Hybrid indoor localization using GSM fingerprints, embedded sensors and a particle filter

Indoor localization using the magnetic fingerprint collected by the embedded sensors of smartphone has been under constant improvement with the widespread popularity of the smartphone. Most of the current systems rely on the Particle Filter (PF) or the Kalman Filter (KF) to combine the Pedestrian Dead Reckoning (PDR) with the magnetic or WiFi fingerprint for improving their accuracy. In this paper, a system using the PDR and the magnetic fingerprint is proposed for indoor localization, which is based on the combination of the Particle Filter (PF) and the Extend Kalman Filter (EKF). The system includes the Pedestrian Dead Reckoning (PDR) module and the magnetic fingerprint matching module. In particular, the hybrid indoor positioning algorithm which combines the Particle Filter (PF) and the Extend Kalman Filter (EKF) is proposed in the magnetic fingerprint matching module for the fusion of the results of the Pedestrian Dead Reckoning module and the magnetic fingerprint. This hybrid indoor positioning algorithm is the key component which can reduce the computation of the Particle Filter effectively and solve the inherent blindness and particles degeneration problem. The obtained results in the real scenarios show that our fusion system achieves better results than the widely adopted system in which the Particle Filter (PF) or the Kalman Filter (KF) is used. The evaluation shows the system achieves a localization accuracy about 1-2m on average in a large building.

Sleep modes are emerging as a promising technique for energy-efficient networking: by adequately putting to sleep and waking up network resources according to traffic demands, a proportionality between energy consumption and network utilization can be approached, with important reductions in energy consumption. Previous studies have investigated and evaluated sleep modes for wireless access networks, computing variable percentages of energy savings. In this paper we characterize the maximum energy saving that can be achieved in a cellular wireless access network under a given performance constraint. In particular, our approach allows the derivation of realistic estimates of the energy-optimal density of base stations corresponding to a given user density, under a fixed performance constraint. Our results allow different proposals to be measured against the maximum theoretically achievable improvement. We show, through numerical evaluation and simulation, the possible energy savings in today's networks, and we further demonstrate that even with the development of highly energy-efficient hardware, a holistic approach incorporating system level techniques is essential to achieving maximum energy efficiency.

Reliable indoor positioning is an important foundation for emerging indoor location based services. Most existing indoor positioning proposals rely on a single wireless technology, e.g., Wi-Fi, Bluetooth, or RFID. A hybrid positioning system combines such technologies and achieves better positioning accuracy by exploiting the different capabilities of the different technologies. In a hybrid system based on Wi-Fi and Bluetooth, the former works as the main infrastructure to enable fingerprint based positioning, while the latter (via hotspot devices) partitions the indoor space as well as a large Wi-Fi radio map. As a result, the Wi-Fi based online position estimation is improved in a divide-and-conquer manner. We study three aspects of such a hybrid indoor positioning system. First, to avoid large positioning errors caused by similar reference positions that are hard to distinguish, we design a deployment algorithm that identifies and separates such positions into different smaller radio maps by deploying Bluetooth hotspots at particular positions. Second, we design methods that improve the partition switching that occurs when a user leaves the detection range of a Bluetooth hotspot. Third, we propose three architectural options for placement of the computation workload. We evaluate all proposals using both simulation and walkthrough experiments in two indoor environments of different sizes. The results show that our proposals are effective and efficient in achieving very good indoor positioning performance.

User location and tracking information are increasingly used for contact tracing and social community detection. In-door positioning and indoor navigation systems are reaching good performances in several realistic scenarios. After an evaluation exclusively done through simulations, nowadays, these systems are trying to reach robust performances and good accuracy in heterogeneous environments. Problems are manifold as each environment presents a structure that strongly affects inertial sensors and radio signal propagation. Generally, systems showing the best performances rely on an extended knowledge of the indoor map. Moreover, they implement a model for pedestrian dynamics in terms of e.g step length, stride and the behaviour of the target users. Experimental results obtained during realistic indoor competitions, clearly show that performances drop when such systems are used in unseen scenarios in which an external user test the proposed solution. In fact, many parameters that are generally calibrated and set to maximize the performances might not work as expected. In this paper, we highlight which best practices should be applied for model calibration in smartphone-based indoor positioning systems. We describe a reference system based on a particle filter, and we show the most relevant parameters and the main factors that are generally in common with all similar systems in the literature. We also present the Run-Once tool for reaching optimal parameters, highlighting those best practices that should be applied to indoor positioning systems to maximize their performances and improve their robustness.

