Sensing devices are now ubiquitous in people’s homes, vehicles and workplaces. Already useful in isolation, these distributed sensors are increasingly being connected to each other and integrated with AI- infused systems, paving the way for rapid advances in collaborative sensing that can generate insights to improve the capabilities of individual sensors. Beyond autonomous urban mobility, promising applications for collaborative sensing are diverse, including perceptive mobile networks that combine communications and sensing on the same network. Collaborative sensing will reshape how cities operate and how organizations use information to make decisions. Promising applications for collaborative sensing are diverse, including improving urban mobility. For example, connected traffic lights can dynamically adjust themselves based on traffic cameras and environmental sensors to manage urban congestion and emission levels. Other examples for collaborative sensing include large-scale autonomous mapping in mines,129 analysing storm systems,130 drone swarms,131 internet-of-things- based structural health monitoring,132 environmental monitoring and bringing more precision to agriculture and natural resource management.133 Collaborative sensing pairs distributed sensors, including those on satellites and underwater and subterranean platforms, with reliable connectivity and algorithmic processing at the network’s edge to reduce transmitted data volumes. Autonomous agents, such as robots, drones, intelligent vehicles and IT systems, with semantic reasoning and dynamic planning capabilities, will be equipped to navigate unfamiliar environments and make collective decisions. Research in sensor fusion, collaborative sensing and collaborative autonomy has often been driven by the defence industries’ need for real-time decisions and actions. Increasingly, the civilian benefits of these linked capabilities are becoming apparent. Imagine an autonomous vehicle that drives appropriately in the context of its own sensors, and that also knows (thanks to connected sensors on a traffic light hundreds of yards away) that a speeding vehicle is approaching on a collision course. The US Federal Communications Commission’s (FCC) recent decision134 to adopt the 5.9 gigahertz (GHz) band for cellular-vehicle- to-everything technology is a critical step towards enabling such advances and will create new opportunities to explore how collaborative sensing might address infrastructure costs, reduce traffic congestion and accidents and lower carbon emissions. The European Commission and China’s Ministry of Industry and Information Technology have similarly enacted enabling legislation. Challenges remain. Most platforms on which sensors are deployed have strict power and connectivity constraints, requiring engineering approaches such as compressing 3D scene classification methods,135 improved navigation136 in the absence of GPS and improved low-power processing at the network edge. Data-sharing security and privacy policies will also need to evolve. The key to unlocking the benefits of collaborative sensing at scale and achieving true collaborative autonomy, will be multi-modal algorithms137 that can process numerous varieties of sensor data, from LiDAR (light detection and ranging) to EO/ IR (electro-optical/infra-red) cameras to radar and beyond. Much of this work is currently focused on balancing a shared information landscape and operational picture with distributed processing, while also minimizing bandwidth and power requirements. Generative AI may play a role here. Recent research demonstrates that large language models (LLMs) may optimize simple collaborative navigation tasks much more efficiently than traditional deep reinforcement learning (DRL) approaches.138Daniel Dossenbach Scientific Advisor in Innovation, State Secretariat for Education, Research and Innovation Switzerland Karen Hallberg Principal Researcher, Bariloche Atomic Center (CONICET)David Parekh Chief Executive Officer, SRI International Develop cross-industry data standards – Establish common protocols for sensor data sharing, security and interoperability across industries to enable seamless integration of distributed sensing networks.Ecosystem readiness map Boxed Icons Modernize critical infrastructure – Upgrade telecommunications and connectivity infrastructure in key urban environments to support the bandwidth and reliability requirements of collaborative sensing systems.KEY ACTIONS TO ACHIEVE SCALE TechnologicalSocial Economic EnvironmentalPolicy Image: Collaborative sensing networks combine distributed sensors with AI to improve decision- making, urban systems and autonomous technologies. Credit: Midjourney and Studio Miko. Prompt (abbreviated): “LiDAR scan of buildings, cars, people and scenery.” Read more: For more expert analysis, visit the collaborative sensing transformation map. Authored by: Mehrdad Dianati. Top 10 Emerging Technologies of 2025 34