Matthew BoyleThe Wi-Fi asset tracking industry is projected to grow from $5 billion in 2024 to $9.2 billion by 2029, driven by advances in technologies like Wi-Fi, RFID, and Bluetooth. Wi-Fi asset tracking, particularly suitable for enterprises with existing infrastructures, provides wide-range tracking capabilities. However, it comes with challenges, such as high power consumption and dependency on strong network coverage. Comparatively, RFID offers a cost-effective, power-efficient solution but requires readers and has a shorter range than Wi-Fi. As businesses weigh their options, integrating AI and machine learning into asset tracking systems is becoming a future trend. AI can process large volumes of data, optimize signal accuracy, and enhance location services by overcoming challenges such as signal interference. Innovative solutions like UnaBiz’s Sigfox Atlas Sparks showcase the potential for AI-powered tracking, blending Wi-Fi and Sigfox data to advance geolocation services. For enterprises, careful planning, system maintenance, and exploring new technologies are key to successfully deploying and scaling Wi-Fi-enabled asset tracking solutions.
Connectivity options
Wi-Fi, Radio Frequency Identification (RFID), and Bluetooth, which have previously been covered on IoT Insider, are some of the options available to businesses for asset tracking.
Enterprises with existing Wi-Fi infrastructures who benefit from the company’s ability to offer a big wireless range for tracking are well suited for asset tracking that is Wi-Fi-enabled. By integrating it into existing infrastructure, this reduces the need for extra investment, thus lowering the total cost and complexity. In the same vein, because Wi-Fi enabled asset tracking is dependant on the strength of a Wi-Fi network, if it’s not up to scratch, it’s no use in asset tracking.
Another drawback of a Wi-Fi asset tracking system is that, in contrast to passive RFID tags or Bluetooth Low Energy devices in particular, Wi-Fi enabled tracking devices can be very power-hungry.
Another well-liked method for tracking items or managing inventory in financial and manufacturing is RFID, which is also used. RFID systems can be categorised as passive or active, passive RFID tags are generally inexpensive and do n’t require a power source, which is suitable for large-scale deployments. Readers are required for RFID, and its range is a few times that of Wi-Fi.
Creating and maintaining a Wi-Fi asset tracking system
Planning and outlining your goals is the first step in the implementation of a Wi-Fi asset tracking system. Do you want to improve security or inventory management? This will require an evaluation of the existing infrastructure, including the network’s coverage, bandwidth, and overhead; this will be particularly useful for identifying potential problems that might have an impact on how the tracking system functions.
Factors like building materials, layout, and device interference can have an impact on the signal strength and accuracy that Wi-Fi outputs, depending on the environment.
When the Wi-Fi asset tracking system is up and running, it needs regular maintenance to continue to operate properly. This includes periodically recalibrating the system for accuracy purposes, updating the firmware and software, and monitoring the Wi-Fi network itself.
The future of Wi-Fi asset tracking
One of the emerging trends in the IoT industry is the integration of AI and machine learning ( ML) into asset tracking systems, which is one of the possible future trends for the field.
Because of its ability to process large amounts of data from various sources and extracting valuable insights, AI can be employed in asset tracking to examine data patterns over time. For instance, it can be used to precisely process data from Wi-Fi signals and sensors to locate an asset. Additionally, it might be able to take into account factors like network congestion and signal interference that might affect tracking accuracy.
UnaBiz showed off the potential application of ML when it unveiled the beta version of Sigfox Atlas Sparks for asset tracking in the supply chain and logistics. The proposed solution employs patented machine learning algorithms for data from Smart Wi-Fi trackers working alongside Sigfox trackers and two different tracker types that have been aggregated in the Cloud.
According to Alexis Susset, Chief Technology Officer at UnaBiz,” Sigfox Atlas Sparks demonstrates the transformative power of AI and machine learning more advancing our world-leading suite of geolocation services.”
