Omnissa Workspace Uses iBeacons

Omnissa Workspace is a digital workspace platform that streamlines the management of enterprise devices, applications and user experiences. It combines unified endpoint management, identity and access management, application management, and tools for enhancing employee experiences.

The platform integrates with iBeacon and geofencing technologies to enhance its functionality. Geofencing utilises GPS and Wi-Fi to define larger geographic boundaries, allowing for targeted policies based on user location. These features enable organisations to trigger location-based actions, enhance security, and provide contextual notifications to users. Overall, integration of these technologies offers a flexible approach to managing digital workspaces, improving both security and user experience.

Real-World Performance Evaluation of a Hybrid Bluetooth Low-Energy Positioning and Direction-Finding System

There’s new research evaluating the performance of a Bluetooth Low-Energy (BLE) positioning and direction-finding system under conditions that closely mimic real-world usage. The aim of the study was to enhance a BLE-based hybrid algorithm, which integrates both positioning and direction-finding capabilities. The researchers focused on evaluating the system in realistic conditions, which included using multiple types of devices, separating the devices used for creating the database from those used for evaluation, and ensuring a sufficient time gap between data collection and evaluation measurements.

The hybrid algorithm used in the study combines proximity detection, based on the strongest Received Signal Strength Indicator (RSSI), with a fingerprinting approach, where the evaluation data is compared to a pre-existing database. By limiting the search area for positioning to locations with the highest RSSI, the algorithm aims to reduce significant positioning errors. The study also integrated direction-finding functionality into the algorithm, taking into account issues such as signal obstruction caused by the user’s body, which can block radio signals from certain directions.

The evaluation was conducted in a corridor environment, with BLE beacons installed along the walls and ceilings. The research utilised five different smartphone models for both data collection and evaluation. To simulate real usage, measurements were taken from four directions at each evaluation point. The study compared the performance of this hybrid method with a previously proposed method that only included direction estimation based on signal divergence.

The findings demonstrated that the hybrid algorithm significantly outperformed the earlier method in terms of both positioning and direction-finding accuracy, especially under realistic usage conditions. Although the performance of the system declined when the intervals between the BLE beacons were increased, it remained at an acceptable level even with fewer beacons installed. This suggests that the hybrid algorithm is robust and effective, even when the system’s infrastructure is reduced.

In conclusion, the study demonstrated the effectiveness of the hybrid BLE algorithm for positioning and direction-finding in realistic environments. The findings emphasised the importance of conducting performance evaluations under real-world conditions, which better reflect the challenges and variability of actual usage.

iBeacon App Development Considerations

If you are considering writing apps to communicate with iBeacons, here are some high level things you need to think about that are specific to beacon app development:

  • Detecting whether Location and Bluetooth are on/off and alerting the user for permission to use these
  • Detecting beacons in background when the iOS app is closed or the Android app is in doze mode
  • On Android, taking account of the various Bluetooth APIs that exist for the different Android releases
  • Fetching data, associated with a beacon, from a service, such that it’s cached and not fetched every time
  • Arranging for some initial data bundled with the app so that it works straight away without a data connection
  • Fetching data before it is needed such that it’s available with no delay and when there’s no network connection
  • Re-fetching of data when it becomes stale
  • Fetching metadata from the server to control the behaviour of triggering
  • Arranging how Apple will test the app for app review otherwise complications will arise and the review will take weeks
  • Assessing whether to use the mobile OS or manufacturer supplied SDKs (or both)
  • If connecting to beacons, taking account of the unreliability of wireless connections
  • Collecting and uploading statistics/analytics to assess usage
  • Providing end user diagnostics to aid support troubleshooting

Need an experienced beacon app developer to get these things done quicker? Consider our development services.

Creating User Indoor Movement Logs

New research (pdf) looks into the development of an application that tracks user indoor movement logs using Bluetooth beacons. The main focus is on creating a system that is easy to install and use without requiring expertise in beacon installation or positioning analysis. This application is designed for personal home use and simplifies the process by allowing users to install beacons in desired locations, name the spaces and track their movements within the home. The application records users’ movements and the time spent in specific spaces, offering statistical insights such as daily and weekly movement patterns.

