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Balancing Bluetooth Throughput and Reliability in Interference-Rich Environments

There’s an interesting new paper titled Modeling the Trade-off between Throughput and Reliability in a Bluetooth Low Energy Connection that provides a comprehensive analysis of the performance of Bluetooth Low Energy (BLE) communication in terms of throughput and reliability under various interference conditions.

The primary objective of the study was to develop and validate mathematical models that predict the throughput and reliability of BLE connections under interference.

Two models were developed, a Throughput Model using a Markov chain approach to predict the throughput of BLE connections under interference, and a Reliability Model that quantified the reliability of BLE connections by considering various transmission parameters and interference levels.

The throughput model was validated through extensive practical experiments under different interference scenarios. The experiments involved varying parameters such as packet length, number of packets, and connection intervals. The results showed a close match between the theoretical predictions and the experimental data, highlighting the accuracy of the models.

As might be expected, the study found that the interference level in the environment significantly affects both throughput and reliability. Higher interference levels (higher BER) reduce both metrics.

There is a non-linear relationship between payload size and throughput. While larger payload sizes can increase throughput in low-interference environments, they significantly reduce reliability and throughput in high-interference conditions.

Increasing the connection interval improves energy efficiency but reduces throughput without affecting reliability. This suggests that connection interval adjustments can optimise energy usage without compromising communication reliability.

Bluetooth devices should be configured based on the specific interference environment they will operate in. For instance, smaller payload sizes are preferable in high-interference environments to maintain reliability.

De-risking Bluetooth Projects

Many projects encounter insurmountable problems that ought to have been identified before the initiation phase. For example, some make commitments to specific hardware, which can significantly impede future development or result in large unexpected costs.

This lack of foresight and planning, referred to as ‘unknown unknowns’, can lead to project failure or necessitate unwelcome changes in course. Savvy founders, on the other hand, seek advice from experts to lessen the risk of being blindsided.

Another common problem is compatibility issues can occur between various manufacturers or versions. This discrepancy can give rise to unforeseen development challenges or affect user experience post-launch. Another issue is the achievable range of Bluetooth that can be heavily influenced by real-world conditions. Elements such as walls, the presence of other wireless devices and even atmospheric conditions can significantly limit Bluetooth’s effective range, potentially undermining the overall functionality of your project.

Although Bluetooth is praised for its energy efficiency, the actual power consumption can substantially deviate based on factors such as signal strength, data rate and connection interval. Misunderstanding or failing to anticipate these factors can lead to unanticipated issues with battery life in the final product.

A preliminary study can help avert costly and humiliating errors. At BeaconZone, we evaluate the feasibility of your project and provide guidance on both software and hardware options. We are here to answer your queries, highlight potential barriers and bring to light issues you may have overlooked.

By choosing not to go it alone, you can avoid mistakes that would otherwise occur. You’ll gain insight into any pragmatic decisions that may need to be taken. We can provide accurate cost and time estimates for implementation, ensuring that you purchase the most suitable hardware.

Our services also help to guard against getting locked into platforms with uncertain future costs and risks. Learning from the anonymous mistakes of our past clients can provide you with valuable insights. Moreover, we can expediently integrate Bluetooth knowledge into your organisation, giving you a head start on future developments.

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Using Covert Channels with iBeacon

A new study Implementation and Analysis of Covert Channel Using iBeacon (PDF) explores the creation and analysis of covert communication channels using iBeacon, which is based on Bluetooth Low Energy (BLE). Covert channels are methods used to transmit information secretly, bypassing normal security measures.

The authors introduce two types of covert channels: one that uses the payload of the iBeacon broadcast messages and another that employs the broadcasting intervals. The payload-based covert channel modifies the UUID, Major, Minor, and TX power fields of the iBeacon packets to transmit covert messages. This method achieved a maximum throughput of 911,600 Bytes per second (Bps) with a Packet Delivery Rate (PDR) consistently above 75%, indicating its efficiency in transmitting substantial data covertly.

The interval-based covert channel, on the other hand, encodes messages in the time intervals between consecutive iBeacon broadcasts. Although this method provides higher concealment compared to payload-based channels, it has a lower channel capacity and can cause transmission delays.

