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Monitoring spatio-temporal continuous fields using wireless sensor networks has emerged as a novel and efficient solution. The development of energy efficient query dissemination and data collection algorithms for environments where only a small subset of nodes has relevant readings is a challenging problem if no information about the location of these nodes is available. Monitoring these data requires not only an initial discovery but also a continuous search for new relevant data due to field variations in time. One solution to this problem is to let nodes cooperate and decide jointly which data are relevant and their location. Trails to relevant data can be distributively marked as insect colonies do using pheromone-based schemes. In this work, we propose PhINP, a probabilistic pheromone-based in-network processing scheme to monitor information on WSNs. Our proposal takes advantage of both query-based innetwork filtering to select nodes to answer, and a pheromone-based strategy to direct queries towards nodes with relevant readings. Additionally, nodes use reinforcement learning to improve the routing performance of queries. We demonstrate by extensive simulations that the routing cost can be reduced by approximately 70% over flooding with an error below 1%.