Indoor localization is a fundamental issue in IoT (Internet of Things). On the other hand, IoT provides a lot of networked devices that would help increase the precision of indoor localization. Particle Filter (PF) is widely used in indoor localization due to its flexibility that can adapt to different, and usually complex, indoor floorplans and furniture placements. In this work, we consider the fusion of multi-sensory data using PF. We focus on three types of popular sensors: IM (inertial measurement) sensor, RF (radio frequency) sensor, and environmental visual sensor. In particular, with environmental visual sensors, there is no extra device to be attached to localized targets. We propose a PF model that can adopt these types of sensory inputs. We show that in scenarios where visual sensory inputs are available, sub-meter precision can be achieved and in places with no visual coverage, seamless localization with reasonable precision can be supported by other sensors. Field trial results are presented, which show that our model is quite suitable for areas like lobby, corridor, and meeting room.

The indoor pedestrian positioning methods are affected by substantial bias and errors because of the use of cheap microelectromechanical systems (MEMS) devices (e.g., gyroscope and accelerometer) and the users’ movements. Moreover, because radio-frequency (RF) signal values are changed drastically due to multipath fading and obstruction, the performance of RF-based localization systems may deteriorate in practice. To deal with this problem, various indoor localization methods that integrate the positional information gained from received signal strength (RSS) fingerprinting scheme and the motion of the user inferred by dead reckoning (DR) approach via Bayes filters have been suggested to accomplish more accurate localization results indoors. Among the Bayes filters, while the particle filter (PF) can offer the most accurate positioning performance, it may require substantial computation time due to use of many samples (particles) for high positioning accuracy. This paper introduces a pedestrian localization scheme performed on a mobile phone that leverages the RSS fingerprint-based method, dead reckoning (DR), and improved PF called a double-stacked particle filter (DSPF) in indoor environments. As a key element of our system, the DSPF algorithm is employed to correct the position of the user by fusing noisy location data gained by the RSS fingerprinting and DR schemes. By estimating the position of the user through the proposal distribution and target distribution obtained from multiple measurements, the DSPF method can offer better localization results compared to the Kalman filtering-based methods, and it can achieve competitive localization accuracy compared with PF while offering higher computational efficiency than PF. Experimental results demonstrate that the DSPF algorithm can achieve accurate and reliable localization with higher efficiency in computational cost compared with PF in indoor environments.

Indoor localization can provide a number of different services such as location-aware advertisement, indoor navigation and automating different appliances based on the user location. A number of different techniques such as time-difference- of-arrival, angle-of-arrival, time-of-flight, and received signal strength indicator (RSSI) have been used to provide Location Based Services (LBS). RSSI is one of the widely used methods as it is cost efficient and easy to implement. However, RSSI's performance is limited by multipath fading and indoor noise. Particle Filter (PF) is an accurate Bayesian Filtering algorithm that can improve the performance of RSSI-based indoor localization. However, PF is not able to satisfy the high accuracy requirement (possibly 10cm) of indoor localization. In this paper, we present Particle Filter-Extended Kalman Filter (PFEKF) cascaded algorithm that combines PF and EKF in series to reduce the impact of multipath effects and noise on the RSSI. Our experimental results show that PFEKF improves the localization accuracy by 31.3% and 33.9% in 3D and 2D environments respectively when compared with using only a PF.

