RSSI-Based Indoor Localization With the Internet of Things

As humans, nearly eighty percent of our day-to-day activities are conducted in an indoor environment, making indoor tracking and monitoring extremely necessary. The needs of having an Indoor Positioning System (IPS) has become crucial as the development is critically challenged due to the fact that satellite signals via Global Positioning System (GPS) cannot penetrate through a building. Variety techniques and approaches on IPS have been proposed but the most desirable approach used is the Received Signal Strength Indicator (RSSI) due to existing infrastructure already in place to provide a low-cost implementation. Internet of Things (IoT) itself has brought drastic changes and opened new opportunities for growth and innovation in technology. An integration between RSSI and IoT is the perfect combination of the development of IPS to allow an increased accuracy. This paper proposes an Indoor Monitoring and Positioning System (IMPS) using RSSI algorithm and triangulation technique integrated with IoT. The experimental results show that the proposed IMPS has achieved an average accuracy of 0.5 m for 2D. Future work has suggested an improvement in the IMPS with AR mapping integrated with Machine Learning.

Recent developments in machine learning algorithms are playing a significant role in wireless communication and Internet of Things (IoT) systems. Location-based Internet of Things services (LBIoTS) are considered one of the primary applications among those IoT applications. The key information involved in LBIoTS is finding an object’s geographical location. The Global Positioning System (GPS) technique does not perform better in indoor environments due to multipath. Numerous methods have been investigated for indoor localization scenarios. However, the precise location estimation of a moving object in such an application is challenging due to the high signal fluctuations. Therefore, this paper presents machine learning algorithms to estimate the object’s location based on the Received Signal Strength Indicator (RSSI) values collected through Bluetooth low-energy (BLE)-based nodes. In this experiment, we utilize a publicly available RSSI dataset. The RSSI data are collected from different BLE ibeacon nodes installed in a complex indoor environment with labels. Then, the RSSI data are linearized using the weighted least-squares method and filtered using moving average filters. Moreover, machine learning algorithms are used for training and testing the dataset to estimate the precise location of the objects. All the proposed algorithms were tested and evaluated under their different hyperparameters. The tested models provided approximately 85% accuracy for KNN, 84% for SVM and 76% accuracy in FFNN.

The increasing availability of mobile devices with wireless communications capabilities has stimulated the growth of indoor positioning services. Indoor positioning is used to locate, in real time, devices’ positions for easy access. The indoor positioning, however, is challenging compared to outdoor positioning due to the large number of obstacles. Global positioning system is ideal for outdoor localization but fails in indoor environments with limited space. Recent development of the Internet of Things (IoT) has brought forth portable and cost-effective wireless technologies that can be used for indoor positioning. In this work, an adaptive trilateration algorithm based on received signal strength indicator (RSSI) was proposed. To assess the positioning accuracy of the proposed algorithm, Bluetooth Low Energy (BLE), Wi-Fi (IEEE 802.11n), ZigBee and LoRaWAN IoT technologies were used. Results show that the error performance is improved by 4% in BLE, 17% in ZigBee, 22% in Wi-Fi and 33% in LoRaWAN when compared to the existing related work.

The Received Signal Strength Indication (RSSI) value of Bluetooth can be used to estimate distance between Internet of Things (IoT) devices. IoT devices, within Wireless Body Area Network (WBAN) area, can recognize each other in office environment automatically. Peer to peer distance estimation using the RSSI of Bluetooth is difficult because of large deviation of RSSI value. This paper describes the experimental results of RSSI measurement between IoT devices in office environment. And it applies the Low Pass Filter (LPF) to reduce the deviation of RSSI value. In east direction, when RSSI raw data is used, there is an overlap region, 23dBm, between WBAN area and outside of WBAN area. However, when LPF data is used, there is a gap about 1.7dBm. So, we can estimate distance using Bluetooth RSSI LPF data whether the IoT device is within WBAN area or not.

