Wireless sensor networks are subject to node and link failures for a variety of reasons. This paper proposes a k-disjoint-path routing algorithm that varies the number of disjoint paths (k) in order to meet a target-delivery ratio of critical events and to reduce energy consumption. The proposed algorithm sends packets to the base station through a single path without the occurrence of critical events, however, it sends packets to the base station through k disjoint paths (k>1) under the occurrence of critical events, where k is computed from a well-defined fault model. The proposed algorithm detects the occurrence of critical events by monitoring collected data patterns. The simulation results reveal that the proposed algorithm is more resilient to random node failure and patterned failure than other routing algorithms, and it also decreases energy consumption much more than the multi-path and path-repair algorithms.

This paper proposes a novel routing algorithm that constructs position-based k-disjoint paths to realize greater resiliency to patterned failure. The proposed algorithm constructs k-disjoint paths that are spatially distributed by using the hop-count based positioning system. Simulation results reveal that the proposed algorithm is more resilient to patterned failure than other routing algorithms, while it has low power consumption and small delay.

In this paper, we propose a novel in-network aggregation scheduling scheme for forest fire monitoring in a wireless sensor network. This adaptively configures both the timeout and the collecting period according to the potential level of a fire occurrence. At normal times, the proposed scheme decreases a timeout that is a wait time for packets sent from child nodes and makes the collecting period longer. That reduces the dissipated energy of the sensor node. Conversely, the proposed scheme increases the timeout and makes the collecting period shorter during fire occurrences in order to achieve more accurate data aggregation and early fire detection.

This paper proposes a reliable data aggregation scheduling that uses caching and re-transmission based on track topology. In the proposed scheme, a node detects packet losses by overhearing messages that includes error indications of the child nodes, from its neighbor nodes. If packet losses are detected, as a backup parent, the node retransmits the lost packet. A retransmission strategy is added into the adaptive timeout scheduling scheme, which adaptively configures both the timeout and the collection period according to the potential level of an event occurrence. The retransmission steps cause an additional delay and power consumption of the sensor nodes, but dramatically increase the data accuracy of the aggregation results. An extensive simulation under various workloads shows that the proposed scheme outperforms other schemes in terms of data accuracy and energy consumption.