Experimental Demonstration for Indoor Localization Based on AoA of Bluetooth 5.1 Using Software Defined Radio

Future telecommunications aim to be ubiquitous and efficient, as widely deployed connectivity will allow for a variety of edge/fog based services. Challenges are numerous, e.g., spectrum overuse, energy efficiency, latency and bandwidth, battery life and computing power of edge devices. Addressing these challenges is key to compose the backbone for the future Internet-of-Things (IoT). Among IoT applications are Indoor Positioning System and indoor Real-Time-Location-Systems systems, which are needed where GPS is unviable. The Bluetooth Low Energy (BLE) 5.1 specification introduced Direction Finding to the protocol, allowing for BLE devices with antenna arrays to derive the Angle-of-Arrival (AoA) of transmissions. Well known algorithms for AoA calculation are computationally demanding, so recent works have addressed this, since the low-cost of BLE devices may provide efficient solutions for indoor localization. In this paper, we present a system topology and algorithms for self-localization where a receiver with an antenna array utilizes the AoAs from fixed battery powered beacons to self-localize, without a centralized system or wall-power infrastructure. We conduct two main experiments using a BLE receiver of our own design. Firstly, we validate the expected behaviour in an anechoic chamber, computing the AoA with an RMSE of <inline-formula> <tex-math notation="LaTeX">$\mathrm {10.7~ ^{circ}}$ </tex-math></inline-formula>. Secondly, we conduct a test in an outdoor area of 12 by 12 meters using four beacons, and present pre-processing steps prior to computing the AoAs, followed by position estimations achieving a mean absolute error of <inline-formula> <tex-math notation="LaTeX">$\mathrm {3.6~ \text {m}}$ </tex-math></inline-formula> for 21 map positions, with a minimum as low as <inline-formula> <tex-math notation="LaTeX">$\mathrm {1.1~ \text {m}}$ </tex-math></inline-formula>.

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.

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.

Internet of Things (IoT) applications are generally developed in a vertical manner, i.e., each IoT application is developed for a certain scenario which generally does not share data with IoT applications for recommendation services. This leads to an inefficient exploitation of other IoT service applications. In fact, such recommendation services can be achieved with the help of Social IoT (SIoT) by using data generated by various IoT applications. SIoT builds a profile of objects based on IoT applications data that can be exchanged with SIoT network for accessible to other IoT applications. In this manner, the SIoT network provides recommendation services for reusability of IoT applications' data among various IoT applications, as well as adapting IoT services according to users' needs which improve user experience. Additionally, the profiles built by a SIoT network can also help a single IoT application by looking for similar conditions that have been addressed in the past for the same IoT application. We propose a concept for exploiting the SIoT for recommendation services among various IoT applications with the help of a sample application scenario that highlights how the SIoT can help by providing recommendations. We also provide some implementation challenges for this concept.

Due to extremely low power consumption, backscatter is promising to become general-purpose communication for Internet of Things (IoT) networks. The state-of-the-art BLE backscatter system RBLE can work fully with commodity Bluetooth Low Energy (BLE) devices, however, the communication reliability of RBLE is still limited by its modulation scheme based on Binary Frequency Shift Keying (BFSK). To address this issue, we refer to the modulation of active BLE radios to improve the modulation scheme of the backscatter tag. We present PBLE, a practical BLE backscatter communication system that modulates using phase shift. We conduct experiments to verify the feasibility and efficacy. Evaluation results demonstrate the feasibility and performance gains over RBLE in terms of bit error rate (BER). With PBLE, it is possible for us to reliably read the sensor data of tags using daily BLE devices.

The correct implementation and behavior of Internet of Things (IoT) applications are seldom investigated in the literature. This paper shows how the simulation mechanism can be integrated well into an IoT application development platform for correct implementation and behavior investigation. We use an IoT application development platform called IoTtalk as an example to describe how the simulation mechanism called SimTalk can be built into this IoT platform. We first elaborate on how to implement the simulator for an input IoT device (a sensor). Then we describe how an output IoT device (an actuator) can be simulated by an animated simulator. We use a smart farm application to show how the simulated sensors are used for correct implementation. We use applications including interactive art (skeleton art and water dance) and the pendulum physics experiment as examples to illustrate how IoT application behavior investigation can be achieved in SimTalk. As the main outcome of this paper, the SimTalk simulation codes can be directly reused for real IoT applications. Furthermore, SimTalk is integrated well with an IoT application verification tool in order to formally verify the IoT application configuration. Such features have not been found in any IoT simulators in the world.

