Bluetooth Low Energy for Autonomous Human-Robot Interaction

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.

This paper proposes a new indoor positioning algorithm for Bluetooth Low Energy (BLE) devices, such as mobile phones and tablets. The algorithm integrates an Off-set triangulation algorithm and a multi-stage weighted framework. An off-set triangulation algorithm was used to provide a general method to locate the user’s position. Results are integrated into a weighted framework in order to increase the adaptability of the proposed localization algorithm to different complex environments and increase the accuracy of the localization. Three experiments in different environments have been examined in order to test the performance and the limitations of this algorithm. Results show that the proposed algorithm demonstrated strong adaptability to complex non-Line-of-Sight (LOS) environments.

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.

This paper investigates the possibility to develop a Bluetooth Low Energy (BLE) device as part of Internet of Things chain. For this purpose, both the hardware and the software aspects are considered. Thus, the BLE software stack from Texas Instruments and the CC2541 platform were chosen. Considering the CC2541 Keyfob device which contains an accelerometer, new facilities are firstly introduced by modifying the existing embedded software. Then, a new hardware component is connected, and the necessary software components are included in order to use this new element. The two main applications that are employed to monitor the activity of the BLE devices are analyzed and a new one based on MATLAB is introduced to improve the facilities of the existing applications. Thus, using the ideas presented in this paper, new BLE devices can be developed.

With the rapid development of Internet of Things (IoT) technologies, Cross-Technology Communication (CTC) has become a key method for establishing direct links between heterogeneous wireless systems. Existing CTC approaches typically rely on emulating the waveform of the target signal; however, such emulation often faces precision limitations due to bandwidth differences, mismatched modulation schemes, and hardware non-idealities. This paper proposes WiBlue, a CTC scheme that enables direct wireless data transmission from Wi-Fi devices to Bluetooth Low Energy (BLE) devices. The approach is based on the IEEE 802.11n physical layer and uses cyclic prefix (CP) processing, quadrature amplitude modulation (QAM), and frequency compensation mechanisms to achieve efficient compatibility across heterogeneous physical layers. WiBlue has been implemented on the USRP B210 platform with commercial BLE devices. Experimental results show that WiBlue achieves high reliability in various indoor environments, maintaining a packet reception rate above 96 % and a symbol error rate below 0.2, demonstrating its practicality and stability for heterogeneous wireless communication.

The rapid and increasing application of wireless technology, especially Bluetooth Low Energy (BLE), provides many benefits to our daily lives. With BLE, the small packets of data can be exchanged over a wireless channel, whereas the data security is a concern. For example, the BLE standards define that if one BLE device with input-output capabilities tries to establish a connection while the other BLE device does not support input-output, an unauthenticated channel will be established. That is, in the worst case, the connection is established where no security is applied. To mitigate this issue, we propose a security protocol, named BALSAPro, at the application layer that complements the existing protocols such as Security Manager Protocol (SMP), which can help establish a secure channel with authentication and non-repudiation, when pairing has not been used. In the evaluation, we implement and evaluate our protocol performance compared with similar work at the application layer. The results demonstrate that our protocol can enhance the security level with less time consumption.

With the technical advances of wireless sensor networking and smart mobile device, the demand for position information of pedestrians (especially in the indoor environment) has increased remarkably. In this paper, we proposed an indoor localization approach based on received signal strength (RSS) and Bayesian filter. In the following sections, we describe our virtual modeling method of environment and the way we take object's movement sequences as history conditions in Bayesian filter. The experiment results show that our solution provides accurate tracking results (within 80 centimeters for moving object). The contribution of this research is that it provides a general implementation utilizing Bayesian filter which is able to estimate location precisely with off-the-shelf hardware. And Bluetooth Low Energy (BLE) is employed which reduces power consumption considerably. Meanwhile the accuracy is sufficient for pedestrian tracking in real application scenarios where BLE devices can be easily deployed. Simply connecting static points will distort the real trajectory. One solution for this problem is filter technique. Filter technique is not only can be used in signal measurements, but also on general position estimations. In this paper we propose a Bluetooth indoor pedestrian tracking system which employs RSS as scene profiling basis. During the tracking process, our system keeps iterating a recursion of prediction and update in which Bayes filter (4) is the mathematical fundamental. Other than the classic procedure of filtering the measurement data only, our filter integrates context information and history record as conditions for the estimation. Our evaluation shows that our combination of current measurements with history records increases both the accuracy and efficiency. The rest of this paper is organized as follows: Section II overviews fundamental and essential related works. Then Section III describes detailed implementation. After that in Section IV we present an evaluation of the proposed algorithm by comparing it with existing RSS-based fingerprint approaches. Finally, Section VI summarizes the main conclusions and gives directions for future work.