COVID-19 has had a devastating impact worldwide, including in care homes where there have been substantial numbers of cases among a very vulnerable population. A key mechanism for managing exposure to the virus and targeting interventions is contact tracing. Unfortunately, environments such as care homes that were most catastrophically impacted by COVID-19 are also those least amenable to traditional contact tracing. A promising alternative to recall and smartphone-based contact tracing approaches is the use of discrete wearable devices that exploit Bluetooth Low Energy (BLE) and Long-Range Wide Area Network (LoRaWAN) technologies. However, the real-world performance of these devices in the context of contact tracing is uncertain. A series of experiments were conducted to evaluate the performance of a wearables system that is based on BLE and LoRaWAN technologies. In each experiment, the number of successful contacts was recorded and the physical distance between two contacts was compared to a calculated distance using the Received Signal Strength Indication (RSSI) to determine the precision, error rate, and duration of proximity. The overall average system contact detection success rate was measured as 75.5%; when wearables were used as per the manufacturer’s guidelines the contact detection success rate increased to 81.5%, but when obstructed by everyday objects such as clothing or inside a bag the contact detection success rate was only 64.2%. The calculated distance using RSSI was close to the physical distance in the absence of obstacles. However, in the presence of typical obstacles found in care home settings, the reliability of detection decreased, and the calculated distance usually appeared far from the actual contact point. The results suggest that under real-world conditions there may be a large proportion of contacts that are underestimated or undetected.

The Internet of Things (IoT) is increasing in size by having more devices connected to it as they are becoming low-cost to manufacture and easier to connect to the internet. New use cases are being created by the need for it and feasibility to provide it, with low-cost solutions. As a key enabler of the IoT, Long Range Wide Area Network (LoRaWAN) is gaining great attention in research and industry. It provides a desirable solution for applications that require hundreds or thousands of actively connected devices to monitor a process or an environment or to assist in controlling a certain process. Some of these IoT use cases require having the location information of the IoT devices. In some cases, localization can be the intrinsic purpose of deployment. In this regard, the received signal strength indicator (RSSI)-based localization offers a feasible and affordable solution. Since LoRaWAN has only been there for only a few years, research on utilizing LoRaWAN RSSI for localization purposes is in early stages and is scarce. In this paper, we study LoRaWAN RSSI-based localization and evaluate its accuracy, impairments, and prospects. In addition, we employ the use of software-defined radios (SDR) into our work for the purpose of path-loss characterization. The experimental results revealed the fact that a high variance of RSSI due to frequency hopping feature of LoRaWAN could severely impact the localization performance. Potential solutions are developed and presented to reduce this negative impact, hence improve the performance.

In this paper, we propose a two-tier ratio metric and Residue Under Curve (RUC) metric method to detect the compromised node for different attacks in the Internet of Things (IoT) framework. We compare the performance of the proposed method for Wireless Fidelity (Wi-Fi), Bluetooth Low Energy (BLE), Zigbee, and Long-Range Wide-Area Network (LoRaWAN) utilizing the Received Signal Strength (RSS) values of the signals. Additionally, the attack tolerance level of different protocols is investigated. The experiments are conducted on a popular IoT database. Results show that the proposed method successfully detects the compromised node for multiple IoT protocols.

In recent years, Long Range Wide Area Network (LoRaWAN) has attracted attention because it enables long-distance communication with low power. LoRaWAN adopts the MAC protocol is based on Pure ALOHA and the terminal can send packets at any time. However, the problem is that packet collisions frequently occur as the number of terminals increases. On the other hand, Bluetooth Low Energy (BLE) which is standardized for short-distance communication for IoT is now widely used in various IoT devices because it enables high speed and stable communication. Therefore, we proposed a method that can reduce the traffic on the LoRaWAN by dynamically selecting LoRaWAN and BLE when the terminals move in an environment where it can use both systems. However, it is necessary to consider the Quality of Service (QoS) requirements which are the application requirements of the terminal because LoRaWAN and BLE is standardized supposed different target applications. Therefore, in this paper, we propose a method that can adaptively select a system by weighting the QoS realizations of each system such as the packet error rate predicted using the spectrum database and the power consumption during communication with the application requirements of the terminal.

