Fingerprinting techniques, which are a common method for indoor localization, have been recently applied with success into outdoor settings. Particularly, the communication signals of Low Power Wide Area Networks (LPWAN) such as Sigfox, have been used for localization. In this rather recent field of study, not many publicly available datasets, which would facilitate the consistent comparison of different positioning systems, exist so far. In the current study, a published dataset of RSSI measurements on a Sigfox network deployed in Antwerp, Belgium is used to analyse the appropriate selection of preprocessing steps and to tune the hyperparameters of a kNN fingerprinting method. Initially, the tuning of hyperparameter k for a variety of distance metrics, and the selection of efficient data transformation schemes, proposed by relevant works, is presented. In addition, accuracy improvements are achieved in this study, by a detailed examination of the appropriate adjustment of the parameters of the data transformation schemes tested, and of the handling of out of range values. With the appropriate tuning of these factors, the achieved mean localization error was 298 meters, and the median error was 109 meters. To facilitate the reproducibility of tests and comparability of results, the code and train validation test split used in this study are available.
The proliferation of Low Power Wide Area Networks (LPWAN), such as Sigfox and LoRaWAN, has brought a new domain of application of the fingerprinting methods. A recent study @cite_10 has experimentally verified the intuitive assumption that fingerprinting methods outperform, in terms of accuracy, proximity or ranging positioning methods, in a Sigfox setting.
@cite_10
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Because of the increasing relevance of the Internet of Things and location-based services, researchers are evaluating wireless positioning techniques, such as fingerprinting, on Low Power Wide Area Network (LPWAN) communication. In order to evaluate fingerprinting in large outdoor environments, extensive, time-consuming measurement campaigns need to be conducted to create useful datasets. This paper presents three LPWAN datasets which are collected in large-scale urban and rural areas. The goal is to provide the research community with a tool to evaluate fingerprinting algorithms in large outdoor environments. During a period of three months, numerous mobile devices periodically obtained location data via a GPS receiver which was transmitted via a Sigfox or LoRaWAN message. Together with network information, this location data is stored in the appropriate LPWAN dataset. The first results of our basic fingerprinting implementation, which is also clarified in this paper, indicate a mean location estimation error of 214.58 m for the rural Sigfox dataset, 688.97 m for the urban Sigfox dataset and 398.40 m for the urban LoRaWAN dataset. In the future, we will enlarge our current datasets and use them to evaluate and optimize our fingerprinting methods. Also, we intend to collect additional datasets for Sigfox, LoRaWAN and NB-IoT. Fingerprinting techniques, which are a common method for indoor localization, have been recently applied with success into outdoor settings. Particularly, the communication signals of Low Power Wide Area Networks (LPWAN) such as Sigfox, have been used for localization. In this rather recent field of study, not many publicly available datasets, which would facilitate the consistent comparison of different positioning systems, exist so far. In the current study, a published dataset of RSSI measurements on a Sigfox network deployed in Antwerp, Belgium is used to analyse the appropriate selection of preprocessing steps and to tune the hyperparameters of a kNN fingerprinting method. Initially, the tuning of hyperparameter k for a variety of distance metrics, and the selection of efficient data transformation schemes, proposed by relevant works, is presented. In addition, accuracy improvements are achieved in this study, by a detailed examination of the appropriate adjustment of the parameters of the data transformation schemes tested, and of the handling of out of range values. With the appropriate tuning of these factors, the achieved mean localization error was 298 meters, and the median error was 109 meters. To facilitate the reproducibility of tests and comparability of results, the code and train validation test split used in this study are available. The Internet of Things (IoT) has caused the modern society to connect everything in our environment to a network. In a myriad of IoT applications, smart devices need to be located. This can easily be done by satellite based receivers. However, there are more energy-efficient localization technologies, especially in Low Power Wide Area Networks (LPWAN). In this research, we discuss the accuracy of an outdoor fingerprinting technique using a large outdoor Sigfox dataset which is openly available. A kNN (k Nearest Neighbors) algorithm is applied to our fingerprinting database. 