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.

This paper presents a detailed investigation into off-body links for wrist mounted Bluetooth Low Energy (BLE) devices for an indoor environment and revolves around three low profile antennas (microstrip patch, printed monopole and external monopole). It studies the received signal strength across different usage scenarios and obstructions such as body shadow, wall and ceiling through measurement campaigns carried out in a 2-storey house. It is observed that RSSIs show little variation from the mean value at any position in a given room of the house. In the living room, the average RSSI for patch, printed and external monopole is close to -61,-73 and -61 dBm respectively. The study describes the effect of usual obstructions such as wall and ceiling to off-body links and shows that the range of RSSI for any antenna (30 dBm at P=0.5 for Patch Antenna) is bounded at one end by the CDF of wall plus body shadow and at the other end by CDF of Line of Sight (LOS). The effect of body shadowing is studied in greater detail. It is observed that the microstrip patch antenna in LOS, under a ceiling and behind a wall, drops by approximately 15dB, 5dB and 10dB at P=0.4 when it goes into body shadow. The study also suggests that the Access Point (AP) on the ceiling provides better off-body links as compared to it in the adjacent room. The study compares the overall performance of the three antennas across all possible usage scenarios, obstructions & polarizations and concludes that the external monopole and patch antenna performs better than printed monopole by approximately 10-13 dBm. Finally, the study shows the variation of Packet Error Rate (PER) with RSSI and determines threshold values of RSSI for acceptable PERs, for reliable and robust applications.

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.

In this paper, we propose an indoor positioning system based on Bluetooth low energy (BLE), and the localization tag is with characteristics of low power, low cost, and high portability. The proposed system consists of BLE tags, BLE/WiFi repeaters, and a fusion server. The BLE tag is a device which broadcasts BLE beacons. The BLE/WiFi repeaters collect the beacons transmitted from the tag and extract the received signal strength (RSS). The RSS values are then transmitted to the fusion server through WiFi, and the server will estimate the position of the BLE tag with RSS-based positioning algorithm. We propose an indoor positioning algorithm which is a hybrid from received signal strength indication (RSSI)-fingerprint and cell of origin (CoO) to estimate the BLE tag's location. Some modifications are made to typical RSSI-fingerprint and CoO algorithm to get better accuracy. The test results show 1.2 m to 1.4 m positioning accuracy. The size of the BLE tag is 1.7 cm in radius and 0.5 cm thick. Each BLE tag costs 3 US dollars. The current consumption of the tag is 50 μA which can be used without charge for 136 days with a CR2025 battery.

Bluetooth Low Energy (BLE) is a popular low power wireless technology, used for short-range communication. In BLE networks, discovery process enables fast and energy-efficient communication, since all communications must involve it at the first step. For this reason, some studies have proposed novel discovery schemes to improve the performance of device discovery process. Nevertheless, literature report that provides a consolidated view are limited and insufficient. In this paper, we survey different device discovery schemes in BLE networks and discuss their advantages and limitations. The enhanced schemes are categorized into two approaches: parameter adjustment and collision avoidance approach. The first approach improves the performance by adjusting the discovery parameters according to a network situation. In the second approach, the BLE devices try to avoid the interference to reduce collisions in crowded networks.

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.

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>.

In the paper we focus on the problem of improving the efficiency of the peer-to-peer connection establishment for the recently suggested Bluetooth Low Energy (BLE) protocol. We suggest a novel Accelerated Connection Establishment (ACE) mechanism which enables to significantly reduce the time and energy consumption for establishing the connection between BLE devices in the multi-node scenarios. We discuss in details the background of the problem, introduce the suggested solution, and present the results of BLE network simulations, which confirm the efficiency of the suggested mechanism.