Enhancing iBeacon Based Micro-Location with Particle Filtering

Bluetooth Low Energy (BLE) is one of the key technologies empowering the Internet of Things (IoT) for indoor positioning. In this regard, Angle of Arrival (AoA) localization is one of the most reliable techniques because of its low estimation error. BLE-based AoA localization, however, is in its infancy as only recently direction-finding feature is introduced to the BLE specification. Furthermore, AoA-based approaches are prone to noise, multi-path, and path-loss effects. The paper proposes an efficient Convolutional Neural Network (CNN)-based indoor localization framework to tackle these issues specific to BLE-based settings. We consider indoor environments without presence of Line of Sight (LoS) links affected by Additive White Gaussian Noise (AWGN) with different Signal to Noise Ratios (SNRs) and Rayleigh fading channel. Moreover, by assuming a 3-D indoor environment, the destructive effect of the elevation angle of the incident signal is considered on the position estimation. The effectiveness of the proposed CNN-AoA framework is evaluated via an experimental testbed, where In-phase/Quadrature (I/Q) samples, modulated by Gaussian Frequency Shift Keying (GFSK), are collected by four BLE beacons. Simulation results corroborate effectiveness of the proposed CNN-based AoA technique to track mobile agents with high accuracy in the presence of noise and Rayleigh fading channel.

Bluetooth Low Energy (BLE) based Wireless Indoor Localization System (WILS) with high localization accuracy and high localization precision is a key requirement in enabling the Internet of Things (IoT) in today’s applications. In this paper, we investigated the effect of BLE signal variations on indoor localization caused by the change in BLE transmission power levels. This issue is not often discussed as most of the works on localization algorithms use the highest power levels but has important practical implications for energy efficiency, e.g., if a designer would like to trade-off localization performance and node lifetime. To analyze the impact, we used the established trilateration based localization model with two methods i.e., Centroid Approximation (CA) and Minimum Mean Square Error (MMSE). We observed that trilateration based localization with MMSE method outperforms the CA method. We further investigated the use of two filters i.e., Low Pass Filter (LPF) and Kalman Filter (KF) and evaluated their effects in terms of mitigating the random variations from BLE signal. In comparison to non-filter based approach, we observed a great improvement in localization accuracy and localization precision with a filter-based approach. Furthermore, in comparison to LPF based trilateration localization with CA, the performance of a KF based trilateration localization with MMSE is far better. An average of 1 m improvement in localization accuracy and approximately 50% improvement in localization precision is observed by using KF in trilateration based localization model with the MMSE method. In conclusion, with KF in trilateration based localization model with MMSE method effectively eliminates random variations in BLE RSS with multiple transmission power levels and thus results in a BLE based WILS with high accuracy and high precision.

COVID-19 is highly contagious and spreads rapidly; it can be transmitted through coughing or contact with virus-contaminated hands, surfaces, or objects. The virus spreads faster indoors and in crowded places; therefore, there is a huge demand for contact tracing applications in indoor environments, such as hospitals and offices, in order to measure personnel proximity while placing as little load on them as possible. Contact tracing is a vital step in controlling and restricting pandemic spread; however, traditional contact tracing is time-consuming, exhausting, and ineffective. As a result, more research and application of smart digital contact tracing is necessary. As the Internet of Things (IoT) and wearable sensor device studies have grown in popularity, this work has been based on the practicality and successful implementation of Bluetooth low energy (BLE) and radio frequency identification (RFID) IoT based wireless systems for achieving contact tracing. Our study presents autonomous, low-cost, long-battery-life wireless sensing systems for contact tracing applications in hospital/office environments; these systems are developed with off-the-shelf components and do not rely on end user participation in order to prevent any inconvenience. Performance evaluation of the two implemented systems is carried out under various real practical settings and scenarios; these two implemented centralised IoT contact tracing devices were tested and compared demonstrating their efficiency results.