31 different distance functions and four RSS data representations are evaluated. Our analysis shows that a Sigfox transmitter can be located with a mean estimation error of 340 meters. Location-based services play an important role in Internet of Things (IoT) applications. However, a trade-off has to be made between the location estimation error and the battery lifetime of an IoT device. As IoT devices communicate over Low Power Wide Area Networks (LPWAN), signal strength localization methods can use the existing communication link to estimate their location. In this paper, we present a comparison of three proximity methods, one fingerprinting method and three ranging methods using Sigfox communication messages. To evaluate these methods, we use a ground truth Sigfox dataset which we collected in a large urban environment, as well as new evaluation data that was collected in the same urban area. With a mean estimation error of 586 m, our fingerprinting method achieves the best result compared to other signal strength localization methods. Positioning is an essential element in most Internet of Things (IoT) applications. Global Positioning System (GPS) chips have high cost and power consumption, making it unsuitable for long-range (LoRa) and low-power IoT devices. Alternatively, low-power wide-area (LPWA) signals can be used for simultaneous positioning and communication. We summarize previous studies related to LoRa signal-based positioning systems, including those addressing proximity, a path loss model, time difference of arrival (TDoA), and fingerprint positioning methods. We propose a LoRa signal-based positioning method that uses a fingerprint algorithm instead of a received signal strength indicator (RSSI) proximity or TDoA method. The main objective of this study was to evaluate the accuracy and usability of the fingerprint algorithm for large areas in the real world. We estimated the locations using probabilistic means based on three different algorithms that use interpolated fingerprint RSSI maps. The average accuracy of the three proposed algorithms in our experiments was 28.8 m. Our method also reduced the battery consumption significantly compared with that of existing GPS-based positioning methods. Low-Power Wide Area Networks (LPWANs) are continuously gaining momentum as fundamental enablers of the Internet of Things (IoT) paradigm. These networks provide longrange coverage to end nodes, exploiting license-free frequency bands. The focus of this work is on one of the most prominent LPWAN technologies: LoRa™. We implemented a new ns-3 module to study the performance of a LoRa-based IoT network in a typical urban scenario. Simulation results show that a LoRa network can scale well, achieving packet success rates above 95 in presence of a number of end devices in the order of 104. Low-Power Wide Area Networks (LPWANs) are prominent technologies in the field of the Internet of Things (IoT). Due to the long range capabilities and low energy consumption, Low-Power Wide Area Networks (LPWANs) are the ideal technologies to send small data occasionally. With their unique characteristics, LPWANs can be used in many applications and in different environments such as urban, rural and even indoor. The work in this paper presents a study on the LPWAN LoRa technology, by testing and evaluating its range, signal quality properties and its performance in delivering data. Three distinct scenarios are proposed and tested, one rural and two urban scenarios in Portugal. The maximum communication range achieved was 5660 meters for the rural scenario and around 2000 meters for the urban ones, and we concluded that there are three essential scenario characteristics that influence the achieved results: distance, elevation difference and obstacles in the signal path. Appearing on the stage quite recently, the Low Power Wide Area Networks (LPWANs) are currently getting much of attention. In the current paper we study the susceptibility of one LPWAN technology, namely LoRaWAN, to the inter-network interferences. By means of excessive empirical measurements employing the certified commercial transceivers, we characterize the effect of modulation coding schemes (known for LoRaWAN as data rates (DRs)) of a transmitter and an interferer on probability of successful packet delivery while operating in EU 868 MHz band. We show that in reality the transmissions with different DRs in the same frequency channel can negatively affect each other and that the high DRs are influenced by interferences more severely than the low ones. Also, we show that the LoRa-modulated DRs are affected by the interferences much less than the FSK-modulated one. Importantly, the presented results provide insight into the network-level operation of the LoRa LPWAN technology in general, and its scalability potential in particular. The results can also be used as a reference for simulations and analyses or for defining the communication parameters