Localization of Wi-Fi Access Point using smartphone's GPS information

As a result of the drastic increase in the number of mobile device users, there is considerable public debate about possible health hazards especially due to base stations and Wi-Fi access points. For this reason, in this study the effects of the number of users in a base station and wireless fidelity (Wi-Fi) access point on electric field strength (E) levels were investigated using real-time measurements. Two-stage E measurements were performed on Ondokuz Mayıs University's (OMU) Kurupelit Campus with a PMM-8053 and SRM-3006 electromagnetic field (EMF) meter. In the first stage, 24-h measurements were performed with PMM-8053 at the location where the maximum E was measured and from which the busiest times of the day were then determined. The relationship among band selective E values was assessed using the number of users per minute provided by three cellular system operators for the location. Upper and lower bounds of E according to the number of users were calculated, and then an empirical model that helps calculate the E of medium with 86% accuracy was proposed. In the second stage, the effect of the number of users in a Wi-Fi access point on E level was investigated. For this purpose, the measurement environment was set as including one Wi-Fi access point with needed measurement devices. The number of users was increased as per user per minute. The number of users was validated with the data obtained from OMU IT Department. Using the measurement result, a model with a 96% accuracy between the E value in the environment and the number of users accessing the Wi-Fi system is proposed. With the use of these models, E level in the medium can be determined without using any EMF meters, thus precautions can be taken to stay within regulatory limits.

In indoor positioning system, the user’s location is estimated based on user fingerprints database collected. The received signal from a user at a certain location is compared with all database entries and the best match is selected as the correct estimation. For a given number of access points (APs), optimizing the locations of APs can significantly improve the performance of user location estimation. In this work, the probability of correct estimation is analyzed based on APs location. Both analytical expressions and numerical results are presented and discussed. It has been shown that the probability of correct estimation is highly affected by the APs locations. Furthermore, a new technique is proposed to remarkably reduce the search-cost when comparing a received user signal to the data-base. The performance of the system is analyzed considering different APs locations, different user locations, and different noise variances. It is shown that user’s location estimation is significantly improved when compared to existing techniques.

Wi-Fi networks are becoming more and more ubiquitous and represent a substantial source of energy consumption around the globe, mainly when it comes to Access Points (APs). There has been some work done on the characterization of the power consumption of Wi-Fi APs and network interface cards (NICs), and the power usage of these devices under different configurations and standards but mostly using legacy standards. A detailed AP power consumption analysis, exploring the whole set of degrees of freedom and capabilities of these devices is lacking in the state of the art. In this paper, we present a thorough power consumption analysis, covering the configuration options available in enterprise Wi-Fi APs from the three major vendors on the market. The goal is to understand how the power consumption of an AP varies with the different configurations, and provide insights on the parameters that significantly affect the AP power consumption. The obtained experimental results confirm previous state-of-the-art conclusions but contradict some of the studies and results found in the literature, while updating results and conclusions taken in the past to the most recent standards, configurations, and data rates available today. The analysis provided herein is a valuable source of information for deriving new AP power consumption models and designing energy-efficient Wi-Fi networks.

Wireless fidelity (WiFi) networks are deployed in several varied environments all around the World. Usually, the wireless fidelity access points are always on in houses and other small companies. In buildings of large companies and public organizations and in university campuses the number of access points is elevated; they are powered using power over the ethernet and are always on. Consequently, they consume a considerable amount of electric energy. The last versions of the International Electric and Electronic Engineers 802.11 standardized procedures to save energy in a wireless fidelity terminal but not in the access point. We designed a formal method to show when energy can be saved in wireless fidelity access points considering different power supplies for the access point: an electric energy battery and a standard voltage supply. We use an external battery that stores electric energy during an interval of time from a standard voltage supply (Charge period). After that interval (Discharge period), the energy supply for the access point is the external battery. Those intervals of time are repeated sequentially (Charge and Discharge cycles). We verified our formal model implementing a hardware circuit that controls the power supply for the access point. The amount of energy saving for a large number of of access points during a long period of time is considerably high.

