Space-partitioning with cascade-connected ANN structures for positioning in mobile communication systems

The world around us is getting more connected with each day passing by – new portable devices employing wireless connections to various networks wherever one might be. Locationaware computing has become an important bit of telecommunication services and industry. For this reason, the research efforts on new and improved localisation algorithms are constantly being performed. Thus far, the satellite positioning systems have achieved highest popularity and penetration regarding the global position estimation. In spite the numerous investigations aimed at enabling these systems to equally procure the position in both indoor and outdoor environments, this is still a task to be completed. This research work presented herein aimed at improving the state-of-the-art positioning techniques through the use of two highly popular mobile communication systems: WLAN and public land mobile networks. These systems already have widely deployed network structures (coverage) and a vast number of (inexpensive) mobile clients, so using them for additional, positioning purposes is rational and logical. First, the positioning in WLAN systems was analysed and elaborated. The indoor test-bed, used for verifying the models’ performances, covered almost 10,000m2 area. It has been chosen carefully so that the positioning could be thoroughly explored. The measurement campaigns performed therein covered the whole of test-bed environment and gave insight into location dependent parameters available in WLAN networks. Further analysis of the data lead to developing of positioning models based on ANNs. The best single ANN model obtained 9.26m average distance error and 7.75m median distance error. The novel positioning model structure, consisting of cascade-connected ANNs, improved those results to 8.14m and 4.57m, respectively. To adequately compare the proposed techniques with other, well-known research techniques, the environment positioning error parameter was introduced. This parameter enables to take the size of the test environment into account when comparing the accuracy of the indoor positioning techniques. Concerning the PLMN positioning, in-depth analysis of available system parameters and signalling protocols produced a positioning algorithm, capable of fusing the system received signal strength parameters received from multiple systems and multiple operators. Knowing that most of the areas are covered by signals from more than one network operator and even more than one system from one operator, it becomes easy to note the great practical value of this novel algorithm. On the other hand, an extensive drive-test measurement campaign, covering more than 600km in the central areas of Belgrade, was performed. Using this algorithm and applying the single ANN models to the recorded measurements, a 59m average distance error and 50m median distance error were obtained. Moreover, the positioning in indoor environment was verified and the degradation of performances, due to the crossenvironment model use, was reported: 105m average distance error and 101m median distance error. When applying the new, cascade-connected ANN structure model, distance errors were reduced to 26m and 2m, for the average and median distance errors, respectively. The obtained positioning accuracy was shown to be good enough for the implementation of a broad scope of location based services by using the existing and deployed, commonly available, infrastructure

RFID Localisation For Internet Of Things Smart Homes: A Survey

The Internet of Things (IoT) enables numerous business opportunities in fields as diverse as e-health, smart cities, smart homes, among many others. The IoT incorporates multiple long-range, short-range, and personal area wireless networks and technologies into the designs of IoT applications. Localisation in indoor positioning systems plays an important role in the IoT. Location Based IoT applications range from tracking objects and people in real-time, assets management, agriculture, assisted monitoring technologies for healthcare, and smart homes, to name a few. Radio Frequency based systems for indoor positioning such as Radio Frequency Identification (RFID) is a key enabler technology for the IoT due to its costeffective, high readability rates, automatic identification and, importantly, its energy efficiency characteristic. This paper reviews the state-of-the-art RFID technologies in IoT Smart Homes applications. It presents several comparable studies of RFID based projects in smart homes and discusses the applications, techniques, algorithms, and challenges of adopting RFID technologies in IoT smart home systems.Comment: 18 pages, 2 figures, 3 table

Positioning in Indoor Mobile Systems

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A Review of Hybrid Indoor Positioning Systems Employing WLAN Fingerprinting and Image Processing

Location-based services (LBS) are a significant permissive technology. One of the main components in indoor LBS is the indoor positioning system (IPS). IPS utilizes many existing technologies such as radio frequency, images, acoustic signals, as well as magnetic sensors, thermal sensors, optical sensors, and other sensors that are usually installed in a mobile device. The radio frequency technologies used in IPS are WLAN, Bluetooth, Zig Bee, RFID, frequency modulation, and ultra-wideband. This paper explores studies that have combined WLAN fingerprinting and image processing to build an IPS. The studies on combined WLAN fingerprinting and image processing techniques are divided based on the methods used. The first part explains the studies that have used WLAN fingerprinting to support image positioning. The second part examines works that have used image processing to support WLAN fingerprinting positioning. Then, image processing and WLAN fingerprinting are used in combination to build IPS in the third part. A new concept is proposed at the end for the future development of indoor positioning models based on WLAN fingerprinting and supported by image processing to solve the effect of people presence around users and the user orientation problem

RSS-based wireless LAN indoor localization and tracking using deep architectures

Wireless Local Area Network (WLAN) positioning is a challenging task indoors due to environmental constraints and the unpredictable behavior of signal propagation, even at a fixed location. The aim of this work is to develop deep learning-based approaches for indoor localization and tracking by utilizing Received Signal Strength (RSS). The study proposes Multi-Layer Perceptron (MLP), One and Two Dimensional Convolutional Neural Networks (1D CNN and 2D CNN), and Long Short Term Memory (LSTM) deep networks architectures for WLAN indoor positioning based on the data obtained by actual RSS measurements from an existing WLAN infrastructure in a mobile user scenario. The results, using different types of deep architectures including MLP, CNNs, and LSTMs with existing WLAN algorithms, are presented. The Root Mean Square Error (RMSE) is used as the assessment criterion. The proposed LSTM Model 2 achieved a dynamic positioning RMSE error of 1.73 m, which outperforms probabilistic WLAN algorithms such as Memoryless Positioning (RMSE: 10.35 m) and Nonparametric Information (NI) filter with variable acceleration (RMSE: 5.2 m) under the same experiment environment.ECSEL Joint Undertaking ; European Union's H2020 Framework Programme (H2020/2014-2020) Grant ; National Authority TUBITA

A Review of pedestrian indoor positioning systems for mass market applications

In the last decade, the interest in Indoor Location Based Services (ILBS) has increased stimulating the development of Indoor Positioning Systems (IPS). In particular, ILBS look for positioning systems that can be applied anywhere in the world for millions of users, that is, there is a need for developing IPS for mass market applications. Those systems must provide accurate position estimations with minimum infrastructure cost and easy scalability to different environments. This survey overviews the current state of the art of IPSs and classifies them in terms of the infrastructure and methodology employed. Finally, each group is reviewed analysing its advantages and disadvantages and its applicability to mass market applications