The localization accuracy can be achieved by algorithm improvements or joint localization that deploys multiple Access Points (APs). We performed an experiment that assessed the Test Point (TP) accuracy and distribution of results in a complex environment. The testing space was a 290 m three-room environment with three APs with 38 TPs. The joint localization using three APs was performed in the same test space. We developed and implemented a new algorithm for improved accuracy of joint localization. We analyzed the statistical characteristics of the results based on each TP and show that the local space-dependent factors are the key factors for localization accuracy. The most important factors that cause errors are distance, obstacles, corner locations, the location of APs, and the angular orientation of the antenna array. Compared with the well-known SpotFi algorithm, we achieved a mean accuracy (across all TPs) improvement of 46%. The unbiased joint localization median accuracy improved by 20% as compared to the best individual localization.

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.

A Novel RSSI Fingerprint Positioning Method Based on Virtual AP and Convolutional Neural Network

Indoor Wi-Fi positioning based on Received Signal Strength Indicator (RSSI) has been widely used in all kinds of location-based services. However, the accuracy of positioning is susceptible to the sharp fluctuation of RSSI and the low sampling density. To address these difficulties, this paper proposes a novel fingerprint positioning method combining the virtual Access Point (AP) technique with Convolutional Neural Network (CNN) classification model. In our proposed method, a data augmentation method is designed to improve the positioning accuracy with few samples. The virtual AP location obtained by the distance ratio positioning method is utilized to adaptively modify the inputs of the CNN model. This new CNN model is used to determine the area of the samples, whereas a virtual AP method computes the accurate position of the samples. The experimental results show that the accuracy of area determination is up to 91%, and 95% of the positioning error is controlled within 2 meters. Compared with the existing positioning methods, the proposed method has a significant improvement in terms of positioning accuracy.

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.

SPARSE CELLULAR RADIO NETWORK

The wireless telecommunications environment is characterized by high variation in signal strength, and architectural constraints are imposed on the placement of radio access points. As an extreme case, it can be assumed that the location of the radio access points in such systems is arbitrary. Such radio systems are proposed to be called sparse cellular radio networks. This term is proposed by analogy with the well-known definition of a sparse array from the field of programming.

Indoor Localization with a Single Access Point Based on TDoA and AoA

Indoor localization in 5G and the Internet of Things has been paid increasing attentions. Recent advances have demonstrated reasonable localization accuracy that can be provided by using a single access point (AP). Yet, existing single AP localization solutions require frequency hopping or suffer from degraded accuracy performance due to complicated indoor propagation environment. This paper proposes a new single AP indoor localization method based on time difference of arrival (TDoA) and angle of arrival (AoA). We divide multiple antennas into two groups, one group with close distance for AoA estimation and the other group with the farther distance for TDoA estimation. Then, a joint estimation of AoA and ToA is carried by a spatial smoothing-based multiple signal classification (MUSIC) algorithm. Finally, we propose a weighted least squares method based on TDoA and AoA estimations to achieve indoor localization. Simulation results verify the effectiveness of the proposed single AP localization method.

Проблемы построения беспроводных сетей связи в парковой зоне

Today, all spheres of our life are closely connected with the transmission of information on the Internet. Walking or exercising now also requires a good internet connection to listen to music, send telemetry data for sports programs, or make audio or video calls with family or for work. Unfortunately, mobile operators are not always able to provide a good Internet connection, and do not forget that the use of the mobile Internet leads to a rapid discharge of the cell phone battery. Therefore, covering a park area with a wireless network based on Wi-Fi technology becomes an urgent and challenging task. One of the difficulties is that there are a large number of trees in the park areas. The radio signal changes significantly when it passes through the foliage. It is affected by a large number of negative factors such as reflection, attenuation, and so on. This leads to difficulties in choosing the location of access points. The article provides an approximate diagram of the wireless network of a small park. There is also a description of the main difficulties that engineers encounter when designing such networks. Moreover, the article provides options for solving problems that are ignited in the process of developing these networks.