Implementation of an indoor localisation algorithm for Internet of Things

Indoor positioning has attracted much attention in recent years due to the trend of Internet of Things (IoT), which is capable of providing numerous applications such as personal tracking, vehicle locator, and Location-Based Service (LBS). Since Global Navigation Satellite System (GNSS) is no longer useful in the indoor environment because of the sheltered satellite signals, Bluetooth Low Energy (BLE) is a good alternative for realizing indoor positioning. BLE, the latest version of Bluetooth in 2010, has the advantages of low cost, low power consumption, and pervasive availability in smartphones. However, BLE still faces some challenges in terms of the fluctuating Received Signal Strength Indicator (RSSI) caused by the environment such as reflection, fading and multipath effect. Those variable RSSI values make it difficult to derive positioning result accurately. To reduce the environmental effect, the concepts of Differential Global Navigation Satellite System (DGNSS) and Network Real Time Kinematic (NRTK) motivate a novel method named Differential Distance Correction proposed in this study. This method utilizes the differential information from the reference station at the known coordinate to correct the distance measurement of the rover station and further enhance the positioning accuracy of it. In this study, power regression model is used to convert RSSI to distance; then Differential Distance Correction is applied to obtain better distance measurements of the rover. In addition, trilateration is selected as the primary positioning technique, which exploits estimated distances to determine the target’s position. This technique can be solved by two main approaches: linear and nonlinear least squares. With corrected distance measurements, it can gain more accurate result through trilateration compared with simply using the original distances. The experimental results presented in this study illustrate that the overall positioning error using the proposed method can be reduced to less than 2.5 m in 90% positioning error, and 2 m in RMSE with both improvement ratios over 30%.

The Internet of Things (IoT) idea now entails a dispersed collection of different sensor networks with several functions that collect data, which is then analysed and used in applications such as smart cities. These networks are capable of transmitting massive volumes of data in a relatively efficient, yet unsecure wireless environment. These applications will only succeed if is developed a dependable a low-cost real-time method for pinpointing accurate location. Power consumption is another consideration for indoor localisation. Recent wireless technologies like ZigBee, Bluetooth Low Energy (BLE) and Long Range (LoRa), use less resources, making them ideal for interior positioning. When IoT devices are utilised, these technologies are compared in terms of precision of localisation and power consumption. Tracking the Received Signal Strength Indicator (RSSI) values can be used to locate mobile sensor nodes for low-power IoT networks. The RSSI values of sensor nodes in the BLE, ZigBee and LoRa networks for IoT were explore in this study.

With the start of the Fourth Industrial Revolution, Internet of Things (IoT), artificial intelligence (AI), and big data technologies are attracting global attention. AI can achieve fast computational speed, and big data makes it possible to store and use vast amounts of data. In addition, smartphones, which are IoT devices, are owned by most people. Based on these advantages, the above three technologies can be combined and effectively applied to navigation technology. In the case of an outdoor environment, global positioning system (GPS) technology has been developed to enable relatively accurate positioning of the user. However, due to the problem of radio wave loss because of many obstacles and walls, there are obvious limitations in applying GPS to indoor environments. Hence, we propose a method to increase the accuracy of user positioning in indoor environments using wireless-fidelity (Wi-Fi). The core technology of the proposed method is to limit the initial search region of the particle swarm optimization (PSO), an intelligent particle algorithm; doing so increases the probability that particles converge to the global optimum and shortens the convergence time of the algorithm. For this reason, the proposed method can achieve fast processing time and high accuracy. To limit the initial search region of the PSO, we first build an received signal strength indicator (RSSI) database for each sample point (SP) using a fingerprinting scheme. Then, a limited region is established through a fuzzy matching algorithm. Finally, the particles are randomly distributed within a limited region, and then the user’s location is positioned through a PSO. Simulation results confirm that the method proposed in this paper achieves the highest positioning accuracy, with an error of about 1 m when the SP interval is 3 m in an indoor environment.