The Bluetooth beacons used in this system rely on RSSI (Received Signal Strength Indicator) to estimate the distance between the user’s device and the beacons, with methods like the Kalman filter applied to reduce noise and improve accuracy. To verify its effectiveness, the study conducted experiments comparing manually recorded movement logs with those captured by the application. The results showed an accuracy rate of over 99%, making the system a practical solution for indoor movement tracking in homes, small offices, and other limited spaces.

Key advantages include ease of installation, automatic logging of movement data, and statistical analysis of time spent in different rooms. The application is also suitable for environments like small offices with fewer than 10 employees.

Each Beacon Manufacturer Has its Own App

Each Bluetooth beacon manufacturer typically provides its own proprietary configuration app for several key reasons. Firstly, manufacturers use different internal components and designs, meaning a custom app is necessary to tailor configuration options specifically to the hardware. Many also implement proprietary Bluetooth communication protocols for setup, requiring a unique app to handle these configurations correctly.

Security is another factor, as manufacturers often include measures to prevent unauthorised reconfiguration, and custom apps allow for the necessary authentication and encryption. Customisation also allows manufacturers to highlight unique features that may not be available in generic tools, while a branded app ensures control over the look, feel and overall user experience during configuration.

Some beacons only permit configuration within a limited timeframe after being powered on or require a special mode to be enabled and custom apps are designed to accommodate these specific procedures. Firmware updates are also often delivered through these apps, while support and troubleshooting features, including diagnostic tools and links to support resources, are commonly integrated.

For beacons that store data locally, custom apps offer interfaces to manage that data according to manufacturer-specific formats. While universal configuration tools are theoretically possible, the wide variety of hardware, protocols and features in use makes them difficult to develop, and manufacturer-specific apps remain the most reliable way to fully manage proprietary beacon hardware.

Challenges in Deploying a Location-Based Coupon Service

New research Deploying a Location-Based Coupon Recommendation Service in Retail: Challenges and Lessons Learnt explores the implementation of a Bluetooth Low Energy (BLE) beacon-based location service designed to enhance the retail shopping experience by offering personalised coupon recommendations. This system not only improves customer engagement but also provides retailers with valuable insights into consumer behaviour. The study looks into various challenges encountered during the development and deployment phases, expanding on technical, business, and user-related difficulties, and offers lessons that go beyond typical technological issues.

One of the primary technical challenges was ensuring accuracy in tracking customers’ locations within the store. Initially, the system used trilateration to pinpoint exact X-Y coordinates. However, this method proved inadequate due to signal interference and environmental factors. As a result, the team adopted an area-based tracking system, which was better suited for the retail context. To maintain robustness and scalability, advanced techniques such as fingerprinting and machine learning algorithms were employed, which allowed the system to adapt to various store layouts. Expanding the service to over 2,000 stores posed scalability issues that required innovative solutions, particularly in managing different store environments and layouts. Additionally, cost constraints, particularly in regard to hardware and devices, and ensuring compliance with privacy regulations like the GDPR, were significant hurdles. The system had to balance performance with legal requirements while limiting data collection to ensure customer privacy.

From a business perspective, the service needed to align with operational goals. One key challenge was determining the appropriate level of accuracy for tracking customer movements. After discussions with the business stakeholders, it was agreed that precise X-Y positioning was unnecessary; instead, tracking customer movements within specific store areas, such as aisles or product sections, sufficed. Defining these areas of interest was critical, as some store sections required more detailed tracking than others, depending on the season or product demand. For example, chocolate aisles may be more important during the winter, whereas ice cream sections are prioritised in the summer. This required a flexible, business-driven approach to configuring the system.

Beacon placement posed another set of challenges. Initially, the beacons were installed at human height on store shelves, but this led to significant interference from obstacles such as stocked products. Moving the beacons to the ceiling reduced signal interference and provided more stable coverage. However, this required careful calibration to ensure optimal signal strength, battery life, and overall system performance. The team also had to consider different types of mobile devices used by customers, as varying device capabilities affected the system’s performance, requiring additional adjustments and testing.