Cluster Routing-Based Data Packet Backhaul Prediction Method in Vehicular Named Data Networking

Vehicular named data networking (V-NDN) is a network architecture that combines named data networking (NDN) and vehicular ad hoc networks (VANETs). Due to the high-speed mobility of the on-board unit (OBU) in V-NDNs, topological changes may cause the problem of reverse path breaking for data packets, thus impacting the communication quality of service (QoS) among vehicles. To address this issue, a data packet backhaul prediction method (DBPM) based on cluster routing in the V-NDN is proposed in this paper. The DBPM uses GPS and a convex programming location algorithm (CPLA) at roadside units (RSUs) to obtain the positioning information of vehicle in the clusters, and uses two positioning data items to predict the location of the vehicle's future access point (AP) for the cluster by using the Kalman filtering model. Then, the DBPM forwards the returned data packets to the vehicle by the cluster. Simulation experiments are performed by using the simulators Simulation of Urban Mobility (SUMO) and VanetMobiSim. Results show that the proposed DBPM can effectively reduce the average delay and packet loss ratio in the vehicle-to-infrastructure (V2I) communication in urban scenes, thus enhancing the robustness of data transmission and effectively supporting the data communication's QoS of V-NDN.

A Time Series-Based Study of Correlation, Channel Power Imbalance, and Diversity Gain in Indoor Distributed Antenna Systems at 60 GHz

In this article, we investigate the potential enhancements in signal reliability which can be achieved using a millimeter-wave distributed antenna system (DAS) within an indoor environment. To achieve this, we measured the signal power simultaneously received at nine ceiling-mounted access point (AP) locations likely to be used in future indoor DAS deployments while a mobile user imitated making a voice call on a hypothetical user equipment. Key metrics, associated with the performance of multiple antenna systems, such as the cross correlation coefficient (CCC) and channel power imbalance (CPI) are determined. It was found that line-of-sight (LOS) and quasi-LOS (QLOS) links with the APs typically led to higher CCC values than the non-LOS (NLOS) cases. Similarly, LOS and QLOS links typically produced higher CPI values between APs than the NLOS case. To enable the reproduction of our results, we have successfully applied autoregressive moving average and autoregressive integrated moving average modeling to the CCC and CPI time series. The performance improvement that can be achieved using a DAS instead of a single AP was evaluated using three commonly deployed diversity combining schemes, namely, selection combining , equal gain combining , and maximal ratio combining along with three AP selection mechanisms, namely, per-sample random AP selection , one-shot AP selection , and per-sample optimal AP selection . Finally, we have provided some useful insight into the influence of differing AP numbers on the diversity gain when considering the aforementioned AP selection methods.

Genetic Algorithm Applied to Planning IEEE 802.11g Networks

The problem of planning local wireless network IEEE 802.11g consists of automatically positioning and setting up wireless access points (APs) in order to provide access to the local network with the desired coverage and the required quality of service (QOS).In addition to the complexity of predicting the Quality of Service (QoS) of a network from the variables of the problem (positions, parameters and frequency of the APs), the planning of WLAN networks faces several difficulties. In particular, the location of APs and the allocation of frequencies. There is no single model to solve the problem of designing wireless local networks. Depending on the situations and the hypotheses studied, different criteria can be considered and expressed in terms of constraints to be observed or in terms of objectives to be optimized. The first distinction is to separate the financial criteria from the network quality criteria. The nature of these two criteria being fundamentally different. Then there are a variety of service quality criteria, but we can still group them into three main categories: coverage criteria, interference criteria and capacity criteria.. In this article, we will use an optimization method based on an algorithm of stochastic optimization, which is also based on the mechanisms of natural selection and of genetic. It is genetic algorithm. Our goal consist of minimizing the total interaction between the APs to perform the good choices when deploying a network 802.11g in a way that gives users signal-to-interference ratios (SIR) greater than the required threshold ß.