The experimental setup involved using Raspberry Pi devices to simulate the transmission and reception of iBeacon packets, where various advertising intervals were tested. The findings highlighted that shorter advertising intervals resulted in higher throughput, with the best performance observed in the 100–200 ms range.

The study concludes by emphasising the potential for significant data transmission through BLE beacons and suggests future research to explore countermeasures against such covert channels.

iBeacon vs Beacon: Understanding the Difference

The term ‘Beacon’ is a generic name for all types of devices that use standard Bluetooth to transmit signals. Among these, iBeacon is the most popular and widely recognised.

Beacons: The Broad Category

Beacons are small, wireless transmitters that use Bluetooth Low Energy (BLE) technology to send signals to nearby devices. These signals can trigger actions, such as sending notifications, providing navigation or tracking assets. The technology is simple yet powerful, enabling a myriad of applications across various industries, from retail to healthcare.

The term ‘Beacon’ encompasses a variety of beacon types, each with its unique specifications and use cases. These include Eddystone, AltBeacon, and, of course, iBeacon. Despite their differences, all beacons share the fundamental ability to transmit data using Bluetooth, making them interoperable with any Bluetooth-enabled device that scans for such signals.

iBeacon: Apple’s Contribution to Beacon Technology

Among the different types of beacons, iBeacon is perhaps the most well-known. It’s important to note that while the iBeacon data format was developed by Apple, it can be detected by any device that has Bluetooth scanning capabilities, not just Apple products.

The iBeacon protocol defines a specific data format for Bluetooth advertising. This format includes three main components:

  • UUID (Universally Unique Identifier): A 128-bit value that uniquely identifies the beacon or a group of beacons.
  • Major Value: A 16-bit integer used to group related beacons. For instance, all beacons in a specific retail store might share the same Major value.
  • Minor Value: Another 16-bit integer that allows for more granular identification within a group. This could, for example, differentiate individual beacons within a retail store.

Other Types of Beacons

While iBeacon is the most prominent, several other beacon technologies are worth mentioning:

  • Eddystone: Developed by Google, Eddystone is an open beacon format that supports multiple data frame types. This flexibility allows it to broadcast URLs, telemetry data and other forms of information, making it versatile for various applications.
  • AltBeacon: Created by Radius Networks, AltBeacon is an open and interoperable beacon standard. It aims to provide a flexible alternative to proprietary beacon formats, ensuring compatibility across different platforms and devices.

Inside the iBeacon Data Advertising

The iBeacon Bluetooth packet structure includes the following fields:

  • Preamble: A series of bytes that mark the beginning of the transmission.
  • Access Address: A 32-bit field that identifies the packet as a BLE advertisement.
  • PDU (Protocol Data Unit): Contains the actual iBeacon data, including the UUID, Major, and Minor values.
  • CRC (Cyclic Redundancy Check): Ensures data integrity by checking for errors in the received data.

The brevity of this format allows iBeacons, in fact all beacons, to operate effectively with minimal power consumption, making them suitable for prolonged use in various environments.

Enhancing Indoor Localisation for Ambient Assisted Living

New research Simplified Indoor Localisation Using Bluetooth Beacons and Received Signal Strength (RSSI) Fingerprinting with Smartwatch, introduces an innovative system for indoor localisation using Bluetooth Low Energy beacons and smartwatches, aimed at simplifying the process for users. This system is designed to detect a user’s location within specific areas like rooms within a house, rather than providing exact coordinates, with a particular focus on applications in ambient assisted living, especially for the elderly.

The study presents the methodology, implementation, and evaluation of the system, highlighting its practicality for real-world applications. The system demonstrated high accuracy, achieving 92.1% in environments with five rooms and 85.9% with three rooms, showcasing its effectiveness. The setup process is streamlined to reduce the number of reference points and employs a straightforward nearest neighbour algorithm, which simplifies the use and maintenance for users who may not have extensive technical skills.