<h3>Abstract</h3> Filtering-based indoor positioning using ultra wideband (UWB) requires known velocity to predict prior position in the prediction stage. Velocity can be obtained from an inertial measurement unit (IMU) sensor or the posterior state vector at the previous time stamp. Both methods have limitations when using them in practice. This paper proposes two novel velocity determination approaches, which use measurements to approximate velocity in a self-contained way. They are integrated into particle filtering algorithms for prior position determination. The test result shows that the particle filter with the proposed approaches performs similarly to the Rao-Blackwellized particle filter and slightly better than the particle filter with IMU. Compared with the standard particle filter, the particle filters with our proposed approaches achieve similar positioning accuracies with less computation time. Moreover, it is found that the integration of Angle-of-Arrival measurements in particle-filter-based positioning improves the 3-D positioning accuracy by about 37.3% on average.

Indoor localization has gained significant attention recently due to the growing demand for location-aware applications within indoor environments. Among various techniques, Wi-Fi fingerprinting combined with the Internet of Things (IoT) has emerged as a promising solution for accurate and cost-effective indoor localization. This paper surveys the existing research related to indoor localization using Wi-Fi fingerprinting with the IoT, aiming to provide a comprehensive understanding of the advancements, challenges, and potential applications in this field. The paper begins by introducing the fundamental concepts of indoor localization and the role of Wi-Fi fingerprinting in achieving accurate position estimation. In addition, this paper focuses on the contributions of Artificial Intelligence (AI), Machine Learning (ML), and Deep Learning (DL) in improving localization accuracy, robustness, and scalability. The reviewed papers have been examined from various aspects, including system architecture, deployment strategies, fingerprint creation techniques, and localization algorithms, with a discussion about the advantages and limitations of each approach. In addition to discussing the state-of-the-art techniques, this paper identifies research gaps and open challenges in indoor localization using Wi-Fi fingerprinting with the IoT. The findings presented in this paper can guide future research efforts, leading to the development of intelligent and context-aware IoT applications within indoor environments.

With growing technology, Location Based Services (LBSs) are becoming a vital part of life. The rapid development of mobile internet has offered an opportunity for Wi-Fi indoor positioning to come under the spotlight due to its low cost. Here, indoor positioning is performed using Wi-Fi. The earlier solutions like GPS does not provide efficient positioning in indoor environment due to weak signal reception which makes Wi-Fi more reliable. However, nowadays the accuracy of the traditional indoor positioning algorithms like trilateration cannot meet the practical requirement. This paper proposes the performance analysis of indoor positioning algorithms using Wi-Fi technology based on Received Signal Strength (RSS) from the access points in the network. The positioning algorithms are based on location fingerprinting consisting of two stages: The offline database creation and the online positioning. The RSS values obtained by the mobile device at reference points are sent to the server where all the location fingerprints of all reference points are stored in a database. Coordinates of user position are calculated using positioning algorithms and the position is displayed in terms of 2Dcoordinates of the room and indoor map. Basic Fingerprint algorithm, RSS Fingerprint with minimum Euclidean Distance algorithm and RSS Fingerprint with Euclidean Distance and k-nearest neighbors' algorithm were implemented. Moreover comparison of their performance was carried out in terms of accuracy of position. From the result, it is found that an average accuracy of 0.4962 meters was achieved by RSS Fingerprint with minimum Euclidean distance algorithm.

Map information and sensor information is considered to be an effective way to assist indoor wireless location. Wireless location combined with map information and Sensor information can solve many problem such as positioning point cross the wall, falling into the wrong room. In this paper, a new indoor map matching algorithm was proposed based on particle filter. The indoor positioning system could be considered as non-linear system, so Non-parametric Monte Carlo (Monte Carlo) simulation method could achieve recursive Bayesian filtering, at the same time, the indoor GIS data, and sensor information, wireless location information was incorporated into the weight value of the particles to revise the observations constantly. Many experiments show that the indoor positioning accuracy is improved.

