Location Of Access Points Research Articles

A novel method for measurement points selection in access points localization

Precise localization of access points (APs) has become more and more important with the booming of diverse location based services. AP localization accuracy heavily relies on the locations of measurement points. However, collecting abundant information from the APs usually requires tremendous manual efforts, which greatly compromise the efficiency and accuracy of the localization of the APs. Thus, the selection of appropriate measurement points becomes an important issue to achieve a high localization accuracy with a low-cost method, which has not been fully explored in existing research. To this end, we propose a novel approach to select measurement points for AP localization under the assumption that the number of APs to be located is known in advance. Specifically, an initial point is selected randomly and the locations of APs are estimated according to the measured information. Then, subsequent measurement points are determined by locating the intersection of the coverage areas of APs in real time, so that as many APs as possible can be detected at each measurement point. Both simulation and experimental results show that our approach can reduce the number of measurement points while improving the AP localization accuracy.

Improved Indoor Location Systems in a Controlled Environments

The precise localization by using Wi-Fi Access Point (AP) has become a very important issue for indoor location based services such as marketing, patient follow up and so on. Present AP localization systems are working on specially designed Wi-Fi units, and their algorithms using radio signal strength (RSS) exhibit (relatively) high errors, so industry looks more precise and fast adaptable methods. A new model considering/eliminating strong RSS levels in addition to close distance error elimination algorithm (CDEEA) combined with median filters has been proposed in order to increase the performance of conventional RSS based location systems. Collecting local signal strengths by means of an ordinary WiFi units present on any laptop as a receiver is followed by the application of CDEEA to eliminate strong RSS levels. Median filter is then applied to those eliminated values, and AP based path loss model is generated, adaptivelly. Finally, the proposed algorithm predicts locations within a maximum mean error of 2.96m for 90% precision level. This achievement with an ordinary wifi units present on any commercial laptop is comparably at very good level in literature.

Modeling and Analysis of Opportunistic Beamforming for Poisson Wireless Networks

This paper introduces a model to study both single tier and multitier wireless communication systems consisting of a multitude of wireless access points (AP), and operating according to the classical opportunistic beamforming framework. The AP locations in the proposed network model are determined by using planar Poisson point processes. The extreme value distribution of signal-to-interference-plus-noise-ratio ( ${\rm{ SINR}}$ ) on a beam is of fundamental importance for obtaining performance bounds for such an opportunistic communication system. Two tight distribution approximation results are provided for the distribution of maximum ${\rm{ SINR}}$ on a beam, which is hard to obtain due to correlation structure of the underlying inter-AP interference field, using key tools from stochastic geometry. These approximations hold for general path loss models that satisfy some mild conditions. Simulations and numerical evaluations are presented to validate the results, to provide further insights into the derived approximate maximum beam ${\rm{ SINR}}$ distributions, and to illustrate the utility of these approximations in obtaining performance bounds for opportunistic communication systems having multiple interfering APs. In particular, key performance measures such as beam outage probability and ergodic aggregate data rate of an AP are derived by utilizing the approximated distributions.

Placement Optimization of Energy and Information Access Points in Wireless Powered Communication Networks

The applications of wireless power transfer technology to wireless communications can help build a wireless powered communication network (WPCN) with more reliable and sustainable power supply compared to the conventional battery-powered network. However, due to the fundamental differences in wireless information and power transmissions, many important aspects of conventional battery-powered wireless communication networks need to be redesigned for efficient operations of WPCNs. In this paper, we study the placement optimization of energy and information access points in WPCNs, where the wireless devices (WDs) harvest the radio frequency energy transferred by dedicated energy nodes (ENs) in the downlink, and use the harvested energy to transmit data to information access points (APs) in the uplink. In particular, we are interested in minimizing the network deployment cost with minimum number of ENs and APs by optimizing their locations, while satisfying the energy harvesting and communication performance requirements of the WDs. Specifically, we first study the minimum-cost placement problem when the ENs and APs are separately located, where an alternating optimization method is proposed to jointly optimize the locations of ENs and APs. Then, we study the placement optimization when each pair of EN and AP is colocated and integrated as a hybrid access point, and propose an efficient algorithm to solve this problem. Simulation results show that the proposed methods can effectively reduce the network deployment cost and yet guarantee the given performance requirements, which is a key consideration in future applications of WPCNs.