With the advantage of high bandwidth and low cost, WLAN is widely regarded as an appealing technology for wireless access. However, WLAN only provides very limited coverage which is no more than hundreds of meters, making users experience intermittent connectivity when they move near Wi-Fi access points (AP). To fill in the gaps between APs, we present a new framework, which combines infrastructure composed of APs with the concept of DTN to improve network performance. In particular, when a mobile client has moved out of the reach of the infrastructure, our framework predicts the trajectory of this client and lets the APs in the proximity identify suitable relay nodes among the mobile clients that happen to visit those APs. If some passing by mobile clients have a chance to encounter the target client soon, the corresponding AP(s) will transfer the data destined to the target client to those mobile clients, in the hope that they will accomplish the delivery in a store-carry-forward fashion. In this way, the target client may be able to receive data even before it arrives at an AP. Our evaluation shows that our framework fairly improves the performance in terms of both delivery delay and delivery ratio, with controlled overhead.

In this work, we investigate the problem of channel and rate allocation for LTE-Unlicensed (LTE-U) to efficiently coexist with WiFi access points (APs). Specifically, we formulate an auction mechanism where LTE-U first announces an aggregation number (number of WiFi channels that LTE-U wishes to aggregate) and a reserve rate (maximum rate that LTE-U is willing to allocate to an AP), following which the APs decide their mode of operation—cooperation or competition. In cooperation mode, an AP allows LTE-U to exclusively occupy its channel (in return for a rate) while in the competition mode both LTE-U and AP simultaneously contend for channel access. We characterize the solution to the auction problem in terms of Symmetric Bayesian Nash Equilibrium (SBNE) and prove results illustrating its structure. We then optimize the auction mechanism by evaluating the optimal aggregation number and reserve rate that maximizes the total rate achieved by LTE-U subject to a constraint on APs’ rates. Finally, through simulation experiments we demonstrate the efficacy of our algorithm in comparison with (1) the strategy of aggregating a fixed number of channels and (2) a heuristic algorithm where a random number of channels are aggregated.

With the fast-growing demand for mobile services, where to place the access points (APs) for providing uniformly and appropriately distributed signals in a wireless local area network (WLAN) becomes an important issue in the wireless network planning. Basically, AP placement will affect the coverage and strength of signals in a WLAN. The number and locations of APs in a WLAN are often decided on the basis of the trial-and-error method. Based on this method, the network planner first selects suitable locations to place APs through observation, and then keeps changing the locations to improve the signal strength based on the received signal. Such process is complicated, laborious and time-consuming. To overcome this problem, we investigate the back-propagation neural network (BPNN) algorithm to improve over the traditional trial-and-error method. Without increasing the number of APs, our approach only needs to adjust AP locations to overcome weak signal problems and thus increase the signal coverage for the Internet connection anywhere within the area. In our experiment, we established a WLAN on a C campus. Our experiments also indicate that placing APs according to the BPNN provided better signal coverage and met the students’ demands for connecting to the Internet from anywhere in the classroom.

In this paper, we present Wi-Cache, an SDN (Software Defined Networking) based distributed content caching system in a wireless local area network (WLAN). One way to effectively reduce congestion in the backhaul links of today's IP network is caching videos and non-voice traffic closer to mobile and wireless devices. Cellular base stations and WiFi access points (APs) are the major serving points and the closest network nodes to mobile and wireless devices. WiFi APs are becoming a part of heterogeneous cellular networks (HCNs), and are also present abundantly at homes and workplaces. Therefore, one viable solution to reduce the bandwidth consumption of backhaul links is caching contents in these WiFi APs so that user requests can be served locally. Wi-Cache caches contents on low-cost wireless APs, and delivers them to mobile clients when they are requested. It minimizes the need to fetch contents from remote content servers. It also addresses two major WLAN challenges during content delivery-client mobility and load balancing on WiFi APs. Wi-Cache prototype implementation, and the evaluation of its feasibility and performance is based on Odin, an open source SDN framework for enterprise WLANs.