The use of common and low-cost hardware components, such as Raspberry Pi for beacons and commercial smartwatches, helps keep the system affordable and simple to manage. Calibration is quick and efficient, which is ideal for residential settings. Despite its current effectiveness, the research suggests there is room for improvement. Future enhancements might include the adoption of multiple reference points per region to refine accuracy, particularly in transitional spaces between rooms.

This system offers a robust solution for indoor localisation with significant implications for healthcare, particularly aiding elderly individuals to live independently while ensuring their safety and mobility within their homes.

Beacons on Cruise Ships

Carnival Cruises is using Ocean Medallion™, a beacon that allows you to board the ship, open your room door, navigate the ship, find your family and friends onboard, make reservations and order and pay for food and drinks.

This is a significant and well thought out rollout for many reasons:

  • Large undertaking – In order to use the system, each ship is fitted with 75 miles (121km) of cables, more than 7,000 sensors and 4,000 digital screens.
  • Mass market promotion – it has even been mentioned on the BBC.
  • First large rollout to use beacons with NFC – As we previously mentioned, NFC can be used for, closer, security-related activities such as payment.
  • Used as a USP – The How It Works web page is using the added convenience as a unique selling point.
  • No battery life problems – The beacon only has to transmit and last as long as the holiday.
  • User Experience aware – The beacon has been designed to look like jewellery to gain acceptance. It’s engraved with the customer’s name and can be worn as a necklace, clip or on a keychain.

The clever part is that the gains aren’t just for Carnival cruise guests. The new system will also allow more personal location information to be gathered that can be used offer better targeted promotions and hence help increase revenue per customer.

Indoor Navigation for Environments with Repetitive Structures

New research looks into indoor navigation systems specifically designed for environments with repetitive structures, such as cruise ships, using Bluetooth low-energy (BLE) beacons without relying on GPS. The system incorporates a mobile application that uses these beacons to guide users accurately within buildings. The system optimises navigation through the use of pre-calculated routes, which minimises data storage requirements and enhances the application’s energy efficiency.

It system includes a sophisticated user interface that displays the route and updates navigation in real-time based on user movement and beacon signal reception. The implementation faced several challenges, particularly related to the synchronisation and real-time processing of beacon signals, which were addressed by optimising the beacon scanning process and the communication between system components.

The study lays the groundwork for future exploration and deployment of indoor navigation systems that leverage repetitive architectural features for enhanced navigation efficiency.

Minew’s New MWC01 and MBT02 Repeater Beacons

Minew has recently launched two new beacon models, the MWC01 and MBT02, a new type of product that is a repeater. These repeaters are designed to scan for beacons and re-transmit the strongest signal, providing a new approach to locating, and a way of extending Bluetooth communication.

The intended use of these repeaters isn’t entirely clear from the Minew website, so here’s an explanation of how they work and their benefits.

Traditional Beacon Systems

In a conventional setup, multiple gateways detect beacons within an area and server-side software uses trilateration to calculate the position of an asset or person. This method often lacks accuracy due to the fluctuating and imprecise nature of Received Signal Strength Indicator (RSSI) measurements.

Bluetooth Direction Finding

Bluetooth Direction Finding was introduced to improve accuracy. It uses the angle of arrival of Bluetooth signals, requiring complex software and specialised hardware. While very effective, this method can be complicated, time consuming and expensive to implement.

The Role of Repeaters

Minew’s new repeaters offer a middle ground between traditional RSSI-based systems and advanced Bluetooth direction finding. Here’s how they work.

Extra fixed Bluetooth beacons are placed throughout the area. The repeaters, attached to moving assets, detect the beacons and send the strongest signal to a single gateway. This reduces the number of gateways needed and allows for the strategic placement of beacons to enhance accuracy where necessary. It is a simpler and more cost-effective solution than Direction Finding implementations.

Additional Uses

Repeater beacons can also function as fixed repeaters to extend the range of a beacon where it’s insufficient. This is particularly useful for sensing and IoT applications, providing extra range and reliability.

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Interested in integrating Minew’s repeater beacons into your solutions? Contact us for more information.