Toward the Energy-Saving Optimization of WLAN Deployment in Real 3-D Environment: A Hybrid Swarm Intelligent Method

The deployment of access points (APs) imposes a great impact on the performance of wireless local area networks (WLANs). Although there have been plenty of works on the optimal AP deployment (OAD), however, the issue remains open that how to deploy APs energy-savingly in 3-D environments while considering some practical demands. Therefore, this article investigates the OAD with the objective of minimizing the total AP transmission power while guaranteeing the effective coverage by jointly optimizing the transmission power and location of each AP. Different from existing works, our article fully considers not only real 3-D environments with various obstacles but also the differentiated coverage demand and the restrictions on AP locations. To solve this NP-hard optimization problem, this article first proposes a novel hybrid swarm intelligence optimization algorithm, named PSO-Lévy-DFOA. The computational results on 16 benchmark functions and comprehensive comparisons with other algorithms demonstrate the superior global convergence performance of PSO-Lévy-DFOA. The proposed PSO-Lévy-DFOA-based OAD method is then applied to WLAN planning in a two-story building. It has been shown that the total AP transmission power can be considerably reduced compared to the current deployment while still fulfilling the effective coverage.

Geodata-driven approaches to financial inclusion – Addressing the challenge of proximity

Financial Inclusion is, in many ways, a spatial planning issue: Where do financial institutions provide services, how far do customers travel to access mobile money, which services are available where and how is agent cash-flow handled? Utilising geodata can contribute significantly to measuring financial access and thus assist in improving Financial Inclusion by expanding the reach of services and locating areas of economic exclusion. This study presents a new Spatial Decision Support System, with a frontend embedded directly within a spreadsheet interface, that enables measuring and planning financial access through geospatial analysis and Earth Observation derived products. The purpose is to complement existing Financial Inclusion measures, which rely significantly on large-scale representative household surveys to quantify financial access and opportunity to proxy quality of inclusion. The Decision Support System relies on Earth Observation and Public Participatory GIS, which enables a decoupling from the census cycle and global reach. Our findings indicate that a geospatial approach to measuring and making decisions regarding the location of financial access points can positively affect both tracking and delivering Financial Inclusion and reducing the urban–rural service cliff-edge. Our proposed geospatial methodology is useful for decision-makers in two ways: a) It allows the measurement of the large-scale geospatial reach of financial services – useful for decision-makers, planners, politicians, national statistical offices, and NGOs in charge of tracking progress towards the Sustainable Development Goals. b) It helps with planning and optimising services for local financial entities such as mobile money agents, brick and mortar bank branches, and less formal saving mechanisms such as saving clubs. The Spatial Decision Support System is currently used by several Financial Service Providers in Ghana and undergoing implementation for one in North-western Tanzania.

A Joint Rogue Access Point Localization and Outlier Detection Scheme Leveraging Sparse Recovery Technique

With the pervasive deployments of Access Point (AP) in Wireless Local Area Network (WLAN), rogue AP has emerged as such a large threat to user's privacy, that it is expected to be detected and located accurately. Hence, in this paper, we propose a novel rogue AP localization scheme leveraging sparse recovery technique, which consists of three steps: 1) Coarse localization, which is the result of comparing the online records with clustered fingerprint database. A novel Object Weighting Affinity Propagation (OWAP) clustering method is proposed to group the offline fingerprints. When computing the similarities, unlike traditional affinity propagation clustering method which views each object equally, we utilize the prior physical coordinates information to assign weight to each object. 2) Compressive sensing (CS) kernel optimization, in which the minimum number requirement of monitors in localization system is deduced through analyzing the problem formulation theoretically, and an Equiangular Tight Frame (ETF) based monitors selection scheme is presented to achieve higher location accuracy. 3) Joint fine rogue AP localization and outlier detection through a formulation of an improved CS based sparse recovery model. It could localize the rogue AP and identify the monitors whose readings are either not available or erroneous simultaneously, which increases the localization robustness. We operate simulations as well as experiments to verify the superiority of the proposed scheme both theoretically and practically.

Achievable Rate Characterization of NOMA-Aided Cell-Free Massive MIMO With Imperfect Successive Interference Cancellation

This paper investigates the throughput improvement of cell-free massive multiple-input multiple-output (MIMO) systems by non-orthogonal multiple access (NOMA) for future cellular networks under stochastic access point and user locations. In this context, the node locations are modeled with Poisson point processes. The time division duplexing mode is employed, and uplink channels are estimated locally using uplink pilots. Furthermore, unique pilot sequences are used between NOMA clusters, while pilot reuse occurs within each cluster to strike a balance between the training overhead and the number of clusters. Matched-filter-based precoding is utilized for downlink transmission. The aggregate received signal is analytically characterized by deriving the moment generating function and approximations via moment matching. Then, the asymptotic achievable rates of the NOMA users are derived, thereby quantifying the adverse impact of error propagation owing to imperfect successive interference cancellation. Special scenarios with prior downlink channel state information and log-distance power control are also considered. We show that NOMA greatly increases the achievable average rate, especially under low path loss exponents and dense networks, while user fairness may be boosted by the adoption of a log-distance transmit power control scheme with proper parameter selection (i.e. lower values for the power control parameter).