User acceptance played a crucial role in the success of the system. Initially, employees expressed concerns about the potential health risks of working near BLE beacons. These concerns were alleviated after the staff was educated about the low levels of radiation emitted by the beacons. On the customer side, users were more likely to engage with the system when offered personalised incentives, such as coupons tailored to their shopping preferences. The system proved effective, as it increased average basket size, showing that personalised coupon recommendations not only improved the shopping experience but also contributed to higher sales. Customers appreciated receiving relevant offers as they moved through the store, streamlining their shopping experience and saving them time.

The study concludes by highlighting the importance of integrating technical solutions with business goals, user preferences and privacy considerations. The deployment of location-based services in retail is not just a technical exercise but one that requires close collaboration between developers, retailers, and end-users. The lessons learned from this project provide a valuable roadmap for future implementations of similar services, emphasising the need for flexibility, privacy protection, and user-centric design.

Can an iBeacon Send Users to a Website?

The short answer is no, iBeacons cannot directly send users to a website. iBeacons do not have the capability to push content or URLs to devices automatically. Instead, they rely on compatible apps to detect their presence and take appropriate actions which can include sending the user to a web site.

There used to be a mechanism in Android that used the Eddystone-URL advertising format but this has since been discontinued by Google.

New ATEX-Certified Beacon

There’s a new ATEX-certified beacon from Teltonika. ATEX means it’s suitable for use in hazardous environments such as the oil, gas and chemical industries.

There are two variants, one that just sends out its ID and another with temperature, humidity, movement and magnet detection.

These beacons aren’t yet on our web site but are available to special order for use in consulting projects.

What Bluetooth Systems Can Track Working Using Their Smartphones?

Contrary to popular belief, it’s not possible to directly track smartphones using Bluetooth alone. Both iOS and Android devices have built-in privacy protections and limitations that prevent this kind of tracking.

For iOS devices, Apple has implemented randomised MAC addresses for Bluetooth transmissions. This means that the unique identifier broadcast by an iPhone or iPad changes regularly, making it impossible to consistently track a specific device over time. Android doesn’t continuously send out Bluetooth transmissions.

However, whilst smartphones themselves can’t be directly tracked via Bluetooth, there are systems that can perform location tracking using Bluetooth beacons and gateways. These systems rely on people carrying small Bluetooth beacons, often in the form of keyfobs or badges, which broadcast a unique identifier. Fixed gateway devices are then installed throughout an area to detect these beacons.

When a gateway detects a beacon, it records the beacon’s identifier and signal strength to infer distance, along with a timestamp. By combining data from multiple gateways, the system can estimate the location of the beacon, and by extension the person carrying it, within the covered area. This approach is often used in workplace settings for things like occupancy monitoring or contact tracing.

It’s important to note that these systems require active participation – people must choose to carry the beacon devices. This is quite different from the idea of passively tracking smartphones without user consent.

Some retailers have experimented with using Bluetooth beacons to track customers’ movements within stores. However, this still requires customers to have the store’s app installed and Bluetooth enabled on their phones. These work the other way around by having fixed beacons and the app detecting the beacons. It’s not a covert tracking system, but rather one that customers opt into, often in exchange for discounts or other benefits. It’s less reliable due to the nuances of ensuring the app runs on all phones, at all times.

In summary, whilst it’s not possible to directly track smartphones via Bluetooth due to privacy protections and limitations, there are Bluetooth-based systems that can provide location based services when users actively participate.

Low-Cost AoA Wayfinding

There’s a new paper (pdf) on a low-cost wayfinder system using Bluetooth’s Angle-of-Arrival (AoA) technology. This system is designed to help visually impaired individuals navigate public spaces, such as airports or shopping centres. The innovation lies in moving the antenna array required for angle measurement onto the user’s device, simplifying the beacon infrastructure. Each beacon becomes a low-cost, single-antenna transmitter, significantly reducing the deployment cost compared to traditional indoor positioning systems.

The prototype, built with Bluetooth 5.1 boards and developed using Python, successfully demonstrated accurate angle and distance measurement. The system achieved a 10° angle accuracy within 15 meters and calculated distance using the Received Signal Strength Indicator (RSSI). For visually impaired users, the system could be extended with a voice notification feature. The ultimate goal is to develop the system into a smartphone app.

Future enhancements include addressing front-and-back signal ambiguities by adding orthogonal antennas and extending the system’s range.