for real-life applications. Low-Power Wide-Area Network (LPWAN) technologies such as Long Range (LoRa) are emerging that enable power efficient wireless communication over very long distances. LPWAN devices typically communicate directly to a sink node which removes the need of constructing and maintaining a complex multi-hop network. However, to ensure efficient and reliable communication LPWAN devices often provide a large number of transmission parameters. For example, a LoRa device can be configured to use different spreading factors, bandwidth settings, coding rates and transmission powers, resulting in over 6720 possible settings. It is a challenge to determine the setting that minimises transmission energy cost while meeting the required communication performance. This paper is the first to present a thorough analysis of the impact of LoRa transmission parameter selection on communication performance. We study in detail the impact of parameter settings on energy consumption and communication reliability. Using this study we develop a link probing regime which enables us to quickly determine transmission settings that satisfy performance requirements. The presented work is a first step towards an automated mechanism for LoRa transmission parameter selection that a deployed LoRa network requires, but is not yet specified within the Long Range Wide Area Network (LoRaWAN) framework. Low power consumption, low transceiver chip cost and large coverage area are the main characteristics of the low power wide area networks (LPWAN) technologies. We expect that LPWAN can be part of enabling new human-centric health and wellness monitoring applications. Therefore in this work we study the indoor performance of one LPWAN technology, namely LoRa, by the means of real-life measurements. The measurements were conducted using the commercially available equipment in the main campus of the University of Oulu, Finland, which has an indoor area spanning for over 570 meters North to South and over 320 meters East to West. The measurements were executed for a sensor node operating close to human body that was periodically reporting the sensed data to a base station. The obtained results show that when using 14 dBm transmit power and the largest spreading factor of 12 for the 868 MHz ISM band, the whole campus area can be covered. Measured packet success delivery ratio was 96.7 without acknowledgements and retransmissions. We propose a new fingerprinting technique that differentiates between unique devices over a Wireless Local Area Network (WLAN) simply through the timing analysis of 802.11 probe request frames. Our technique can be applied to spoof detection, network reconnaissance, and implementation of access control against masquerading attacks. Experimental results indicate that our technique is consistent and accurate in differentiating between unique devices. In contrast with existing wireless fingerprinting techniques, our technique is passive, non-invasive and does not require the co-operation of fingerprintee hosts. The last years have seen the widespread diffusion of novel Low Power Wide Area Network (LPWAN) technologies, which are gaining momentum and commercial interest as enabling technologies for the Internet of Things. In this paper we discuss some of the most interesting LPWAN solutions, focusing in particular on LoRa™, one of the last born and most promising technologies for the wide-area IoT. The growing commercial interest in indoor location-based services (ILBS) has spurred recent development of many indoor positioning techniques. Due to the absence of global positioning system (GPS) signal, many other signals have been proposed for indoor usage. Among them, Wi-Fi (802.11) emerges as a promising one due to the pervasive deployment of wireless LANs (WLANs). In particular, Wi-Fi fingerprinting has been attracting much attention recently because it does not require line-of-sight measurement of access points (APs) and achieves high applicability in complex indoor environment. This survey overviews recent advances on two major areas of Wi-Fi fingerprint localization: advanced localization techniques and efficient system deployment. Regarding advanced techniques to localize users, we present how to make use of temporal or spatial signal patterns, user collaboration, and motion sensors. Regarding efficient system deployment, we discuss recent advances on reducing offline labor-intensive survey, adapting to fingerprint changes, calibrating heterogeneous devices for signal collection, and achieving energy efficiency for smartphones. We study and compare the approaches through our deployment experiences, and discuss some future directions. The rapid development of mobile Internet has offered the opportunity for WiFi indoor positioning to come under the spotlight due to its low cost. However, nowadays the accuracy of WiFi indoor positioning cannot meet the demands of practical applications. To solve this problem, this paper proposes an improved WiFi