The ultimate objective of Software Defined Radio (SDR) is to replace the entire analog signal processing in the wireless transceivers with digital signal processing. This will provide the flexibility, through reconfiguration or reprogramming, to enable the transceivers to work with different air-interfaces standards using a single generic hardware platform. Most of the research in SDR to date has been focused on base stations, which do not have as tight constraints in size and power consumption as handsets. However, in order to realize the promise of integrated services and global roaming capabilities, the design and development of a multi-standard SDR handset with dynamic reconfigurability is crucial. The size and power constraints imposed by handsets with limited resources pose a major design challenge. This special issue brings together 11 papers that address various aspects of this challenge from the perspectives of wireless systems, computing architectures, embedded systems and signal processing. The most important part of an SDR transceiver is the wideband analog-to-digital converter (ADC) that converts the radio frequency signal received by the antenna into digital form for further processing. The first paper, “A multi-mode sigma-delta ADC for GSM/WCDMA/WLAN applications” by Jose et. al. proposed a cascaded reconfigurable sigma-delta modulator that is able to work with three different communication standards. This design, implemented in 0.18 μm CMOS technology, exhibits excellent signal-to-noise plus distortion ratios and has reduced power consumption compared to conventional designs. After ADC, the digitized received signal will need to be separated into signals within the various bands specified by the communication standard. This is performed by the channelizer which has the highest computational requirements of the whole SDR system. The second paper, by Panicker et al., reviews the state-of-the-art filter bank channelizer techniques with low power and dynamic reconfigurability properties. The design of reconfigurable devices can be optimized using the methodology of parametrization which bring together the common aspects of a myriad number of wireless standards into a single processing procedure. In this way, the implementation of the multi-standard SDR will be greatly simplified. “Promising technique of parametrisation for reconfigurable radio, the Common Operators Technique: fundamentals and examples” by Alaus et al. presents the common operator technique for parametrization which is less standards dependent than the more conventional Common Function technique. The direct digitization of the radio frequency (RF) signal requires very high speed ADCs which is not readily available using current technology. Therefore, current SDR implementations typically down-converts the RF A. P. Vinod (*) School of Computer Engineering, Nanyang Technological University, Nanyang, Singapore e-mail: asvinod@ntu.edu.sg

As a major agricultural province in China, Shandong Province has always played an important role. At the same time, it is also closely related to the strong support of science and technology. As a new technology, the Internet of things (IoT) has made positive contributions. Agricultural IoT is a mode that uses information technology, wireless communication technology and radio frequency automatic identification technology to share the identified agricultural information to the network. The application of agricultural IoT is a major change in agricultural development, which provides convenient conditions for the development of agriculture, provides a favorable environment for the agricultural products, promotes the transformation of agricultural technology, realizes the informatization of agricultural production, improves the efficiency of rural land, integrates agricultural resources and provides a foundation for food safety. In this paper, the application of IoT in the field of agriculture in Shandong Province is described from two practical cases. One is about how to use wireless sensor network technology to transmit data in the big data platform based on the Bohai granary project. The other describes how to use IoT for food quality and safety traceability. It takes pigs as the research object, runs through all links of the whole industrial chain of pig breeding, breeding, slaughtering, processing, logistics and sales, collects the data of the whole industrial chain with the help of IoT, and realizes the information tracking and traceability of the whole industrial chain. At the end of the paper, it discusses the countermeasures.

Recently, backscatter technology has attracted much interest in wireless communication due to its novel low-cost and battery-free design. Since Bluetooth Low Energy (BLE) was born to be low energy consumption, great efforts have been made in BLE-based backscatter systems, like FreeRider, RBLE. In this paper, we present BonusBlue, a BLE backscatter system that enables high-goodput communication with commercial BLE devices. BonusBlue tag generates BLE packets by modulating data on excitation signals and set up a data connection with BLE receiver using a state machine, thus achieving a high-goodput communication link. We present this state machine design and build a prototype of our tag using an FPGA, and evaluate its performance with BLE devices. Our evaluation shows that the backscatter tag can build a robust data connection link with commodity BLE device in the guidance of our state machine and transmit tag data on this link. Experimental results show that our backscatter system can achieve a goodput of up to 16.9 kbps.