Capacity Estimation Model for Signalized Intersections under the Impact of Access Point.

Highway Capacity Manual 2010 provides various factors to adjust the base saturation flow rate for the capacity analysis of signalized intersections. No factors, however, is considered for the potential change of signalized intersections capacity caused by the access point closeing to the signalized intersection. This paper presented a theoretical model to estimate the lane group capacity at signalized intersections with the consideration of the effects of access points. Two scenarios of access point locations, upstream or downstream of the signalized intersection, and impacts of six types of access traffic flow are taken into account. The proposed capacity model was validated based on VISSIM simulation. Results of extensive numerical analysis reveal the substantial impact of access point on the capacity, which has an inverse correlation with both the number of major street lanes and the distance between the intersection and access point. Moreover, among the six types of access traffic flows, the access traffic flow 1 (right-turning traffic from major street), flow 4 (left-turning traffic from access point), and flow 5 (left-turning traffic from major street) cause a more significant effect on lane group capacity than others. Some guidance on the mitigation of the negative effect is provided for practitioners.

Vehicle Localization and Velocity Estimation Based on Mobile Phone Sensing

In recent years, smart devices have become ubiquitous. Many of these devices are equipped with global positioning system (GPS), Wi-Fi, and other sensors. With their high mobility, the idea of mobile devices as probes has been attracting more and more attention. Mobility and flexibility offered by smart mobile devices are what traditional fixed sensors lack. However, mobile devices’ power supplies are quite limited. Although GPS is accurate, its high power consumption somewhat limits its accessibility and sustainability. In contrast, Wi-Fi is less power hungry, but at the same time, less accurate. For the sake of sustainability, by adopting Wi-Fi as an alternative to GPS, longer operation is attainable at the cost of losing some degree of accuracy. In this paper, a Wi-Fi-based algorithm based on log-normal probability distribution of distances with respect to received signal strength is proposed. It is suitable for an outdoor environment, where Wi-Fi access points (APs) are abundant. Simulations are conducted over known AP locations, and results show that the proposed algorithm can save, on average, as much as 35% more battery power than GPS does. The average localization error is $\sim 18$ m, and the average velocity estimation error is $\sim 25$ %.

An Information-Based Approach to Precision Analysis of Indoor WLAN Localization Using Location Fingerprint

In this paper, we proposed a novel information-based approach to precision analysis of indoor wireless local area network (WLAN) localization using location fingerprint. First of all, by using the Fisher information matrix (FIM), we derive the fundamental limit of WLAN fingerprint-based localization precision considering different signal distributions in characterizing the variation of received signal strengths (RSSs) in the target environment. After that, we explore the relationship between the localization precision and access point (AP) placement, which can provide valuable suggestions for the design of the highly-precise localization system. Second, we adopt the heuristic simulated annealing (SA) algorithm to optimize the AP locations for the sake of approaching the fundamental limit of localization precision. Finally, the extensive simulations and experiments are conducted in both regular line-of-sight (LOS) and irregular non-line-of-sight (NLOS) environments to demonstrate that the proposed approach can not only effectively improve the WLAN fingerprint-based localization precision, but also reduce the time overhead.

