Mitigating anomalous measurements for indoor wireless local area network positioning

Indoor positioning methods based on wireless local area network (WLAN) signal measurements have gained popularity because of high localisation accuracy. These methods use radio‐maps obtained from wireless signal measurement surveys on location grids. Measurement sets from various WLAN access points are called fingerprints and they can be used to identify locations where the measurements are collected. WLAN positioning methods face unexpected changes in signal patterns because of attenuation changes or transient faults in WLAN cards or access points that often make signal strength readings unavailable. This study studies the effect of faulty measurements on the performance of popular state‐of‐the‐art WLAN indoor positioning methods. Additionally, an integrity monitoring preprocessing algorithm is provided that demonstrates a possibility of faulty measurements mitigation for conventional methods such as K‐ nearest‐neighbour. This is achieved by detecting and excluding faulty measurements prior to classification. Performance figures are provided for both simulated and empirical environments.

The global positioning system (GPS) is a popular choice for accurate location sensing and is often used in navigation systems. However, GPS fails to operate inside buildings because satellite signals are blocked. Thus, GPS cannot be used for localization indoors. Moreover, it is difficult to distinguish between the different floors. Research to overcome this problem using the ultrawideband (UWB), radio frequency identification (RFID), and infrared ray (IR) has been conducted. These methods, however, have drawbacks, such as the need for an additional communication module and database. Wireless local area network (WLAN) access point (AP) is widely installed at various locations, and using these WLAN APs can be a viable alternative. The proposed system in this paper indicates the location information in the service set identifier (SSID) of virtual AP. Therefore, merely by scanning the WLAN signals, mobile users can detect their location indoors.

This paper analyzes the interference effect of WLAN(Wireless Local Area Network) service on WiBro(Wireless Broadband) service in adjacent frequency band by using Monte Carlo method. For the analysis, we make scenario that WLAN service operates on frequency band of 2401 MHz ~ 2423 MHz and WiBro service operates on frequency band of 698 MHz ~ 704 MHz. In case of WiBro receiver is close to WLAN AP(Access Point), the interference will be increased. So, we calculate the protection distance between WiBro receiver and WLAN AP to meet interference probability of 5 % below and required interference power of WLAN AP to meet interference probability of 5 % below in the case of fixed protection distance. As a result, the protection distance of 60 m and -9.5 dBm of required interference power of WLAN AP at 10 m of the fixed protection distance are analyzed.

In recent years, there has been a growing demand in 3G data services, leading to deteriorating 3G service quality. Noting that Wireless Local Area Networks (WLANs) as well as 3G cellular networks are widely available today, WLANs could be effectively utilized to relieve the overload in the 3G networks. On the other hand, use of IEEE 802.11 WLAN Access Points (APs) has proliferated tremendously, resulting in a communication device inside a mobile vehicle to access the Internet. However, using Internet through APs in moving vehicles is challenging since WLAN APs have a short range and are typically not deployed to cover all roads. Several studies have investigated the performance of using intermittently available WLAN connectivity from moving vehicles for data transfers and predictive offloading in WLAN/3G networks. However, these works have not addressed mobility pattern from the viewpoint that drivers' mobility is generally known to have a daily routine. Therefore, in this paper, we consider the user's historical mobility to decide to offload data to WLAN instead of switching to 3G network. The user's application usage pattern is also considered into predicting available WLANs. To evaluate the performance of our offloading algorithm, we analyze the prediction error and conduct simulations. The simulation results show that the proposed algorithm achieves shorter transmission time than the existing schemes that do not consider user's mobility pattern by delivering more data to the WLANs.

Wireless local area networks (WLANs) for public access need to provide privacy between users and also ensure correct charging where access is based on payment. However, by design, conventional LANs and WLANs provide directs unmediated communications between networked devices at the link layer. The paper presents a method to allow networked client devices to communicate with a network comprising routers and other servers while preventing unmediated communication between the clients on the wireless network. An advantage of the method is that it can be implemented entirely within the WLAN access point as a configurable forwarding policy database. No changes are needed to the WLAN client software or the routers and servers and the communication remains completely within the scope of existing industry standards.