This paper focuses on how to optimize the deployment location of the anchor Wi-Fi access points (APs) in order to enhance the indoor positioning performance of the finetime measurement (FTM) protocol. Compared to conventional Wi-Fi positioning, FTM protocol is able to achieve relatively good indoor positioning performance since it estimates distance between a sender and its receiver with the flight duration of Wi-Fi signals. However, the FTM-based positioning accuracy in NLoS environments is still not high, which restricts its application in indoor scenarios. To this end, we formulate an optimization problem to maximize the total number of LoS paths by optimizing the locations of the anchor APs. To solve the problem, we propose a Deep Q-Network (DQN)-based AP deployment optimization method (DQ-WiAPDoM), which utilizes DQN to optimize the locations of APs such that the total number of LoS paths over all covered area is maximized. Experimental results show that DQ-WiAPDoM converges well and is able to reduce the average positioning error efficiently.

In traditional way of meal ordering process at a restaurant, firstly customers browse through the restaurant menu and then they wait for a waiter to come and take their orders. This procedure can be slow and decrease customer satisfaction during busy hours. Moreover, it is prone to human made mistakes. Automated systems can be used to optimize procedures by means of minimizing human made mistakes, decreasing waste of paper and thus making ordering procedures more time and cost efficient. There have been numerous attempts to automate ordering process in restaurants in recent years. For example, having a tablet PC on each restaurant table is one of the most popular solutions in the market while it is not cost effective since it requires many tablets. In this paper, we have designed and implemented an automation solution which benefits from WiFi’s captive portal feature on mobile device of restaurant customers. In the proposed system, customers just need to connect restaurant’s open WiFi from their table without waiting for a waiter to come and take their orders. Once their connection succeeds, customers will be directly forwarded to the web page on cloud server where they can browse through a menu and submit their orders without installing any application or any change in their mobile device. Each customer is identified by their WiFi card’s MAC address which is reported to cloud server by lightweight software that runs on WiFi access point (AP) together with their received signal strength (RSSI) level and restaurant identification code. System is designed to scale easily to multiple restaurants placing configured WiFi AP on each restaurant using same cloud server. Our proposed system provides an easy to use solution for restaurant customers and a cost effective solution for restaurant owners as they can utilize their human resources more efficiently.

Simultaneous localization and mapping (SLAM) has been extensively researched in past years particularly with regard to range-based or visual-based sensors. Instead of deploying dedicated devices that use visual features, it is more pragmatic to exploit the radio features to achieve this task, due to their ubiquitous nature and the widespread deployment of the Wi-Fi wireless network. This article presents a novel approach for collaborative simultaneous localization and radio fingerprint mapping (C-SLAM-RF) in large unknown indoor environments. The proposed system uses received signal strengths (RSS) from Wi-Fi access points (APs) in the existing infrastructure and pedestrian dead reckoning (PDR) from a smartphone, without a prior knowledge about map or distribution of AP in the environment. We claim a loop closure based on the similarity of the two radio fingerprints. To further improve the performance, we incorporate the turning motion and assign a small uncertainty value to a loop closure if a matched turning is identified. The experiment was done in an area of 130 m by 70 m and the results show that our proposed system is capable of estimating the tracks of four users with an accuracy of 0.6 m with Tango-based PDR and 4.76 m with a step counter-based PDR.

It is usually possible to associate with more than one Access Point (AP) in IEEE 802.11 Wireless LANs. AP selection is a crucial issue because the performance achieved by a user heavily depends on the AP selected. A received signal strength (RSS)-based association mechanism is specified by the IEEE 802.11 standard. However, this does not consider the channel conditions and AP load, which leads to a low throughput and a low user transmission rate. An alternative to using the RSS is to exploit the airtime metric, which provides users with information on how busy the channel is, but the results of our simulations show that the airtime metric cannot indicate the exact status of the channel. In this paper, we present a new association framework using channel occupancy in order to provide users with exact channel status information of APs. Via the proposed scheme, users can compare the achievable throughput provided by each AP and select the best AP. We validate our proposed association method via simulation results, which show that the throughput improvement of the method is worthy of notice.