Recently, indoor location-based services (LBS) have become attractive with the rapid development of indoor location estimation and with the spread of smartphones. An estimation technique using Bluetooth low energy (BLE) tags becomes most attractive and promising among other techniques for estimating location because a majority of smartphones supports BLE. The BLE tags transmit advertisement (ADV) packets periodically, which include a unique ID related to each tag. When a smartphone receives the ADV packet, the smartphone sends a query to the server to retrieve the locationbased information for the ID. To provide such a service continuously, operators have to monitor whether BLE tags work well and repair them in the event of a problem. These operations are difficult because they require physical labor that cannot be automated for a large number of BLE tags spread over a wide area. Therefore, we evaluated the operations of BLE tags quantitatively by the metric of operational costs. We established an environment composed of 54 BLE tags in a four-story building and measured the lifetime of the battery, the service availability ratio, and the operational costs. Through the experiments, we obtained various results regarding the battery management of the BLE tags and the effectiveness of redundant deployment.

In recent years, the Internet of Things (IoT) has grown to include the tracking of devices through the use of Indoor Positioning Systems (IPS) and Location Based Services (LBS). When designing an IPS, a popular approach involves using wireless networks to calculate the approximate location of the target from devices with predetermined positions. In many smart building applications, LBS are necessary for efficient workspaces to be developed. In this paper, we examine two memoryless positioning techniques, K-Nearest Neighbor (KNN), and Naive Bayes, and compare them with simple trilateration, in terms of accuracy, precision, and complexity. We present a comprehensive analysis between the techniques through the use of three popular IoT wireless technologies: Zigbee, Bluetooth Low Energy (BLE), and WiFi (2.4 GHz band), along with three experimental scenarios to verify results across multiple environments. According to experimental results, KNN is the most accurate localization technique as well as the most precise. The RSSI dataset of all the experiments is available online.

In daily life, mobile and wearable devices with high computing power, together with anchors deployed in indoor en-vironments, form a common solution for the increasing demands for indoor location-based services. Within the technologies and methods currently in use for indoor localization, the approaches that rely on Bluetooth Low Energy (BLE) anchors, Received Signal Strength (RSS), and lateration are among the most popular, mainly because of their cheap and easy deployment and accessible infrastructure by a variety of devices. Never-theless, such BLE- and RSS-based indoor positioning systems are prone to inaccuracies, mostly due to signal fluctuations, poor quantity of anchors deployed in the environment, and/or inappropriate anchor distributions, as well as mobile device hardware variability. In this paper, we address these issues by using a collaborative indoor positioning approach, which exploits neighboring devices as additional anchors in an extended positioning network. The collaborating devices' information (i.e., estimated positions and BLE- RSS) is processed using a multilayer perceptron (MLP) neural network by taking into account the device specificity in order to estimate the relative distances. After this, the lateration is applied to collaboratively estimate the device position. Finally, the stand-alone and collaborative position estimates are combined, providing the final position estimate for each device. The experimental results demonstrate that the proposed collaborative approach outperforms the stand-alone lateration method in terms of positioning accuracy.

Bluetooth Low Energy (BLE) is a widely adopted wireless communication technology in the Internet of Things (IoT). BLE offers secure communication through a set of pairing strategies. However, these pairing strategies are obsolete in the context of IoT. The security of BLE based devices relies on physical security, but a BLE enabled IoT device may be deployed in a public environment without physical security. Attackers who can physically access a BLE-based device will be able to pair with it and may control it thereafter. Therefore, manufacturers may implement extra authentication mechanisms at the application layer to address this issue. In this paper, we design and implement a BLE Security Scan (BLESS) framework to identify those BLE apps that do not implement encryption or authentication at the application layer. Taint analysis is used to track if BLE apps use nonces and cryptographic keys, which are critical to cryptographic protocols. We scan 1073 BLE apps and find that 93% of them are not secure. To mitigate this problem, we propose and implement an application-level defense with a low-cost $0.55 crypto co-processor using public key cryptography.