Design of Indoor WLANs: Combination of a ray-tracing tool with the BPSO method

This article presents an approach that combines a ray-tracing tool with the binary version of the particle swarm optimization (BPSO) method to the design of infrastructure-mode indoor wireless local area networks (WLANs). This approach uses the power levels of a set of candidate access point (AP) locations obtained with the ray-tracing tool at a mesh of potential receiver locations or test points to allow the BPSO optimizer to carry out the design of the WLAN. For this purpose, several restrictions are imposed through a fitness function that drives the search toward the selection of a reduced number of AP locations and their channel assignments while keeping low transmission power levels. During the design, different coverage priority areas can be defined, and the signal-to-interference ratio (SIR) levels are kept as high as possible to comply with the quality-of-service (QoS) requirements imposed. The performance of this approach in a real scenario at the authors' premises is reported, showing its usefulness.

Wireless Access Point Localization Using Nonlinear Least Squares and Multi-Level Quality Control

Locations of WiFi access points (APs) are important for WiFi positioning when a propagation model is used. The pre-surveyed propagation parameters, such as the path-loss exponent, are usually not available when localizing the APs in a new environment. This letter introduces a novel method that estimates the AP locations and the parameters of the received signal strength (RSS) propagation model simultaneously using the weighted nonlinear least squares (NLLS) method. This method can run on consumer portable devices autonomously in real time without any a-priori information, and eliminate the need of pre-survey. Another contribution of this letter is to introduce a multi-level quality control mechanism, and utilize the statistical testing method in AP localization and propagation parameters (PPs) determination for the first time. Indoor experiments show that the proposed method provided more promising results than previous methods.

Smartphone‐based WiFi access point localisation and propagation parameter estimation using crowdsourcing

The locations of WiFi access points (APs) are important for WiFi positioning, especially when a propagation model is used. The parameters for the propagation model, such as the pathloss exponent and noise variance, usually are not available when localising APs in a new environment. A crowdsourcing-based prototype system is introduced that automatically generates WiFi databases using the uploaded data during normal usage of smartphones. In this system, the adjustment algorithm is originally used for the estimation of AP localisation and propagation parameters. Preliminary experiments show that the average AP localisation error of the prototype system is about 4.0 m in a typical indoor environment with considerably reduced time and labour costs compared with traditional methods.

Alongshore correspondence of beach users and rip channels at Pensacola Beach, Florida

The potential for rescues and drownings is dependent on the spatial and tem- poral correspondence of an active rip current with the distribution of vulnerable beach users. This short communication examines the alongshore correspondence among the (1) exit locations of the walkovers, (2) the spatial and temporal distribution of beachgoers, and (3) the position of semipermanent accretion rips at Pensacola Beach, Florida. Results suggest that the alongshore distribution of beach users on the beach and in the water corresponds to the location of beach access points, which during this period of time sit directly landward of the three rip channels visible in the camera frame during the busy summer season of 2010. Assuming that the majority of beach users are unable to identify a rip current and avoid the hazard, the selection of swimming locations based on conve- nience suggests that beach safety efforts need to focus on guiding beach users to relatively safe locations away from rip current activity.

Handoff Rate and Coverage Analysis in Multi-Tier Heterogeneous Networks

This paper analyzes the impact of user mobility in multi-tier heterogeneous networks. We begin by obtaining the handoff rate for a mobile user in an irregular cellular network with the access point locations modeled as a homogeneous Poisson point process. The received signal-to-interference-ratio (SIR) distribution along with a chosen SIR threshold is then used to obtain the probability of coverage. To capture potential connection failures due to mobility, we assume that a fraction of handoffs result in such failures. Considering a multi-tier network with orthogonal spectrum allocation among tiers and the maximum biased average received power as the tier association metric, we derive the probability of coverage for two cases: 1) the user is stationary (i.e., handoffs do not occur, or the system is not sensitive to handoffs); 2) the user is mobile, and the system is sensitive to handoffs. We derive the optimal bias factors to maximize the coverage. We show that when the user is mobile, and the network is sensitive to handoffs, both the optimum tier association and the probability of coverage depend on the user's speed; a speed-dependent bias factor can then adjust the tier association to effectively improve the coverage, and hence system performance, in a fully-loaded network.

