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The authors describe a scalable distributed methodology for increasing the rate of real packets received by the Base Station (BS) in a Wireless Sensor Network (WSN) and to limit the inimical impacts of intruders in the network. First, the authors utilize a Dynamic Dendritic Cell Algorithm (DDCA) that effectively detects harmful intruders in a WSN and dynamically adjusts the monitoring period in response to the situation of the network. A sensor node running this algorithm can identify fake packets generated by the intruders based on pre-defined rules. Second, they apply a Markov Chain Monte Carlo (MCMC) method called the Metropolis-Hastings (MH) algorithm to infer the location of intruders in a wireless sensor network using partial information obtained from a subset of the sensor nodes.
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