We discuss the derivation of an empirical model for spatial registration patterns of mobile users in a campus wireless local area network (WLAN). Such a model can be very useful in a variety of simulation studies of the performance of mobile wireless systems, such as that of resource management and mobility management protocols. We base the model on extensive experimental data from a campus WiFi LAN installation. We divide the empirical data available to us into training and test data sets, develop the model based on the training set, and evaluate it against the test set. The model shows that user registration patterns exhibit a distinct hierarchy, and that WLAN access points (APs) can be clustered based on registration patterns. Cluster size distributions are highly skewed, as are intra-cluster transition probabilities and trace lengths, which can all be modeled well by the heavy-tailed Weibull distribution. The fraction of popular APs in a cluster, as a function of cluster size, can be modeled by exponential distributions. There is general similarity across hierarchies, in that inter-cluster registration patterns tend to have the same characteristics and distributions as intra-cluster patterns. In this context, we also introduce and discuss the modeling of the disconnected state as an integral part of real traffic characteristics. We generate synthetic traffic traces based on the model we derive. We then compare these traces against the real traces from the test set using a set of metrics we define. We find that the synthetic traces agree very well with the test set in terms of the metrics. We compare the derived model to a simple modified random waypoint model, and show that the latter is not at all representative of the real data. We also show how the model parameters can be varied to allow designers to consider 'what-if' scenarios easily. Finally we develop an extended version of Model T that uses an alternative modeling of relative popularity of APs and clusters, with certain generalization advantages, and evaluate its fidelity to the real data also, with positive results.

The IEEE 802.11 Wireless Local Area Network (WLAN) is a popular internet access technology, and researchers are continuously working on the quality of service (QoS) improvement by proposing new and efficient schemes. Performance analysis and benchmarking are an integral part of WLAN research. In most of the cases, this is done through computer simulation using popular network simulators such as Network Simulator - 2 (NS-2) or OPNET. The computer simulation of a dynamic system such as a communication network heavily depends upon a set of stochastic models for capturing the real-time behaviors of the system. The choice of a set of stochastic models that represent the most practical scenario is often debated and there is no universal consensus on the choice of these models. This is why, for proper assessment of the merits of a proposal, and any associated commercialization activities, a validation on a hardware platform is highly recommended. Researchers have developed few Field Programmable Gate Array (FPGA) and System-on-Chip (SoC) based hardware, but most of these development platforms are expensive and/or have limited support for networking layers software. This letter proposes an Atheros SoC based hardware platform, and an open-source Linux software stack, called OpenWrt, for WLAN research. The proposed hardware is available off-the-shelf as a WLAN access point (AP), and is a cost-effective way of generating results on a hardware system. A new MAC protocol is implemented on one such WLAN access point to prove the platform.

Wireless communication has experienced enormous growth during the past decade, primarily driven by mobile cellular systems for voice communications, but more recently by the use of wireless local area networks(WLANs) for corporate enterprises and hot spot areas. However, mobile operators will face many challenges both economically and technically, as WLAN evolve through a bewildering number of different standards during the coming years. Potentially this will require several upgrades to the WLAN infrastructure. A new approach for addressing these challenges is evaluated in this paper. It is based on a radio over fiber (RoF) technology that uses electro-absorption modulator (EAM) and off-the-shelf WLAN access point, and it promises to provide solutions that will be transparent to changes in protocols and frequency of operation as well as reducing radio access infrastructure costs

Indoor localization based on Received Signal Strengths (RSS) or on some form of power measurements is a low-cost and low-complexity solution gaining more and more interest in the research and commercial worlds. Typically, Wireless Local Area Network (WLAN) signals are employed for such purpose, due to the fact that they are widely spread in indoor environments. Nevertheless, any wireless signal available in indoor scenarios can be used for positioning based on similar power measurement approaches. One example is the radio frequency identification (RFID), which enable portable localization systems in the form of wearable REID tags. Such RFID-enabled systems are highly demanded for health-state monitoring, object tracking, and security. These applications are mostly indoor applications, and thus, we can envision a near future where multiple RFID and WLAN signals will co-exist on multiple frequency bands. The existing signal diversity can offer a benefit in indoor positioning, providing that the signal propagation effects for both WLAN and RFID are well understood and taken into account. However, measurement-based studies on indoor channel modeling, including path loss and shadowing effects of RFID signals are still missing. A comparison between RFII) and WLAN channel models for positioning purpose has yet to be made. It is the purpose of our paper to address the path-loss channel models and shadowing effects for WLAN and RFID signals based on extensive measurement campaigns in an office environment.

The ability to estimate the location of wireless device users has led to many applications that use location information to provide users and service providers with commercially valuable real time information. In this paper, we propose a low-complexity method for location estimation in an indoor wireless local area network (WLAN) deployment. The proposed method relies on measurements of signal strength (SS) of multiple access-points (APs) in a WLAN and is based on a piecewise-linear model that represents distances from APs in terms of SS measurements. The proposed scheme has significantly lower computational and storage requirements as compared to conventional location estimation methods.

