Opportunistic sensing can be used to obtain data from sensors that just happen to be present in the user’s surroundings. By harnessing these opportunistic sensor configurations to infer activity or context, ambient intelligence environments become more robust, have improved user comfort thanks to reduced requirements on body-worn sensor deployment and they are not limited to a predefined and restricted location, defined by sensors specifically deployed for an application. We present the OPPORTUNITY Framework and Data Processing Ecosystem to recognize human activities or contexts in such opportunistic sensor configurations. It addresses the challenge of inferring human activities with limited guarantees about placement, nature and run-time availability of sensors. We realize this by a combination of: (i) a sensing/context framework capable of coordinating sensor recruitment according to a high level recognition goal, (ii) the corresponding dynamic instantiation of data processing elements to infer activities, (iii) a tight interaction between the last two elements in an “ecosystem” allowing to autonomously discover novel knowledge about sensor characteristics that is reusable in subsequent recognition queries. This allows the system to operate in open-ended environments. We demonstrate OPPORTUNITY on a large-scale dataset collected to exhibit the sensor richness and related characteristics, typical of opportunistic sensing systems. The dataset comprises 25 hours of activities of daily living, collected from 12 subjects. It contains data of 72 sensors covering 10 modalities and 15 networked sensor systems deployed in objects, on the body and in the environment. We show the mapping from a recognition goal to an instantiation of the recognition system. We also show the knowledge acquisition and reuse of the autonomously discovered semantic meaning of a new unknown sensor, the autonomous update of the trust indicator of a sensor due to unforeseen deteriorations, and the autonomous discovery of the on-body sensor placement.