Indoor positioning systems (IPS), one of the emerging technologies, play an important role in the Internet of Things (IoT) that addresses the challenges of GPS in indoor environments. This technology creates a great impact on real world application. Bluetooth Low Energy (BLE) has emerged as one of the most cost-effective, energy-efficient mechanisms for facilitating indoor localization. A problem with many available BLE fingerprinting techniques is that they do not consider differences in signal characteristics due to the orientation of the device, and limited statistical evidence exists to demonstrate variations in performance. The present study developed a machine learning-based indoor localization framework using BLE 5.1 Received Signal Strength (RSS) and Angle-of-Arrival (AoA) data to evaluate the effect of a device’s orientation on decimeter-level localization accuracy. Real-world static datasets were assembled under four orientation conditions: North, South, East, and West. In addition, temporal windowing and median-based fingerprint generation techniques were used to alleviate the impact of signal instability on accuracy. Random Forest (RF), k-Nearest Neighbour (KNN), and Multilayer Perceptron (MLP) were used to generate estimates of 2D positions based on the generated fingerprint. Performance of models was evaluated using distance-based error metrics, percentiles, reliability analysis, and the Wilcoxon signed-rank test for statistical assessment. The results obtained indicate that device orientation is a significant factor affecting localization accuracy, and Random Forest (RF) produces more robust localization accuracy than KNN and MLP across each of the four cardinal orientations. The results also illustrated the need to use orientation-aware modeling techniques to create a reliable BLE-based IPS.

Nowadays, the Internet of Things (IoT) is spreading rapidly towards creating smart environments.Home automation, intra-vehicular interaction, and wireless sensor networks (WSN) are among the most popular applications discussed in IoT literature.One of the most available and popular wireless technologies for short-range operations is Bluetooth.In late 2010, the Bluetooth Special Interest Group (SIG) launched the Bluetooth 4.0 Specification, which brings Bluetooth Low Energy (BLE) as part of the specification.BLE characterises as being a very low power wireless technology, capable of working on a coin-cell or even by energy scavenging.Nevertheless, the nature of Bluetooth (and BLE) has always been a connection-oriented communication in a Master/Slave configuration.Several studies exist showing how to create mesh networks for Classic Bluetooth, called Scatternets, by utilizing some nodes as slaves to relay data between Masters.However, BLE didn't support role changing until the 4.1 Specification released in 2013.The capability of a wireless technology to create a Mobile Ad-Hoc Network (MANET) is vital for supporting the plethora of sensors, peripherals, and devices that could coexist in any IoT environment.This work focuses on proposing a new autoconfiguring dynamic address allocation scheme for a BLE Ad-Hoc network, and a network map discovery and maintenance mechanism that doesn't require role changing, thus being possible to implement it in 4.0 compliant devices as well as 4.1 or later to develop a MANET.Any ad-hoc routing protocol can utilise the proposed method to discover, keep track, and maintain the mesh network node topology in each of their nodes.

In this paper, we propose a system for using a commercially available Bluetooth Low Energy (BLE) sensor underwater. Our system comprises a mounting device located close to the BLE sensor and a part located on the water surface. In the water, the mounting device receives the BLE signals from the sensor and converts them into acoustic waves that are emitted from a speaker. The part on the water surface receives those acoustic waves via a microphone and demodulates the data transmitted by the BLE sensor. The proposed system enables existing BLE sensors to be used underwater. Some of the functions of our proposed system are developed and evaluated using commercially available products, and the basic nerformance of the system is confirmed.

In complex indoor environments, many localization techniques have been investigated subject to low-cost, rapid system deployment, and competitive location accuracy over the past two decades. In this paper, we propose an environment-aware tracking scheme to solve the tracking problem on smartphones in indoor environments, when Bluetooth Low Energy (BLE) beacons are deployed. In contrast to the conventional BLE proximity method, we make following three features in our tracking scheme. First, the influence of complex non-light-of-sight effects on the BLE received signal strengths in the indoor environments have been analyzed. Second, we adopt an optimal deployment strategy in indoor building, then modify BLE signals based on a probabilistic classification model of environments. Third, with the prevalence of smartphones, we extract smartphone-inertial measurement unit along with BLE readings to learn user’s behavior preferences and input to tracking algorithm. With this approach, we can increase the localization accuracy while reducing human cost when constructing indoor fingerprint database for anonymous buildings using smartphones. Experiment results shows that our proposed method is superior to the conventional works.