In this paper, we introduce two indoor Wireless Local Area Network (WLAN) positioning methods using augmented sparse recovery algorithms. These schemes render a sparse user's position vector, and in parallel, minimize the distance between the online measurement and radio map. The overall localization scheme for both methods consists of three steps: 1) coarse localization, obtained from comparing the online measurements with clustered radio map. A novel graph-based method is proposed to cluster the offline fingerprints. In the online phase, a Region Of Interest (ROI) is selected within which we search for the user's location; 2) Access Point (AP) selection; and 3) fine localization through the novel sparse recovery algorithms. Since the online measurements are subject to inordinate measurement readings, called outliers, the sparse recovery methods are modified in order to jointly estimate the outliers and user's position vector. The outlier detection procedure identifies the APs whose readings are either not available or erroneous. The proposed localization methods have been tested with Received Signal Strength (RSS) measurements in a typical office environment and the results show that they can localize the user with significantly high accuracy and resolution which is superior to the results from competing WLAN fingerprinting localization methods.

This paper introduces novel schemes for indoor localization, outlier detection, and radio map interpolation using wireless local area networks. The localization method consists of a novel multicomponent optimization technique that minimizes the squared $\ell _{2}$ -norm of the residuals between the radio map and the online received signal strength (RSS) measurements, the $\ell _{1}$ -norm of the user's location vector, and weighted $\ell _{2}$ -norms of layered groups of reference points (RPs). RPs are grouped using a new criterion based on the similarity between the so-called access point (AP) coverage vectors. In addition, since AP readings are prone to containing inordinate readings, called outliers, an augmented optimization problem is proposed to detect the outliers and localize the user with cleaned online measurements. Moreover, a novel scheme to record fingerprints from a smaller number of RPs and estimate the radio map at RPs without recorded fingerprints is developed using sparse recovery techniques. All localization schemes are tested on RSS fingerprints collected from a real environment. The overall scheme has comparable complexity with competing approaches, while it performs with high accuracy under a small number of APs and finer granularity of RPs.

This paper gives an explanatorily idea of connecting mobile phones in a same WIFI system using WLAN(Wireless Local Area Network) Access Point(AP) or P2P(Peer-to-Peer) connection system. By using this IP based local connection system, mobile phone users can share files, photos, send messages and even call other mobile device free of cost. The system will also have a feature to automatically detect if other user is is not in the specified WIFI range and opts the user to connect via standard GSM/CDMA/3G service providers.

The idea of smart and sustainable cities is incomplete without using information and communication technologies. Wireless Local Area Network (WLAN) plays a vital role in connecting different services, transferring data and providing indoor localization. WLAN fingerprinting technique has become the most common approach for indoor localization in smart cities. This technique utilizes received signal strength (RSS) and channel status from the installed WLAN Access Points (AP). Currently, WLAN planning relies on the site survey, which is laborious and uneconomical for large areas. The WLAN has a limited number of channels assigned in the frequency reuse strategy. An inefficient channel assignment strategy will lead towards increased interference, thus degrading the overall service. This paper proposes a WLAN channel assignment strategy for large and crowded indoor areas. The proposed scheme assigns the channels to installed APs for indoor localization in fingerprinting framework. The proposed technique ensures that the proposed AP channel assignment algorithm in our network-planning scheme mitigates interference.

Wireless local area network (WLAN) has changed the paradigm of communication and become ubiquitous. As the number of devices connected to wireless is increasing, it is therefore, essential to find vulnerabilities in the security of wireless networks. In this research WLAN security vulnerabilities are exposed in real time. WLAN security techniques (MAC filtering, Hidden SSID and WPA2) available to residential users were practically exploited. Tenda WiFi wireless access point was set up with MAC filtering, hidden SSID and WPA2 security mechanisms in different combinations in such a way as to add an additional layer of security to previous configuration. Kali Linux version 2017.1 was used as a platform to exploit WLAN networks in real time. In this research, the sensitive information broadcasted by WLAN was utilized to carry out attacks on wireless networks. A practical demonstration of exposing vulnerabilities in MAC filtering, Hidden SSID with MAC filtering and WPA2-PSK with hidden SSID and MAC filtering security mechanisms of AP was carried out in real time. It was observed that the existing security mechanisms were vulnerable. Researchers have exploited many security mechanisms of WLAN focusing upon a single parameter of WLAN security at a time. However, in this work, the three available security mechanisms were cascaded to produce a three layer security mechanism i.e. WPA2 with hidden SSID and MAC filtering. This cascaded model was exploited in real time.