The localization of persons or objects usually refers to a position determined in a spatial reference system. Outdoors, this is usually accomplished with Global Navigation Satellite Systems (GNSS). However, the automatic positioning of people in GNSS-free environments, especially inside of buildings (indoors) poses a huge challenge. Indoors, satellite signals are attenuated, shielded or reflected by building components (e.g. walls or ceilings). For selected applications, the automatic indoor positioning is possible based on different technologies (e.g. WiFi, RFID, or UWB). However, a standard solution is still not available. Many indoor positioning systems are only suitable for specific applications or are deployed under certain conditions, e.g. additional infrastructures or sensor technologies. Smartphones, as popular cost-effective multi-sensor systems, is a promising indoor localization platform for the mass-market and is increasingly coming into focus. Today’s devices are equipped with a variety of sensors that can be used for indoor positioning. In this contribution, an approach to smartphone-based pedestrian indoor localization is presented. The novelty of this approach refers to a holistic, real-time pedestrian localization inside of buildings based on multi-sensor smartphones and easy-to-install local positioning systems. For this purpose, the barometric altitude is estimated in order to derive the floor on which the user is located. The 2D position is determined subsequently using the principle of pedestrian dead reckoning based on user's movements extracted from the smartphone sensors. In order to minimize the strong error accumulation in the localization caused by various sensor errors, additional information is integrated into the position estimation. The building model is used to identify permissible (e.g. rooms, passageways) and impermissible (e.g. walls) building areas for the pedestrian. Several technologies contributing to higher precision and robustness are also included. For the fusion of different linear and non-linear data, an advanced algorithm based on the Sequential Monte Carlo method is presented.

Regions without cellular network access, so-called dead spots, are problematic for Vehicle-to-Infrastructure (V2I) communication, which is usually based on such networks. To address this issue, we propose the Robust Context-Aware Message Flooding Protocol (ROCAMFLOOP) in this article. It combines cellular network access with ad hoc networks between nearby vehicles. This makes it possible to forward messages created in a dead spot to vehicles that are expected to regain cellular network access earlier than the message creators. A particular feature of the protocol is to limit the delivery of message copies only to those vehicles predicted to leave the dead spot first. In this way, the number of copies effectively delivered is kept low. ROCAMFLOOP was equipped with special protocol functions that guarantee robust execution, even when the behavior of the drivers deviates from expectations. Our protocol is a novel way to combine “Store-Carry-Forward”-type message delivery with a spatiotemporal-based selection of vehicles for delivery.

Indoor Positioning System (IPS) has always been a focused area of research due to an increasing demand for IPS in a variety of civilian and defence applications. Bluetooth low energy beacons are best suited for IPS owing to their compatibility with mobile devices such as smart phones. In this paper, a standard bluetooth module is configured to work as a Bluetooth-Beacon (BT-Beacons) and an IPS is implemented using the RSSI values of signals from the BT-Beacons. The position of user in indoors is computed using the principle of trilateration. The proposed system achieved good positioning accuracy and is economical. Further, the proposed system can be improved to assist localization and navigation in indoor environment such as huge buildings.

In today’s era finding the location and navigation of an object is essential for many reasons. The primary reason is locating the object, and navigation helps systems to make decisions faster. Secondary reason is we may detect fault and posses faster processing and operation. Another side of development and research is for cost-effective techniques involving indoor and outdoor systems. Many researchers are working on indoor positioning and developing lightweighted solutions using radio frequencies. Managing and monitoring radio frequencies in an indoor system have physical barriers; they also have some technological limitations where we can provide solutions. The solutions for indoor localization and tracking are GPS signal, Bluetooth, infrared, RFID, and wireless LAN. All these radio frequencies deal with a crucial issue of gaining RSS (Received Signal Strength). In this work, we are focusing on RFID-based indoor environment where RSS is our main attention, we also target on gaining efficiency and accuracy, stability, robustness. The proposed algorithm of this work is mainly targeting with directional antenna by which we can overcome the physical and other barriers of radio frequency. These kinds of indoor environments such as health care, loyalty management system, automatic parking allotment system, tracking services for older people, or customers inside living communities, mobile robot, logistics system, etc., can be facilitated using directional antenna and RFID tagging for navigation indoor positioning.