Autonomous smartphone-based WiFi positioning system by using access points localization and crowdsourcing

The survey of WiFi access points (APs) locations and their propagation parameters (PPs) is a time and labour consuming process, which makes WiFi positioning impractical. In this paper, a novel crowdsourcing method is introduced. The proposed method is used for automatic AP localization and PPs estimation by employing an inertial navigation solution, such as Trusted Portable Navigator (T-PN). The proposed system runs on smartphones, from which it builds and updates the database autonomously and adaptively to account for the dynamic environment. A WiFi positioning method, based on the generated database, is also discussed. The proposed system is validated by both simulated and experimental tests.

Self-mapping radio maps for location fingerprinting

Localisation of a mobile device in Wi-Fi based indoor environments has received much attention and is an important area of research, considering the uptake of devices with built-in Wi-Fi and often blanket coverage in enterprise. Most localisation techniques in this area require an off-line calibration phase where a radio map is manually built using the laborious efforts of administrators. This radio map is then subject to becoming inaccurate with time and requires refreshing as the equipment is upgraded and replaced. We propose a system that can automatically build and calibrate a radio map for use with location fingerprinting techniques to provide indoor and outdoor localisation with Wi-Fi. This system first uses a self-mapping technique that makes use of user roaming patterns to build a weighted undirected graph of Access Point locations. We then combine this initial map with a map built by users of smartphones with built-in Wi-Fi and GPS to generate anchor nodes. We show that the unique combination of these two techniques provides a zero-configuration calibration map for use with location fingerprinting techniques, which not only saves time and effort in the calibration phase, but provides a constantly fine-tuning and self-healing map.

Partially-Distributed Resource Allocation in Small-Cell Networks

We propose a four-stage hierarchical resource allocation scheme for the downlink of a large-scale small-cell network in the context of orthogonal frequency-division multiple access (OFDMA). Since interference limits the capabilities of such networks, resource allocation and interference management are crucial. However, obtaining the globally optimum resource allocation is exponentially complex and mathematically intractable. Here, we develop a partially decentralized algorithm to obtain an effective solution. The three major advantages of our work are as follows: 1) as opposed to a fixed resource allocation, we consider load demand at each access point (AP) when allocating spectrum; 2) to prevent overloaded APs, our scheme is dynamic in the sense that as the users move from one AP to the other, so do the allocated resources, if necessary, and such considerations generally result in huge computational complexity, which brings us to the third advantage: 3) we tackle complexity by introducing a hierarchical scheme comprising four phases: user association, load estimation, interference management via graph coloring, and scheduling. We provide mathematical analysis for the first three steps modeling the user and AP locations as Poisson point processes. Finally, we provide the results of numerical simulations to illustrate the efficacy of our scheme.

WLAN planning: Separate and joint optimization of both access point placement and channel assignment

In this paper, we are interested to the prob- lem of designing wireless local networks (WLANs), which involves deciding where to install the access points (APs), and assigning frequency channels to them with the aim to cover the service area and to guarantee enough capacity to users. Our proposal provides different solutions to the prob- lems related to the WLAN design and can be divided into two main parts. In the first part, we focus on the prob- lem of designing a WLAN by treating separately the AP positioning and the channel assignment problems. For AP positioning issue, we formulate it as a set covering problem. Since the computation complexity limits the exact solu- tion, we propose two heuristics to offer efficient solution. While, for the channel assignment, we define this issue as a minimum interference frequency assignment problem and propose three heuristics. Two heuristics aim to minimize the interference at AP locations. Whereas, the objective of the third heuristic is to minimize the interference at the TPs level. In the second part, we treat jointly the two aforementioned issues based on the concept of virtual forces. In this case, we start from an initial solution provided by the separated approach and try to enhance it by adjusting the APs’ positions and reassigning their operating frequen- cies. In each part of this work, we start first by describing the problem, then presenting the algorithms proposed to resolve it efficiently and finally we illustrate the obtained results and analyze them.

