In this paper, a faulty node detection scheme with a hybrid algorithm using a Markov chain model that performs collective monitoring of wireless sensor networks is proposed. Mostly wireless sensor networks are large-scale systems, heavily noised, and the system workload is unfairly distributed among the master node and slave nodes. Hence, the master node may not easily detect a faulty slave node. Initially, the proposed schemes use interval weighting factors, based on probabilistic computation to enhance Bose-Chaudhuri-Hocquenghem (BCH) code for reliable faulty node detection. In this paper, a more
accurate faulty node detection scheme using a Markov chain model is investigated. Each slave node¡¯s condition can be divided
into three states by probability calculation: Good-, Warning-, and Bad-state. Using this information, the master node can predicts the area in which an error frequently occurs. Hence, the master node can analyze the faulty slave node during communication between the master and several slave nodes. This scheme can be used for detecting and preventing serious damage caused by node failure. Simulation results show that the proposed method can improve the reliability of faulty node detection and the miss detection rate for a Industrial Wireless Networks.