Applications that require fine-grain monitoring of physical environments subjected to critical conditions, such as fire, leaking of toxic gases and explosions, pose a great challenge to sensor network protocols. These networks have to provide a fast, reliable, fault-tolerant and energy-aware channel for events diffusion, which meets the requirements of query-based, event-driven and periodic sensor networks application scenarios. These requirements have to be met even in the presence of emergency conditions that can lead to node failures and path disruption to the sink. This paper proposes two routing protocols: periodic, event-driven and query-based protocol (PEQ) and its variation CPEQ, two fault-tolerant and low-latency algorithms that meet sensor network requirements for critical conditions supervision in context-aware physical environments. While PEQ can provide low latency for event notification, fast broken path reconfiguration, and high reliability in the delivery of event packets for low-network data traffic, CPEQ is a cluster-based routing protocol that groups sensor nodes to efficiently relay the sensed data to the sink by uniformly distributing energy dissipation among the nodes and reducing latency for high-network data traffic (typical in emergency situations). PEQ and its variant CPEQ use the publish/subscribe paradigm to disseminate requests across the network. We discuss both PEQ and CPEQ protocols, their implementation, and report on the performance results of several scenarios using NS-2 simulator. The results obtained are compared with the well-known directed diffusion (DD) protocol, and show that our proposed algorithms exhibit a clear indication to meet the constraints and requirements of critical condition supervision in context-aware physical environments. Our results indicate that PEQ outperforms DD in the average delay since it uses the shortest path for the delivery of packets and speed up new subscriptions by using the reverse path used for event notification packets. CPEQ also outperforms DD in both the average delay and in the packet delivery ratio when the network scales up.