The internet of things (IoT) is focused in offering new technologies which is changing the business environment by showing the potential in different sectors like retail, transportation, healthcare etc. Through IoT, the managers can gather and process data and information in an effective manner, also they can use them to automate in order to enhance productivity and efficiency. With the usage of such technologies, human resource managers can access information for possible candidates which can be accessed through IoT. The overall behaviour of potential candidate tends to help them in shortlisting and choosing the desirable ones. The application of IoT has emerged in enhancing the recruitment of candidates in search of candidates based on large volume of information, assist in screening the candidates, selecting the most suitable ones. The application of IoT technology can modify the HR roles, enable in changing the company perspective in enhancing recruitment process, source the candidates effectively and select them so as to meet the organisational requirements. The implementation of IoT driven technologies support in removing the biases in screening the candidates and aids in candidate selection with the support of other technologies like artificial intelligence, predictive analytics etc. Furthermore, IoT is also involved in better inventory management as it supports the organisation in converting the data which are sourced through RFID (Radio Frequency Identification) into more insights related to inventory data, location of the products, tracking the movement of goods until it reaches the hands of the customers. With the application of IoT inventory managers possess better control in managing the inventory through automated ordering request and thereby reduce manual time and cost for the business. This paper is focused in presenting a conceptual understanding on the application of environmental IoT in recruitment process and also the overall impact created by the technology in managing the inventory efficiently.KeywordsInternet of thingsRecruitment processInventory managementPredictive analytics

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.

Chapter 13 - WoX: Model-Driven Development of Web of Things Applications

A technique to estimate an incoming radio frequency (RF) signal angle of arrival (AOA) irrespective of the RF signal amplitude is presented. It is based on measuring the DC voltage at the output of two modules where each of them consists of an optical modulator, an optical filter and a photodetector. An RF signal amplitude‐independent AOA can be obtained from the ratio of the two DC voltages. Experimental results are presented that demonstrate 0°–63° AOA measurement with &lt;3° errors for an RF signal with three different power levels of 3.2, −0.3 and −3.6 dBm into the modulator.

Tracking objects in indoor is always a challenge for a variety of Internet of Things (IoT) applications. Nowadays, many indoor tracking applications use Low-Power Wide-Area Networks (LPWAN) for Machine-to-Machine (M2M) communication. LoRa is a promising LPWAN radio communication technology designed for wide-area coverage and low-power embedded IoT devices. The objective of this paper is to evaluate the performance of the Angle of Arrival (AoA) direction finding approach in an indoor environment. The AoA performance of the LoRa signal is evaluated using different modulation settings, including Channel Bandwidth (BW) and spreading factor (SF). We measure the AoA accuracy of the LoRa signal using an individual Universal Software Radio Peripheral (USRP) B210, Software Defined Radio (SDR) receiver with the help of the GNU radio software development toolkit. In addition, a new approach to measure the AoA of LoRa signal under interference is investigated. We show that by using the Autocorrelation function combined with our direction finding algorithm, the detection and measurement of two simultaneous receptions is possible. The entire experimental setup was implemented in our indoor office environment.

Under the background of prosperity of Internet of things (IoT), in order to accelerate the training of talented persons of China for IoT, we propose to design and develop a new course for Chinese students: Essential Technologies, Applications, and Commercial Patterns of Internet of Things. Aiming at three-dimensional target, which has dimensionalities of attitude and thoughts, knowledge and skills, process and methods, the contents of the course include the following parts: system and application architectures, key technologies of IoT, applications of IoT, commercial patterns of IoT, IoT industry and social significance etc. The course highlights the training of abilities of innovation, creativity, comprehensive analysis and practical operations. The construction of teaching resources, such as textbooks, lecture notes, teaching equipments, practice bases, are also emphasized in the process of development of this course.