Energy Efficiency of Uplink Cell-Free Massive MIMO With Transmit Power Control in Measured Propagation Channel

Cell-free massive MIMO (CF-mMIMO) provides wireless connectivity for a large number of user equipments (UEs) using access points (APs) distributed across a wide area with high spectral efficiency (SE). The energy efficiency (EE) of the uplink is determined by (i) the transmit power control (TPC) algorithms, (ii) the numbers, configurations, and locations of the APs and the UEs, and (iii) the propagation channels between the APs and the UEs. This paper investigates all three aspects, based on extensive (~30,000 possible AP locations and 128 possible UE locations) channel measurement data at 3.5 GHz. We compare three different TPC algorithms, namely maximization of transmit power (max-power), maximization of minimum SE (max-min SE), and maximization of minimum EE (max-min EE) while guaranteeing a target SE. We also compare various antenna arrangements including fully-distributed and semi-distributed systems, where APs can be located on a regular grid or randomly, and the UEs can be placed in clusters or far apart. Overall, we show that the max-min EE TPC is highly effective in improving the uplink EE, especially when no UE within a set of served UEs is in a bad channel condition and when the BS antennas are fully-distributed.

Multi-Criteria Prediction Mechanism for Vehicular Wi-Fi Offloading

The growing demands of vehicular network applications, which have diverse networking and multimedia capabilities that passengers use while traveling, cause an overload of cellular networks. This scenario affects the quality of service (QoS) of vehicle and non-vehicle users. Nowadays, wireless fidelity access points Wi-Fi access point (AP) and fourth generation long-term evolution advanced (4G LTE-A) networks are broadly accessible. Wi-Fi APs can be utilized by vehicle users to stabilize 4G LTE-A networks. However, utilizing the opportunistic Wi-Fi APs to offload the 4G LTE-A networks in a vehicular ad hoc network environment is a relatively difficult task. This condition is due to the short coverage of Wi-Fi APs and weak deployment strategies of APs. Many studies have proposed that offloading mechanisms depend on the historical Wi-Fi connection patterns observed by an interest vehicle in making an offloading decision. However, depending solely on the historical connection patterns affects the prediction accuracy and offloading ratio of most existing mechanisms even when AP location information is available. The present study proposed a multi-criteria wireless availability prediction (MWAP) mechanism, which utilizes historical connection patterns, historical data rate information, and vehicular trajectory computation to predict the next available AP and its expected data capacity in making offloading decisions. The proposed mechanism is decentralized, where each vehicle makes the prediction by itself. This characteristic helps the vehicle users make a proactive offloading decision that maintains the QoS for different applications. A simulation utilizing MATLAB was conducted to evaluate the performance of the proposed mechanism and benchmark it with related state-of-the-art mechanisms. A comparison was made based on the prediction error and offloading ratio of the proposed mechanism in several scenarios. The MWAP mechanism exhibited a lower prediction error (i.e., below 20%) and higher offloading ratio (i.e., above 90%) than the existing mechanisms for several tested scenarios.

On Distance Statistics of First Arriving Multi-Paths in Indoor mm-Wave Communications

This paper presents a mathematical framework to evaluate distance statistics of first arriving multi-path components at millimeter wave (mm-Wave) frequencies in a typical indoor environment. The proposed modeling approach is based on a numerically tractable channel model that accounts for the line-of-sight (LoS) component and first-order reflection components from surrounding walls. Based on the derived statistics, an approximation for coverage probability of mm-Wave indoor wireless local area networks (WLAN)s is obtained. Further, the coverage probability for an access point (AP) to user node link is determined after accounting for blockage of LoS component and optimal antenna beamforming in the modeling framework. The analysis of coverage probability provides useful insights which can be applied to plan optimum AP location layout in a typical mm-Wave indoor WLAN.

