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Abstract Nowadays, many Internet-of-Things (IoT) devices with rich sensors and actuators are being deployed to monitor community spaces. The data generated by these devices are analyzed and turned into actionable information by analytics operators. In this article, we present a Resource Efficient Adaptive Monitoring (REAM) framework at the edge that adaptively selects workflows of devices and analytics to maintain an adequate quality of information for the applications at hand while judiciously consuming the limited resources available on edge servers. Since community spaces are complex and in a state of continuous flux, developing a one-size-fits-all model that works for all spaces is infeasible. The REAM framework utilizes reinforcement learning agents that learn by interacting with each community space and make decisions based on the state of the environment in each space and other contextual …