Memoryless Techniques and Wireless Technologies for Indoor Localization With the Internet of Things

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.

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.

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.

Optical Camera Communication (OCC) is an emerging technology that has attracted research interest in recent decades. Unlike previous communication technologies, OCC uses visible light as the medium to transmit data from receivers and cameras to receive the data. OCC has several advantages that can be capitalized in several implementations. However, the Internet of Things (IoT) has emerged as a technology with immense potential. Numerous research endeavors support the IoT’s prospective technology that can be implemented in various sectors, including the healthcare system. This study introduces a novel implementation of the Internet of Medical Things (IoMT) system, using OCC for real-time health monitoring and indoor location tracking. The innovative system uses standard closed-circuit television CCTV setups, integrating deep learning-based OCC to monitor multiple patients simultaneously, each represented by an LED matrix. The effectiveness of the system was demonstrated through two scenarios: the first involves dual transmitters and a single camera, highlighting real-time monitoring of vital health data; the second features a transmitter with dual cameras, focusing patient movement tracking across different camera fields of view. To accurately locate and track the position of LED arrays in the camera, the system used YOLO (You Only Look Once). Data are securely transmitted to an edge server and stored using the REST API, with a web interface providing real-time patient updates. This study highlights the potential of OCC in IoMT for advanced patient care and proposes future exploration in larger healthcare systems and other IoT domains.

Location Based Services (LBS) and Proximity Based Services (PBS) can play an important role in our daily life by simplifying tasks. Functions such as turning on and off lights can occur automatically or locking and unlocking doors can be done using LBS. By knowing the location of a user, appliances can be automated to function once the user is near them. Through the use of indoor localization, a user's position can be calculated. When designing an indoor localization system the density of transmitters plays an important role in maximizing the accuracy obtained. Increasing the number of references can improve the accuracy by providing additional information that the system can use in calculating a location. However, placing too many transmitters in the area can create interference in signals and negatively impact the localization results, while not having enough transmitters will hinder localization as not enough information is available. In this paper, we examine the optimal number of Bluetooth Low Energy (BLE) beacons to be used for indoor localization to optimize localization accuracy. Two algorithms were compared: trilateration and nonlinear least squares applying two types of filtering: moving average, and Kalman. Nine different types of systems were developed and compared in terms of accuracy and precision. According to experimental results placing six beacons in an environment will produce the optimal results. Using a nonlinear least squares algorithm with the three closest references with a moving average filter produced the lowest error of 1.149 meters with a standard deviation of 0.698 meters.

In the Internet of thing (IoT), with the geographic location of geospatial sensor data and the global positioning systems, location-based services (LBSs) can provide powerful location-aware IoT applications for mobile clients according to their current locations. For LBSs, a k-nearest neighbor (kNN) search can provide a mobile client with geospatial sensor data of k-nearest spatial points of interest (POIs) according to its current location. In this paper, we propose a spatial air index with neighbor information to organize IoT geospatial sensor data for processing kNN searches in the wireless broadcast systems. Since the answered POIs may be neighbors of each other, we add neighbor information to the index structure, which is interleaved with geospatial sensor data, to speed up the query processing. To avoid unnecessary examination of geospatial sensor data from the wireless channel, the proposed method provides the centroid of geospatial data and the corresponding longest distance between the centroid and geospatial data in the region. With this information, the query processing of a kNN search can quickly determine whether to skip examining this region, saving energy consumption of the mobile device. Performance evaluations have verified that the proposed method outperforms the distributed spatial index.

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.

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.

The advent of Bluetooth Low Energy (BLE) enabled Beacons is poised to revolutionize the indoor contextual aware services to the users. Due to the lower energy consumption and higher throughput, BLE could therefore be an integral pillar of an Internet of Things (IoT) Location Based Service (LBS). Tracking a user with high accuracy is known as Micro-Location. This is a requirement of many IoT user-centric applications for indoor environments. Although several technologies have been used for tracking purposes, the accuracy has always been a serious issue. At the same time, each vendor would install different technologies. In this work, we propose to use the cutting edge and commercially available Apple's iBeacon protocol and iBeacon BLE sensors for micro-location. We propose to leverage a control theoretic approach, namely particle filtering, in order to increase the tracking accuracy in an indoor environment. We performed extensive experiments and our results show that the proposed beacon based micro-location system can be used to locate a user in an indoor environment with an error as low as 0.27 meters.