Two-Tier Communication for UAV-Enabled Massive IoT Systems: Performance Analysis and Joint Design of Trajectory and Resource Allocation

In this article, we propose a two-tier communication strategy to facilitate data collection in unmanned aerial vehicle (UAV)-enabled massive Internet of Things (IoT) systems through introducing ground access points (APs) to serve between the UAV and IoT devices. In the first tier of our proposed strategy, all IoT devices transmit their packets to their local APs via a multi-channel ALOHA-based random access scheme, while in the second tier, APs deliver their aggregated data to the UAV through coordinated time division multiple access. Thus, our introduced APs not only liberate the UAV from the potential massive IoT congestion but also facilitate the design of UAV's trajectory based on the location of APs. To examine the performance of our strategy, we propose a tractable framework to analyze the average system throughput. We reveal that the average two-tier throughput of each AP monotonically increases with its maximum achievable throughput in the second tier, while the increasing slope becomes steeper with a higher traffic load mean in the first tier. Then, we formulate the joint design of UAV's trajectory and resource allocation as a non-convex optimization problem to maximize the average system throughput while considering the heterogeneous quality of service requirement of each AP. To solve this problem, a low-complexity iterative algorithm is devised based on successive convex approximation. Numerical results demonstrate the substantial average system throughput gain achieved by our proposed strategy and design in the context of massive access, compared to the baseline schemes in the literature.

Minimizing The Number of Channel Switches of Mobile Users in Cognitive Radio Ad-Hoc Networks

Cognitive radio (CR) technology is envisioned to use wireless spectrum opportunistically when the primary user (PU) is not using it. In cognitive radio ad-hoc networks (CRAHNs), the mobile users form a distributed multi-hop network using the unused spectrum. The qualities of the channels are different in different locations. When a user moves from one place to another, it needs to switch the channel to maintain the quality-of-service (QoS) required by different applications. The QoS of a channel depends on the amount of usage. A user can select the channels that meet the QoS requirement during its movement. In this paper, we study the mobility patterns of users, predict their next locations and probabilities to move there based on its history. We extract the mobility patterns from each user’s location history and match the recent trajectory with the patterns to find future locations. We construct a spectrum database using Wi-Fi access point location data and the free space path loss formula. We propose a machine learning-based mechanism to predict spectrum status of some missing locations in the spectrum database. We formulate a problem to select the current channel in order to minimize the total number of channel switches during a certain number of next moves of a user. We conduct an extensive simulation combining real and synthetic datasets to support our model.

Performance Analysis of Cell-Free Massive MIMO Systems: A Stochastic Geometry Approach

Cell-free (CF) massive multiple-input-multiple-output (MIMO) has emerged as an alternative deployment for conventional cellular massive MIMO networks. As revealed by its name, this topology considers no cells, while a large number of multi-antenna access points (APs) serves simultaneously a smaller number of users over the same time/frequency resources through time-division duplex (TDD) operation. Prior works relied on the strong assumption (quite idealized) that the APs are uniformly distributed, and actually, this randomness was considered during the simulation and not in the analysis. However, in practice, ongoing and future networks become denser and increasingly irregular. Having this in mind, we consider that the AP locations are modeled by means of a Poisson point process (PPP) which is a more realistic model for the spatial randomness than a grid or uniform deployment. In particular, by virtue of stochastic geometry tools, we derive both the downlink coverage probability and achievable rate. Notably, this is the only work providing the coverage probability and shedding light on this aspect of CF massive MIMO systems. Focusing on the extraction of interesting insights, we consider small-cells (SCs) as a benchmark for comparison. Among the findings, CF massive MIMO systems achieve both higher coverage and rate with comparison to SCs due to the properties of favorable propagation, channel hardening, and interference suppression. Especially, we showed for both architectures that increasing the AP density results in a higher coverage which saturates after a certain value and increasing the number of users decreases the achievable rate but CF massive MIMO systems take advantage of the aforementioned properties, and thus, outperform SCs. In general, the performance gap between CF massive MIMO systems and SCs is enhanced by increasing the AP density. Another interesting observation concerns that a higher path-loss exponent decreases the rate while the users closer to the APs affect more the performance in terms of the rate.