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

A fully integrated RSSI and an ultra-low power SAR ADC for 5.8 GHz DSRC ETC transceiver

This study presents a monolithic received signal strength indicator (RSSI) and an ultra-low power SAR ADC for 5.8 GHz DSRC transceiver in China electronic toll collection systems. In order to meet the stringent requirement of wide input range for the transceiver, two RSSIs collaborate with auxiliary ADC circuits to provide the digitalized received signal strength to the digital baseband of a transceiver. The RSSI design achieves fast transient response and low power consumption with a small die area by using internal active low-pass filters instead of external passive ones. The proposed design has been fabricated using a 0.13 μm 2P6M CMOS technology. Measurement results show that the overall input dynamic range is 86 dB with an accuracy of ±1.72 dB and a transient response of less than 2 μs. Compared with the state-of-the-art designs in the literature, the overall input range and transient settling time are improved by at least 14.6%, and 300%, respectively

Real-Time Localization Using Software Defined Radio

Service providers make use of cost-effective wireless solutions to identify, localize, and possibly track users using their carried MDs to support added services, such as geo-advertisement, security, and management. Indoor and outdoor hotspot areas play a significant role for such services. However, GPS does not work in many of these areas. To solve this problem, service providers leverage available indoor radio technologies, such as WiFi, GSM, and LTE, to identify and localize users. We focus our research on passive services provided by third parties, which are responsible for (i) data acquisition and (ii) processing, and network-based services, where (i) and (ii) are done inside the serving network. For better understanding of parameters that affect indoor localization, we investigate several factors that affect indoor signal propagation for both Bluetooth and WiFi technologies. For GSM-based passive services, we developed first a data acquisition module: a GSM receiver that can overhear GSM uplink messages transmitted by MDs while being invisible. A set of optimizations were made for the receiver components to support wideband capturing of the GSM spectrum while operating in real-time. Processing the wide-spectrum of the GSM is possible using a proposed distributed processing approach over an IP network. Then, to overcome the lack of information about tracked devices’ radio settings, we developed two novel localization algorithms that rely on proximity-based solutions to estimate in real environments devices’ locations. Given the challenging indoor environment on radio signals, such as NLOS reception and multipath propagation, we developed an original algorithm to detect and remove contaminated radio signals before being fed to the localization algorithm. To improve the localization algorithm, we extended our work with a hybrid based approach that uses both WiFi and GSM interfaces to localize users. For network-based services, we used a software implementation of a LTE base station to develop our algorithms, which characterize the indoor environment before applying the localization algorithm. Experiments were conducted without any special hardware, any prior knowledge of the indoor layout or any offline calibration of the system

A Comparison Analysis of BLE-Based Algorithms for Localization in Industrial Environments

Proximity beacons are small, low-power devices capable of transmitting information at a limited distance via Bluetooth low energy protocol. These beacons are typically used to broadcast small amounts of location-dependent data (e.g., advertisements) or to detect nearby objects. However, researchers have shown that beacons can also be used for indoor localization converting the received signal strength indication (RSSI) to distance information. In this work, we study the effectiveness of proximity beacons for accurately locating objects within a manufacturing plant by performing extensive experiments in a real industrial environment. To this purpose, we compare localization algorithms based either on trilateration or environment fingerprinting combined with a machine-learning based regressor (k-nearest neighbors, support-vector machines, or multi-layer perceptron). Each algorithm is analyzed in two different types of industrial environments. For each environment, various configurations are explored, where a configuration is characterized by the number of beacons per square meter and the density of fingerprint points. In addition, the fingerprinting approach is based on a preliminary site characterization; it may lead to location errors in the presence of environment variations (e.g., movements of large objects). For this reason, the robustness of fingerprinting algorithms against such variations is also assessed. Our results show that fingerprint solutions outperform trilateration, showing also a good resilience to environmental variations. Given the similar error obtained by all three fingerprint approaches, we conclude that k-NN is the preferable algorithm due to its simple deployment and low number of hyper-parameters

Acoustic indoor localization employing code division multiple access

Thesis (Master)--Izmir Institute of Technology, Electronics and Communication Engineering, Izmir, 2010Includes bibliographical references (leaves: 107-108)Text in English; Abstract: Turkish and Englishxvi, 160 69 leavesIndoor localization becomes a demand that comes into prominence day by day. Although extensively used outdoor location systems have been proposed, they can not operate in indoor applications. Hence new investigations have been carried on for accurate indoor localization in the last decade. In this thesis, a new indoor location system, that aims to locate an entity within an accuracy of about 2 cm using ordinary and inexpensive off-the-shelf devices, has been proposed and an implementation has been applied to evaluate the system performance. Therefore, time of arrival measurements of acoustic signals, which are binary phase shift keying modulated Gold code sequences using direct sequence spread spectrum technique, are done. Direct sequence-code division multiple access is applied to perform simultaneous accurate distance measurements and provides immunity to noise and interference. Two methods have been proposed for the location estimation. The first method takes the average of four location estimates obtained by trilateration technique. In the second method, only a single robust position estimate is obtained using three distances while the least reliable fourth distance measurement is not taken into account. The system performance is evaluated at positions from two height levels using two sets of variables determined by experimental results. The precision distributions in the work area and the precision versus accuracy plots depict the system performance for different sets of variables. The proposed system provides location estimates of better than 2 cm accuracy within 99% precision. Eventually, created graphical user interface provides a user friendly environment to adjust the parameters