The Internet-of-Things (IoT) has engendered a new paradigm of integrated sensing and actuation systems for intelligent monitoring and control of smart homes and buildings. One viable manifestation is that of IoT-empowered smart lighting systems, which rely on the interplay between smart light bulbs (equipped with controllable LED devices and wireless connectivity) and mobile sensors (possibly embedded in users’ wearable devices such as smart watches, spectacles, and gadgets) to provide automated illuminance control functions tailored to users’ preferences (e.g., of brightness, color intensity, or color temperature). Typically, practical deployment of these systems precludes the adoption of sophisticated but costly location-aware sensors capable of accurately mapping out the details of a dynamic operational environment. Instead, cheap oblivious mobile sensors are often utilized, which are plagued with uncertainty in their relative locations to sensors and light bulbs. The imposed volatility, in turn, impedes the design of effective smart lighting systems for uncertain indoor environments with multiple sensors and light bulbs. With this in view, the present article sheds light on the adaptive control algorithms and modeling of such systems. First, a general model formulation of an oblivious multisensor illuminance control problem is proposed, yielding a robust framework agnostic to a dynamic surrounding environment and time-varying background light sources. Under this model, we devise efficient algorithms inducing continuous adaptive lighting control that minimizes energy consumption of light bulbs while meeting users’ preferences. The algorithms are then studied under extensive empirical evaluations in a proof-of-concept smart lighting testbed featuring LIFX programmable bulbs and smartphones (deployed as light sensing units). Lastly, we conclude by discussing the potential improvements in hardware development and highlighting promising directions for future work.