(Not) Using Machine Learning for Bluetooth AoA

New Research, On the Generalization of Deep Learning Models for AoA Estimation in Bluetooth Indoor Scenarios, by Ivan Pisa, Guillem Boquet, Xavier Vilajosana, and Borja Martinez of Universitat Oberta de Catalunya, Barcelona, Spain, looks into the application of Deep Learning (DL) models for Angle-of-Arrival (AoA) estimation, a key technique for indoor positioning in Bluetooth indoor positioning.

Accurate estimation of the Angle-of-Arrival (AoA) is complex. The accuracy of AoA estimates can be significantly affected by various signal disturbances such as multipath components, polarisation, spread delays, jitter and noise. These factors can create ambiguities and distort the phase differences of received signals, leading to errors in the position data reported by the system. Also, the multipath effect, where multiple signal replicas interfere with each other, can severely mislead AoA estimations.

Conventional algorithmic AoA estimation techniques rely heavily on processes that can increase the cost, reduce scalability and complicate the operation of the systems where they are used. A primary requirement is the calibration of the antenna array to obtain its steering vector, a process that ensures accurate directional sensitivity of the antenna system. This calibration, along with other computationally intensive tasks such as matrix inversion and eigenvector decomposition, requires significant computational resources. This can be particularly challenging when these systems need to be scaled up for large deployments.

The study’s main objective was to evaluate and compare the generalisation capabilities of AI machine learning models to traditional signal processing techniques, such as the Multiple Signal Classification (MUSIC) algorithm, across various scenarios including different locator positions, time instant and unfamiliar environments.

The results indicated that while DL models perform well within the environment they are trained in, their ability to generalise to new or altered conditions is notably weaker than that of the MUSIC algorithm. The authors concluded that DL models tend to learn specifics of the training environment rather than generalisable features of the AoA estimation task. This learning limitation hampers their practical application since models trained in one environment perform poorly in another.

Analysing Sensor Data From Bluetooth Beacons Using AI Machine Learning

Analysing sensor data from Bluetooth beacons using machine learning involves several steps, from data collection and preprocessing to model development and deployment. Here’s an overview of the process:

Step 1: Data Collection
The first step in analysing sensor data from Bluetooth beacons is to collect the data. Beacons continuously broadcast data such as unique identifiers, signal strength (RSSI) and sometimes telemetry data like temperature an battery level. Sensor beacons also enable detection of a wide range of environmental and operational metrics. To collect this data, you need one or more receivers such as smartphones or gateways that can detect these signals and store and forward the data for further analysis.

Step 2: Data Pre-processing
Once data collection is complete, the raw data often needs to be cleaned and structured before analysis. This may involve:

  • Removing noise and outliers that can distort the analysis.
  • Filtering data to focus only on relevant signals.
  • Normalising signal strength to account for variations in distance and transmitter power.
  • Time-stamping and sorting the data to analyse temporal patterns.

Step 3: Feature Engineering
Feature engineering is sometimes critical in machine learning. For beacon data, features might include the average signal strength, or changes in signal strength over time. These features can help in developing models that understand patterns in data, such as identifying the trajectory of a moving beacon.

Step 4: Machine Learning Model Development
With pre-processed data and a set of features, you can train machine learning models to detect, classify, or predict. Common machine learning tasks for beacon data include:

  • Classification models to determine the type of interaction (e.g. particular types of movement).
  • Regression models to estimate distances from a beacon based on signal strength.
  • Clustering to identify groups of similar behaviours or patterns.

    Tools and frameworks like Python’s scikit-learn, TensorFlow or PyTorch can be used to develop these models.

Step 5: Evaluation and Optimisation
After developing a machine learning model, it’s important to evaluate its performance using metrics like accuracy, precision, recall and F1-score. Cross-validation techniques can help verify the robustness of the model. Depending on the results, you may need to return to feature engineering or model training to optimise performance.

Step 6: Deployment and Real-time Analysis
Deploying the model into a production environment is the final step. This means integrating the model into an existing app or system that interacts with Bluetooth beacons. The goal is to analyse the data in real-time to make immediate decisions such as sending notifications to users’ phones.

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