Indoor Positioning Systems (IPS) are an alternative to Global Positioning System (GPS) in those environments where its signal is attenuated. This is one of the main reasons why IPS has been the subject of much research over the last two decades and where different technologies and methods have been used. Among the technologies used in IPS are those that use radio frequency (RF) signals, such as Bluetooth Low Energy (BLE) or Wi-Fi. BLE is widely used in ubiquitous computing and in many applications of the Internet of Things (IoT) mainly due to its low power consumption and because it can provide advanced services to users. The aim of this paper is to provide an overview of the state of the art of BLE-based IPS including its main methods and algorithms.

IoT or The Internet of Things is the latest modern-day technology that is currently transforming the society in which we live in. Various opportunities in IoT have increased the demand for the need of IoT enabled technologies to a great extent. Bluetooth Low Energy (BLE) is one such technology which helped advance the scope of IoT enabled technologies in smart cities and various other corporate organizations. Asset tracking, person location tracking are some of the key use cases of the BLE technology. Hence integration of BLE technology has helped IoT to reach its technological advancement to a next level. With the advancements in technology, many researches in services like indoor localization had been carried out. Indoor localization is that ability of technology to actually find the exact location of a person or device existing in an indoor setting. So this technology feature have been accepted and put into use in sectors like industries, building management, health sectors and so on. Hence this technology is now integrated into latest smart cities so as to maximize the utilization of the technology to the fullest. Security is one major concern with the emergence of a technology. Many algorithms or protocols help to curb the security threats on a major scale. In this paper we had also discuss on some security issues faced in IoT system to know how effectively and carefully one should implement and design an IoT system to ensure that there are no security flaws. This paper focuses on applications of IoT and BLE technologies with some of its security threats like Denial of Service (DoS) attacks with a proposal for improvement in boarding passes issued to passengers in airports that enable passenger tracking possible in airports around the world. Section I consists of the introduction which describes the brief idea on the paper followed by Section II which is the literature review which consists of applications, security and other various aspects of IoT and BLE systems. Section III is a proposal for improved boarding pass system and in the end is Section IV which concludes the paper with a detailed conclusion.

Internet of Things (IoT) extends the connectivity of internet connected devices like laptops, smartphones with internet-enabled embedded devices using wireless communication technology and sensing devices to interact with the external world. IoT based devices mainly use wireless technology like WiFi, Zigbee, Bluetooth Low Energy (BLE) for communication. Due to the large number of IoT devices and limited spectrum in the Radio Frequency band the system suffers from bandwidth crunch. Hence, the high bandwidth capacity of visible light provides the solution to the spectral crisis. So, the visible light based new wireless technology opens up a new domain of integrated communication system with sensors using lights. The LED beams transmit information from the transmitter's side to the photo-detector on the receiver's side. But, the photo-detector is affected by fluctuations in the brightness of ambient light and also by the variations in the distance between transmitter and receiver.

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.

Internet of Things (IoT) applications can offer a great potential of benefits to end users. We are moving towards an Internet of People, Things and Services (IoPTS) and a future world of ambient intelligence, where users can seamlessly interact with IoT devices and get information about the services they offer. Interaction can take place directly or through dedicated mobile apps that are built to acquire data from sensors or clusters of IoT devices. The article presents a user-centric cluster framework, taking into account the resource-constrained nature of the IoT devices. As part of the framework, the authors propose a Mobility Aware Clustering Scheme (MACS), which organizes the IoT devices in clusters using a fitness function for each node, and a Profile Aware Proactive Cluster Discovery (PAPCD) mechanism using Bluetooth Low Energy (BLE) beacons for service advertisement. The authors also propose an IoT service filtering and ranking algorithm to match the users' preferences and requirements

A key requirements in the Internet of Things (IoT) concept are context-aware computation, smart connectivity with existing networks and cost efficient low-power wireless solutions. Bluetooth Low Energy (BLE) is one of the latest developments of IoT and especially well-suited for ultra-low power sensors running on small batteries. BLE is successful alternative for indoor positioning systems (IPS) which offers reasonable accuracy and low cost deployment. This paper presents a comparative analysis of contemporary BLE indoor positioning solutions, taking into account the classification, comparison and variety considerations of parameters that are required for designing a new indoor positioning approaches.