WiFi Based Fingerprinting Positioning Based on Seq2seq Model.

WiFi-based indoor positioning is widely exploited thanks to the existing WiFi infrastructure in buildings and built-in sensors in smartphones. The techniques for indoor positioning require the high-density training data to archive high accuracy with high computation complexity. In this paper, the approach for indoor positioning systems which is called the maximum convergence algorithm is proposed to find the accurate location by the strongest receiver signal in the small cluster and K nearest neighbours (KNN) of other clusters. Also, the K-mean clustering is deployed for each access point to reduce the computation complexity of the offline databases. Moreover, the pedestrian dead reckoning (PDR) method and Kalman filter with the information from the received signal strength (RSS) and inertial sensors are applied to the WiFi fingerprinting to increase the efficiency of the mobile object's position. The different experiments are performed to compare the proposed algorithm with the others using KNN and PDR. The recommended framework demonstrates significant proceed based on the results. The average precision of this system can be lower than 1.02 meters when testing in the laboratory environment with an area of 7x7 m using three access points.

WiFiTag is a simple and low cost solution for associating the real world to the online media such as web sites and YouTube. Our idea is to redirect a user's access based on the information of surrounding WiFi access points. Compared with WiFi-based indoor positioning system, our system requires no calculation and no much access points. This is because our system associates the digital content not with the location but with the AP itself. as the information of AP, we use ESSID, BSSID, and RSSI. by uploading a scanned WiFi information to the WiFiTag server, a terminal can get the proper URL as a response. Relationship between the WiFiTag and the URL have been defined ahead. WiFiTag realize "real to virtual" connection easily. for example, by associating lab's SSID to lab's HP, a visitor can access a lab's HP easily and directly, when the visitor comes to our lab. a camera required for QR code don't need for our system. in addition, a special card reader and touch action required for NFC are also unnecessary. If WiFi has already exist, no additional cost is necessary for our system. in this paper, we describe a overall concept of WiFiTag and show the availability and feasibility of our proposed system through some experimental evaluation results.

Indoor positioning system is a challenging problem due to the variety of environment and unreliable of data that are used for a prediction of the position. For Wi-Fi based indoor positioning system, signal intensity used to predict the coordinate of the device are known to fluctuate greatly despite being measured at the same position. Therefore, significant errors are often found when solving this problem with regression algorithms. A quantization of co-ordinate data into position IDs can mitigate the fluctuated noises in the data and is able to reformulate the problem into a multi-class classification problem. The error in positioning can then be computed from the distance between the true co-ordinate and the predicted co-ordinate. The experiment shows that Random forest classification can predict the position with the error in positing at 5.65 meters on average when the quantization is applied with threshold setting to 1 meter.

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.

Precise localization of Wi-Fi Access Point (AP) is becoming increasingly important with the rise of diverse location-based services based on smartphone. In this article, we propose a new method for precise Wi-Fi AP localization using GPS information of smartphone. The idea is that the possible area of Wi-Fi AP location, called AP_Area, is first determined by measuring GPS information and the Received Signal Strength (RSS) of smartphones from Wi-Fi AP. As the number of measurement points from users' smartphones increases, the AP_Area can be successively narrowed down to the actual AP location. Simulation results show that the proposed algorithm can detect the Wi-Fi AP localization within 5 m (probability over than 80%).

Optimizing AP and Beacon Placement in WiFi and BLE hybrid localization

As smartphones are increasingly popular, location-based services (LBSs) have become one of the crucial applications in daily lives. While outdoor localization is relatively easy leveraging GPS signals, localization in indoor environments is difficult due to the lack of GPS. Thus, due to the pervasive deployment of WiFi access points, there have been numerous studies on WiFi based indoor positioning. However, the multi-path fading of WiFi signals causes time-varying received signal strengths of WiFi signals, which leads to poor accuracy of WiFi localization. Moreover, WiFi scanning period, about 3∼4 seconds in general smartphone, may provide poor quality of services in the context of refreshment interval. Motivated by these limitations, we study the usability of tw o other sources that are currently available in smartphones: magnetic field strength and cellular signal strength, for indoor positioning purposes. Our preliminary measurements show that these two sources satisfy three properties needed for localization: time-in-variance, location representativeness and universality. Because these three sensors have their own characteristics, some problems in single sensory data based localization could be complementarily overcome. We will show how to combine the three smartphone sensory data for indoor positioning based on each module's characteristics, and how much accuracy is achieved by the hybrid localization.

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.

More and more applications of location-based services lead to the development of indoor positioning technology. Wi-Fi-based indoor localization has been attractive due to its extensive distribution and low cost properties. IEEE 802.11-2016 now includes a Wi-Fi Fine Time Measurement (FTM) protocol which provides a more robust approach for Wi-Fi ranging between the mobile terminal and Wi-Fi access point (AP). To improve the positioning accuracy, in this paper, we propose a robust dead reckoning algorithm combining the results of Wi-Fi FTM and multiple sensors (DRWMs). A real-time Wi-Fi ranging model is built which can effectively reduce the Wi-Fi ranging errors, and then a multisensor multi-pattern-based dead reckoning is presented. In addition, the Unscented Kalman filter (UKF) is applied to fuse the results of Wi-Fi ranging model and multiple sensors. The experiment results show that the proposed DRWMs algorithm can achieve accurate localization performance in line-of-sight/non-line-of-sight (LOS)/(NLOS) mixed indoor environment. Compared with the traditional Wi-Fi positioning method and the traditional dead reckoning method, the proposed algorithm is more stable and has better real-time performance for indoor positioning.

The paper answers a fundamental question: can the accuracy of Wi-Fi based localization be significantly increased by fusing information from alternate sources like LTE signals and magnetometers, collected through software defined radios and smart-devices? Further, it aims to eliminate dependency of well established localization techniques on only Wi-Fi access point (AP) positions, and instead, proposes a diversity-leveraging architecture called as the Wireless Locator (Wi-LO) framework. Wi-LO is a client-server paradigm for indoor localization that achieves precision by pattern-matching the collected signal samples with a priori references for each transmitter type, thus giving a stand-alone decision for these diverse sensing modes. The novelty of the paper is fusing these decision outcomes and resolving mis-matches (for e.g., Wi-Fi and LTE suggest different locations) in a seamless manner, and also identifying the best source to use at each location based on its spatial resolution. Wi-Lo is rigorously evaluated on a test-bed, with the proposed scheme of combining Wi-Fi, LTE and magnetometer performing better localization than the classical Wi-Fi-only approach in both urban (+8%) and rural (+25%) scenarios.

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.

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.

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.

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.