In this paper, we study the physical-layer security in a 3-D multiuser visible light communication (VLC) network. The locations of access points (APs) and mobile users are modeled as two 2-D, independent and homogeneous Poisson point processes at distinct heights. Using mathematical tools from stochastic geometry, we provide a new analytical framework to characterize the secrecy performance in multiuser VLC networks. Closed-form results for the outage probability and the ergodic secrecy rate are derived for networks without AP cooperation. Considering the cooperation among APs, we give tight lower and upper bounds on the secrecy outage probability and the ergodic secrecy rate. To further enhance the secrecy performance at the legitimate user, a disk-shaped secrecy protected zone is implemented in the vicinity of the transmit AP. Based on the obtained results, it is shown that cooperating neighboring APs in a multiuser VLC network can bring performance gains on the secrecy rate, but only to a limited extent. We also show that building an eavesdropper-free protected zone around the AP significantly improves the secrecy performance of legitimate users, which appears to be a promising solution for the design of multiuser VLC networks with high security requirements.

How many small cell (SC) access points (APs) are required to guarantee a chosen quality of service in a heterogeneous network? In this chapter, we answer this question considering two different network models. The first is the downlink of a finite-area SC network where the locations of APs within the chosen area are uniformly distributed. A key step in obtaining the closed-form expressions is to generalize the well-accepted moment matching approximation for the linear combination of lognormal random variables. For the second model, we focus on a two-layer downlink heterogeneous network with frequency reuse-1 hexagonal macro cells (MCs), and SC APs that are placed at locations that do not meet a chosen quality of service from macro base stations (BSs). An important property of this model is that the SC AP locations are coupled with the MC coverage. Here, simple bounds for the average total interference within an MC makes the formulation possible for the percentage of MC area in outage, as well as the required average number of SCs (per MC) to overcome outage, assuming isolated SCs. Introduction Heterogeneous cellular networks (HCNs) are being considered as an efficient way to improve system capacity as well as effectively enhance network coverage [1, 2]. Comprising multiple layers of access points (APs), HCNs encompass a conventional macro cellular network (first layer) overlaid with a diverse set of small cells (SCs) (higher layers). Cell deployment is an important problem in heterogeneous networks, both in terms of the number and positioning of the SCs. Traditional network models are either impractically simple (such as the Wyner model [3]) or excessively complex (e.g., the general case of random user locations in a hexagonal lattice network [4]) to accurately model SC networks. A useful mathematical model that accounts for the randomness in SC locations and irregularity in the cells uses spatial point processes, such as Poisson point process (PPP), to model the location of SCs in the network [5–10]. The independent placement of SCs from the MC layer, has the advantage of analytical tractability and leads to many useful SINR and/or rate expressions. However, even assuming that wireless providers would deploy SCs to support mobile broadband services, the dominant assumption remains that SCs are deployed randomly and independent of the MC layer [11].

In recent years, indoor fingerprint-based localization algorithm has been widely used by applications on smart phone. In these localization algorithms, it’s very popular to use WiFi signal characteristics to represent the location fingerprint. With the fast popularization of WiFi, the WiFi access points (APs) could be seen everywhere. However, as the number of APs increases, the dimension of the fingerprint and the complexity of fingerprint-based localization algorithm subsequently increase. Responding to the above challenges, this paper proposes a novel indoor localization algorithm MAPS (indoor localization algorithm based on multiple access point selection). MAPS could effectively reduce the complexity of localization computation, and improve the performance of localization through AP selection method. With the first round AP selection, MAPS can obtain a stable subset of AP, thus reducing the dimension of fingerprint, and obtaining better discrimination. And with the second round of AP selection, AP subset could be further condensed to construct a decision tree in each location cluster. This step can further improve the localization performance. The experimental results shown, as compared with classical indoor localization algorithm, MAPS has better positioning accuracy, and could achieve the accuracy of over 90% within 2m location error.