Beacons turn the physical world into a new digital channel. They bring digital content and engagement closer to the point of purchase. Beacon technology transmits small packets of data using Bluetooth Low Energy. When a customer’s phone “hears” the beacon it knows how close it is and we can then send a message, a notice, a piece of media or other information. Beacons are low-power transmitters equipped with Bluetooth Low Energy or BLE. Bluetooth Low Energy help mobile devices to detect Proximity and determine micro-location. Advantages of Bluetooth low energy devices are it works up to 100 meters, doesn’t drain the phone battery.In this paper problems with beacon are described and path navigation using Bluetooth Low Keywords: Beacon , Internet of Things – Beacons The New Revolution, Bluetooth Low Energy, Path Navigation, Background Monitoring, Ranging Beacons, Proximity, Tracking

The Bluetooth Low Energy (BLE) is an attractive solution for implementing low-cost, low power consumption, short-range wireless transmission technology and high flexibility wireless products, which working on standard coin-cell batteries for years. The original design of BLE is restricted to star topology networking, which limits network coverage and scalability. In contrast, other competing technologies like Wi-Fi and ZigBee overcome those constraints by supporting different topologies such as the tree and mesh network topologies. This paper presents a part of the researchers’ efforts in designing solutions to enable BLE mesh networks and implements a tree network topology which is not supported in the standard BLE specifications. In addition, it discusses the advantages and drawbacks of the existing BLE network solutions. During analyzing the existing solutions, we highlight currently open issues such as flooding-based and routing-based solutions to allow end-to-end data transmission in a BLE mesh network and connecting BLE devices to the internet to support the Internet of Things (IoT). The approach proposed in this paper combines the default BLE star topology with the flooding based mesh topology to create a new hybrid network topology. The proposed approach can extend the network coverage without using any routing protocol.

Received Signal Strength Indicator (RSSI) is the measurement of the power in the radio signal and a parameter for distance‐based measurements. Bluetooth Low Energy (BLE) is an advance implementation for Internet of Things (IoT). BLE beacons‐based indoor positioning systems provide an easy and energy‐efficient, low deployment cost solution for wide variety of applications including smart phones. This paper presents an in‐depth experimental study of BLE RSSI in a dense indoor environment. Due to noise, fluctuations in RSSI occur, which produces distance estimation error, which ultimately affect position estimation accuracy. The main objective of this paper is to know the variations in RSSI and develop a radio propagation model in order to minimize the distance estimation and position estimation error. Based on our real‐time experimental analysis using BLE modules, there is an average 1.32‐m position estimation error in the presence of 10‐dBm variation in RSSI. Moreover, we also observed that environmental specific radio propagation constants greatly affect distance and position estimation accuracy in BLE modules.

Bluetooth low energy (BLE) is a promising candidate technology for use in the Internet of Things (IoT) because of its ultra-low-power communication. Although BLE devices are designed to run on a small battery for a few years, several attempts have been made to extend BLE lifetime through various techniques. In particular, emerging approaches such as artificial intelligence (AI) can be utilized to further improve the BLE energy efficiency. For this purpose, this article proposes a Q-learning-based scheduling algorithm for BLE. The proposed scheduling algorithm dynamically adjusts the key parameters that govern the operation of the BLE transmission scheme. These key parameters, namely, the length of connection interval and the number of packets to transmit during the interval, have a profound effect on energy efficiency and the quality of service (QoS) specified in terms of maximum latency. According to the framework of reinforcement learning, our Q-learning-based scheduling algorithm is appropriately constructed to simultaneously provide a longer network lifetime and satisfy the QoS requirement. The numerical results show that the proposed Q-learning-based approach significantly increases the network lifetime compared to alternative methods while meeting QoS requirements.