indoor positioning algorithm by weighted fusion. The proposed algorithm is based on traditional location fingerprinting algorithms and consists of two stages: the offline acquisition and the online positioning. The offline acquisition process selects optimal parameters to complete the signal acquisition, and it forms a database of fingerprints by error classification and handling. To further improve the accuracy of positioning, the online positioning process first uses a pre-match method to select the candidate fingerprints to shorten the positioning time. After that, it uses the improved Euclidean distance and the improved joint probability to calculate two intermediate results, and further calculates the final result from these two intermediate results by weighted fusion. The improved Euclidean distance introduces the standard deviation of WiFi signal strength to smooth the WiFi signal fluctuation and the improved joint probability introduces the logarithmic calculation to reduce the difference between probability values. Comparing the proposed algorithm, the Euclidean distance based WKNN algorithm and the joint probability algorithm, the experimental results indicate that the proposed algorithm has higher positioning accuracy. New transceiver technologies have emerged which enable power efficient communication over very long distances. Examples of such Low-Power Wide-Area Network (LPWAN) technologies are LoRa, Sigfox and Weightless. A typical application scenario for these technologies is city wide meter reading collection where devices send readings at very low frequency over a long distance to a data concentrator (one-hop networks). We argue that these transceivers are potentially very useful to construct more generic Internet of Things (IoT) networks incorporating multi-hop bidirectional communication enabling sensing and actuation. Furthermore, these transceivers have interesting features not available with more traditional transceivers used for IoT networks which enable construction of novel protocol elements. In this paper we present a performance and capability analysis of a currently available LoRa transceiver. We describe its features and then demonstrate how such transceiver can be put to use efficiently in a wide-area application scenario. In particular we demonstrate how unique features such as concurrent non-destructive transmissions and carrier detection can be employed. Our deployment experiment demonstrates that 6 LoRa nodes can form a network covering 1.5 ha in a built up environment, achieving a potential lifetime of 2 year on 2 AA batteries and delivering data within 5 s and reliability of 80 . Infrastructure monitoring applications currently lack a cost-effective and reliable solution for supporting the last communication hop for low-power devices. The use of cellular infrastructure requires contracts and complex radios that are often too power hungry and cost prohibitive for sensing applications that require just a few bits of data each day. New low-power, sub-GHz, long-range radios are an ideal technology to help fill this communication void by providing access points that are able to cover multiple kilometers of urban space with thousands of end-point devices. These new Low-Power Wide-Area Networking (LPWAN) platforms provide a cost-effective and highly deployable option that could piggyback off of existing public and private wireless networks (WiFi, Cellular, etc). In this paper, we present OpenChirp, a prototype end-to-end LPWAN architecture built using LoRa Wide-Area Network (LoRaWAN) with the goal of simplifying the design and deployment of Internet-of-Things (IoT) devices across wide areas like campuses and cities. We present a software architecture that exposes an application layer allowing users to register devices, describe transducer properties, transfer data and retrieve historical values. We define a service model on top of LoRaWAN that acts as a session layer to provide basic encoding and syntax to raw data streams. At the device-level, we introduce and benchmark an open-source hardware platform that uses Bluetooth Low-Energy (BLE) to help provision LoRa clients that can be extended with custom transducers. We evaluate the system in terms of end-node energy consumption, radio penetration into buildings as well as coverage provided by a network currently deployed at Carnegie Mellon University. Intermittently powered applications create a need for low-cost security and privacy in potentially hostile environments, supported by primitives including identification and random number generation. Our measurements show that power-up of SRAM produces a physical fingerprint. We propose a system of fingerprint extraction and random numbers in SRAM (FERNS) that harvests static identity and randomness from existing volatile CMOS memory without requiring any dedicated circuitry. The identity results from manufacture-time physically random device threshold voltage mismatch, and the random numbers result from runtime physically random