As cable networks evolve to meet the demands of next-generation connectivity, a quiet transformation is unfolding within the fibers that carry our data.
Distributed fiber optic sensing (DFOS) is emerging as a transformative technology that enables real-time environmental awareness, infrastructure monitoring, and smart grid optimization – all using existing fiber infrastructure.
This sensing revolution reflects broader industry trends toward full automation, digital network alignment and pervasive sensing in CableLabs’ technology vision, positioning cable networks as essential platforms for intelligent and adaptive connectivity.
What is distributed fiber optic sensing and why is it important?
DFOS turns standard optical fibers into thousands of sensors capable of detecting acoustic, thermal and mechanical disturbances. This capability allows operators to proactively monitor their networks, detect threats before they cause damage, and even gather insights into the surrounding environment.
Two main approaches – backscatter-based sensing and forward sensing – provide complementary strengths.
Backscattering systems, shown below in Figure 1, provide high spatial resolution and single-ended diffusion, and work by sending laser pulses through a fiber and analyzing minute variations in reflected light. These changes carry unique signatures of acoustic, thermal, or mechanical disturbances along the fiber.
The term “distributed” means that measurements are captured continuously along the entire length of the optical fiber (not just at discrete points), turning a single fiber strand into thousands of sensor sites.
Figure 1. Backscatter-based distributed sensing.
The forward-facing DFOS system, shown in Figure 2, excels at remote sensing and seamless compatibility with existing optical amplifiers. By leveraging coherent transceivers already deployed in high-capacity networks, this approach enables operators to extract sensor information from the same signals used to transmit data, without the need for additional hardware.
This integration reduces cost, simplifies deployment and opens the door to advanced analytics over hundreds of kilometres, making it ideal for large-scale infrastructure monitoring and proactive maintenance.
Figure 2. Forward-based distributed sensing.
Cable networks as city-scale sensor arrays
Imagine a city where every strand of fiber acts as a sensor. With DFOS, this vision becomes a reality. Cable operators can leverage large-scale fiber deployments to create ubiquitous sensor coverage. Bundled fiber paths crossing urban landscapes can detect vibrations, temperature changes and other anomalies – enabling smarter cities and safer infrastructure.
The “network as sensors” concept enabled by DFOS transforms optical fibers into thousands of sensor elements, enabling real-time monitoring of large-scale environments and infrastructure.
Real-world impact: field trials and use cases
DFOS has already proven its value in proactive maintenance, urban monitoring, environmental sensing and security applications.
Detecting early signs of fiber damage or accidental cable breakage is one of the main uses of DFOS technology. It helps identify unusual activity near critical fiber links, allowing network operators to take preventative action before failures occur.
Researchers have demonstrated this capability using advanced transceivers on long-distance fiber links in real-world network environments. In one case, the DFOS system detected visible polarization changes several minutes before a buried cable was accidentally damaged during construction activity. These early warning signals, combined with advanced coherent transceivers, can improve network stability by enabling proactive forwarding and error prevention.
DFOS is well-suited to cities, where existing fiber networks can be used to monitor traffic, building and infrastructure conditions in real time. Its continuous, high-resolution sensing helps improve safety and resilience by detecting early signs of damage or stress in urban systems.
Recent studies in cities like Hong Kong have shown that DFOS can identify and track vehicles based on their unique vibration patterns near roadside fibers. The combination of acoustic vibration and temperature sensing has also proven effective in detecting underground problems, such as damaged or flooded cables, and has shown strong potential for improving network reliability.
DFOS provides powerful environmental and geophysical monitoring capabilities by converting standard optical fibers into dense, real-time sensor arrays. It can detect and localize ground vibrations, temperature changes and stress along large lengths of propagated fibres, making it ideal for monitoring earthquakes, landslides, thawing permafrost, subsea tsunamis and subsurface hydrological processes. DFOS allows researchers to monitor dynamic environmental changes over time and across large areas. This allows the establishment of early warning systems, the conduct of long-term climate studies and the enhanced understanding of natural hazards in both remote and populated areas.
DFOS can enhance security around critical infrastructure by complementing traditional tools such as cameras, radar and lidar. Using vibration data along the fibers of the network, it can detect and classify mechanical threats such as jackhammers or excavators. Researchers have shown that machine learning (ML) techniques, including transfer learning, can achieve high accuracy when analyzing these signals. This demonstrates that DFOS can reliably identify different types of mechanical activity, even when trained on limited or noisy data.
Overcoming challenges and looking to the future
Although DFOS offers tremendous promise, several hurdles remain.
Integrating sensing with live traffic data. The ultimate goal of fiber sensing is to use existing fiber optic networks to transmit data and sense environmental changes at the same time. However, DFOS systems still rely on unused “dark” fiber because combining sensing and live data traffic is difficult. Early tests showed that strong sensor pulses caused errors in nearby data channels. These high-power signals create interference through nonlinear effects, so the spacing between sensing and communication channels must be carefully controlled. Deployment in PONs. Traditional DFOS technologies are difficult to integrate into access networks, such as passive optical networks (PONs), which use passive power splitters to connect multiple homes and businesses to the Internet. This is because scattered signals from the different incident fibers of the splitters are superimposed on the trunk fibers before they are detected at the end of the optical line. Reduce investigator costs. Most DFOS interrogators available today are expensive because they are designed for long-range operation, high optical power and specialized industrial applications such as oil and gas, security and geophysical sensing. To enable wider deployment in telecom networks, the technology must be scaled by reducing the cost of each unit and optimizing the design for operator-centric use cases. Training ML models on rare events. Training machine learning models to detect critical events in DFOS data is key to realizing the full potential of fiber sensing, especially for rare but critical issues such as fiber damage or premature interruption. The challenge is that DFOS systems generate huge amounts of data, much of which comes from harmless background noise. For example, a system monitoring tens of kilometers of fiber could produce terabytes of data every day. As a result, meaningful events are buried in a sea of routine data, making it difficult for machine learning models to know what really matters.
CableLabs addresses these challenges with pioneering approaches:
Coexistence strategies. The new method allows sensing of active fiber networks without compromising broadband data channels. By using only a small portion of fiber spectrum, operators can integrate distributed sensors into live networks, eliminating the need for dedicated fiber strands and unleashing cost-effective scalability. Low-energy coding sequences. CableLabs has demonstrated technologies that allow sensor signals to coexist seamlessly with traditional data channels, paving the way for seamless integration and enabling self-learning networks. Adaptive sensing algorithms. By leveraging artificial intelligence and machine learning, these algorithms dynamically adapt to changing environments, improving detection accuracy and reducing false positives.
The cable industry now has a unique opportunity to pioneer shaping sensing frameworks and drive global standards.
Join the sensing revolution
DFOS is more than just a technical innovation; It is a strategic asset for cable operators. By turning fiber into a sensing platform, the industry can unleash new capabilities in the areas of resilience, intelligence and environmental awareness.
CableLabs invites operators, vendors and researchers to collaborate on field trials, standards development and commercialization strategies. Whether you’re exploring sensing-as-a-service models or integrating AI-based analytics, now is a good time to get involved. Contact us, Dr. Steve Jia and Dr. Karthik Chottagunta, to get started.
The future of cable isn’t just about high speeds. It’s about smarter, smarter networks that can anticipate, adapt and protect. CableLabs’ vision is to transform connectivity into a platform for innovation, where networks do more than just transfer data: they sense, learn and respond in real time.