Assessing occupants’ energy load variation through existing wireless network infrastructure in commercial and educational buildings

Providing energy-consumption feedback has proven to be an effective approach for changing people's behavior and has led to significant energy-consumption reductions in residential buildings. However, providing feedback in commercial and educational buildings is challenging because of the difficulty in tracking occupants’ behaviors and their corresponding energy usage – especially for temporary occupants. To make providing such feedback possible in commercial and educational buildings, this paper presents the framework for a coupled system that uses residents’ wireless devices’ Wi-Fi connection and disconnection events to detect occupancy and then benchmarks energy loads against these events to monitor the energy use of occupants. A preliminary experiment implemented the proposed approach in a small-scale educational building to ascertain whether Wi-Fi network connection/disconnection events can be an effective indicator of energy load variation. The experiment's results confirmed the positive relationship between the Wi-Fi connection events and energy load increase; these results also indicated that the number of Wi-Fi connections cannot directly represent the magnitude of the energy load. A validation test was also conducted to assess the robustness of the coupled system in terms of the impact of users’ schedules (AM/PM), their length of stay (long-term/temporary), and the locations of access points.

Localization of Access Points Based on Signal Strength Measured by a Mobile User Node

The issue of locating access points (APs) in a building is investigated in detail in this paper. The signal strength of beacon frames from a selected AP is measured at multiple positions, and the location of the selected AP is estimated based on the distance information inferred from the measured signal strength and a modified version of the Hata-Okumara model. A strategy is proposed to select three measurement points to improve localization accuracy for a selected target AP when signal strength is measured at multiple positions. The accuracy of the proposed scheme is evaluated through experiment.

Test-Delivery Optimization in Manycore SOCs

We present two test-data delivery optimization algorithms for system-on-chip (SoC) designs with hundreds of cores, where a network-on-chip (NoC) is used as the interconnection fabric. We first present an effective algorithm based on a subset-sum formulation to solve the test-delivery problem in NOCs with arbitrary topology that use dedicated routing. We further propose an algorithm for the important class of NOCs with grid topology and XY routing. The proposed algorithm is the first to cooptimize the number of access points, access-point locations, pin distribution to access points, and assignment of cores to access points for optimal test resource utilization of such NOCs. Test-time minimization is modeled as an NoC partitioning problem and solved with dynamic programming in polynomial time. Both the proposed methods yield high-quality results and are scalable to large SOCs with many cores. We present results on synthetic grid topology NoC-based SOCs constructed using cores from the ITC'02 benchmark, and demonstrate the scalability of our approach for two SOCs of the future, one with nearly 1000 cores and the other with 1600 cores. Test scheduling under power constraints is also incorporated in the optimization framework.

Testing and Design of Indoor WLAN Using Artificial Intelligence Techniques

In this study, the performance of an indoor Wireless Local Area Network (WLAN) which is installed by using traditional methods and cellular design of a WLAN that is to be installed are tested. To implement the test and design, artificial intelligence methods are used. For the WLAN test, Artificial Neural Network (ANN) is used. Access Points (APs) and receiver coordinates are determined as ANN input parameters and optimum Receive Signal Strength (RSS) have been sought. For WLAN design, the Genetic Algorithm (GA) method is used. Wall structures and AP properties are taken into account to obtain optimum Receive Signal Strengths (RSSs). For the analytic solution of the optimum RSS, the indoor setting is divided into cellular areas. Thus, the most suitable locations for APs and the number of APs are determined. As a result, we have tested the performance of a WLAN, which is installed by means of traditional methods. According the experimental results, the indoor WLAN designed with the GA method has provided better performance. DOI: http://dx.doi.org/10.5755/j01.eee.20.6.7290