noise. We use experimental data from high-performance SRAM chips and the embedded SRAM of the WISP UHF RFID tag to validate the principles behind FERNS. For the SRAM chip, we demonstrate that 8-byte fingerprints can uniquely identify circuits among a population of 5,120 instances and extrapolate that 24-byte fingerprints would uniquely identify all instances ever produced. Using a smaller population, we demonstrate similar identifying ability from the embedded SRAM. In addition to identification, we show that SRAM fingerprints capture noise, enabling true random number generation. We demonstrate that a 512-byte SRAM fingerprint contains sufficient entropy to generate 128-bit true random numbers and that the generated numbers pass the NIST tests for runs, approximate entropy, and block frequency. The Received Signal Strength (RSS) based fingerprinting approaches for indoor localization pose a need for updating the fingerprint databases due to dynamic nature of the indoor environment. This process is hectic and time-consuming when the size of the indoor area is large. The semi-supervised approaches reduce this workload and achieve good accuracy around 15 percent of the fingerprinting load but the performance is severely degraded if it is reduced below this level. We propose an indoor localization framework that uses unsupervised manifold alignment. It requires only 1 percent of the fingerprinting load, some crowd sourced readings, and plan coordinates of the indoor area. The 1 percent fingerprinting load is used only in perturbing the local geometries of the plan coordinates. The proposed framework achieves less than 5 m mean localization error, which is considerably better than semi-supervised approaches at very small amount of fingerprinting load. In addition, the few location estimations together with few fingerprints help to estimate the complete radio map of the indoor environment. The estimation of radio map does not demand extra workload rather it employs the already available information from the proposed indoor localization framework. The testing results for radio map estimation show almost 50 percent performance improvement by using this information as compared to using only fingerprints. The performance of an automatic fingerprint authentication system relies heavily on the quality of the captured fingerprint images. In this paper, two new quality indices for fingerprint images are developed. The first index measures the energy concentration in the frequency domain as a global feature. The second index measures the spatial coherence in local regions. We present a novel framework for evaluating and comparing quality indices in terms of their capability of predicting the system performance at three different stages, namely, image enhancement, feature extraction and matching. Experimental results on the IBM-HURSLEY and FVC2002 DB3 databases demonstrate that the global index is better than the local index in the enhancement stage (correlation of 0.70 vs. 0.50) and comparative in the feature extraction stage (correlation of 0.70 vs. 0.71). Both quality indices are effective in predicting the matching performance, and by applying a quality-based weighting scheme in the matching algorithm, the overall matching performance can be improved; a decrease of 1.94 in EER is observed on the FVC2002 DB3 database. In order to utilize peer-to-peer (P2P) networks in legal content distribution to benefit the legal content providers, copyright protection needs to be enhanced. In this paper, a fingerprint generation and embedding method is proposed for complex P2P file sharing networks. In this method, wavelet and principal component analysis (PCA) techniques are used for fingerprint generation. First, the wavelet technique obtains a low-frequency representation of the test image (or source file, which is assumed to be one I frame of a video with a DVD quality) and PCA finds the features of the representation. Then, a set of fingerprint matrices can be created based on a proposed algorithm. Finally, each matrix combines with the low-frequency representative to become a unique fingerprinted matrix. The fingerprinted matrix is not only much smaller than the original image in size but also contains the most important information. Without this information, the quality of the reconstructed image will be very poor. Thus, the fingerprinted file is more suitable for distribution in P2P networks, because, in the distribution stage, the uniquely fingerprinted matrix will only be dispensed by the source host and leave the rest for P2P networks to handle. On the other hand, among other frames of the same video which are not decomposed, some will be embedded with sharable fingerprints. The relationship between unique fingerprint and sharable fingerprint and the purpose of using it will be discussed in the paper. Our result indicates that the proposed fingerprint has shown strong robustness against common attacks such as Gaussian noise, median filter, and lossy compression.