Internet of Things (IoT) has penetrated different aspects of our modern life where smart sensors enabled with Bluetooth Low Energy (BLE) are deployed increasingly within our surrounding indoor environments. BLE-based localization is, typically, performed based on Received Signal Strength Indicator (RSSI), which suffers from different drawbacks due to its significant fluctuations. In this paper, we focus on a multiplemodel estimation framework for analyzing and addressing effects of orientation of a BLE-enabled device on indoor localization accuracy. The fusion unit of the proposed method would merge orientation estimated by RSSI values and heading estimated by Inertial Measurement Unit (IMU) sensors to gain higher accuracy in orientation classification. In contrary to existing RSSIbased solutions that use a single path-loss model, the proposed framework consists of eight orientation-matched path loss models coupled with a multi-sensor and data-driven classification model that estimates the orientation of a hand-held device with high accuracy of 99%. By estimating the orientation, we could mitigate the effect of orientation on the RSSI values and consequently improve RSSI-based distance estimates. In particular, the proposed data-driven and multiple-model framework is constructed based on over 10 million RSSI values and IMU sensor data collected via an implemented LBS platform.

WiFi and Bluetooth Low Energy (BLE) are widely used in Internet of Things (IoT) devices. Since WiFi and BLE work within the overlapped ISM 2.4 GHz band, they will interfere with each other. Existing approaches have demonstrated their effectiveness in mitigating the interference. However, further performance improvement has been hampered by the design goal of exclusive communication of WiFi or BLE, which only allows one WiFi or BLE device to transmit packets at any specific time slot on the overlapped channel within the communication range. In this paper, we explore a new communication method, called Parallel Inclusive Communication (PIC), which leverages the unique modulation schemes of WiFi and BLE for parallel inclusive bi-directional transmission of both WiFi and BLE data at the same time within the overlapped channel. In this communication system, the PIC gateway is designed upon the IEEE 802.11g and 802.15.1 frameworks while the WiFi and BLE clients are commercial off-the-shelf devices. PIC achieves similar data rates for these parallel WiFi and BLE communications as if WiFi and BLE are communicating separately. PIC's system architecture naturally fits at the edge of the Internet, which is an optimal site for concurrently collecting (or disseminating) data from (or to) an exponentially increasing number of IoT devices that are using WiFi or BLE. We conducted extensive evaluations under four real-world scenarios. Results show that compared with existing approaches, PIC can significantly i) increase the packet reception ratios by 183%; ii) reduce the round-trip delay time by 590 times and energy consumption by 50.5 times; and iii) improve the throughput under WiFi and BLE coexistence scenarios.

IoT (Internet of Things) has become attractive and ubiquitous in recent years. BLE (Bluetooth Low Energy) is a protocol widely used in IoT. For BLE, it has low cost, low energy consumption, and is supported on most types of mobile phones. BLE Beacon which is widely applied in many situations, such as shopping malls and indoor navigation, is an application based on the BLE network. This paper is aimed at proposing a feasible scheme to enhance the performance of the BLE Beacon application. communications in BLE networks involve the neighbor discovery process (NDP) in the first phase to create a connection or exchange information with neighbors. When more people with mobile devices simultaneously send requests to the BLE Beacon, the success probability of the NDP decreases and then the latency of the NDP increases. It leads to poor customer experience. Therefore, we propose an Early Wake-Up and Access Barring (EWAB) scheme to improve the NDP performance among an advertiser and multiple scanners. The scheme makes good use of the information that can be contained in the advertising packet to adjust the scan interval and the backoff window dynamically. The results show that EWAB provides better performance in terms of the probability of successful discovery, the mean discovery latency, and power consumption in the BLE networks.