Fault diagnosis in wireless sensor network using clonal selection principle and probabilistic neural network approach

Abstract

The fault diagnosis in wireless sensor networks is one of the most important topics in the recent years of research work. The problem of fault diagnosis in wireless sensor network can be resembled with artificial immune system in many different ways. In this paper, a detection algorithm has been proposed to identify faulty sensor nodes using clonal selection principle of artificial immune system, and then the faults are classified into permanent, intermittent, and transient fault using the probabilistic neural network approach. After the actual fault status is detected, the faulty nodes are isolated in the isolation phase. The performance metrics such as detection accuracy, false alarm rate, false-positive rate, fault classification accuracy, false classification rate, diagnosis latency, and energy consumption are used to evaluate the performance of the proposed algorithm. The simulation results show that the proposed algorithm gives superior results as compared with existing algorithms in terms of the performance metrics. The fault classification performance is measured by fault classification accuracy and false classification rate. It has also seen that the proposed algorithm provides less diagnosis latency and consumes less energy than that of the existing algorithms proposed by Mohapatra et al, Panda et al, and Elhadef et al for wireless sensor network. KEYWORDS artificial immune system, clonal selection principle, fault diagnosis, probabilistic neural network, wireless sensor networks Int J Commun Syst. 2019;32:e4138.wileyonlinelibrary.com/journal/dac

Improved Clonal Selection Algorithm Based on Biological Forgetting Mechanism

“…FCSA Input: N (the size of the population), n (the number of antibodies selected for cloning), n c (the number of clones), m (the degree of variation), c (Rac1 protein activity threshold) Output: the best antibody (1) Begin (2) Randomly generate N antibodies to form the initial candidate set (3) while not meet algorithm termination conditions do (4) Calculate the affinity Aff ab i of each antibody for antigen in the candidate set and record antibody survival time T ab i (5) Sort the antibodies in the candidate set according to their affinity, and put the best n antibodies into the antibody set Ab s (6) forab i inAb s (7) Update the value of the appropriate memory of antibody ab i : S ab i + � 1. See CLONING METHOD, clone antibody ab i according to n c and Aff ab i , and put all antibodies obtained by cloning into antibody set Ab c (8) end for (9) forab i inAb c (10) See VARIATION METHOD, according to the degree of variation m and the affinity of the antibody for the antigen Aff ab i to mutate ab i (11) if antibody ab i is a variant antibody (12) e ab i survival time T ab i � 0, e appropriate memory intensity S ab i � 1 (13) end if (14) end for (15) Select the N antibodies with the highest antigen affinity in Ab c and Ab to replace the N antibodies in Ab (16) See FORGETTING METHOD, calculate the Rac1 protein activity of each antibody in Ab according to the ratio of T ab i to S ab i (17) if antibody ab i Rac1 protein activity > threshold (18) forget the antibody ab i (19)…”

“…Kim and Bentley [3] used a dynamic cloning selection algorithm to solve the problem of anomaly detection in the changing environment. In recent years, the cloning selection algorithm inspired by biological immunity has also been widely used in power industries such as power plant addressing [4], electricity price prediction [5], hybrid shop scheduling [6], car flow organization [7] and other power industries, playing an active role in the improvement of clustering [8] as well as machine learning algorithms [9][10][11].…”

Environmental monitoring through Vehicular Ad Hoc Network: A productive application for smart cities

“…C i and C j are the misclassification cost of class i and class j, respectively, where i ≠ j. 56 If the condition in Equation ( 10) holds good, then sensor value x belongs to class i . Otherwise, sensor value x belongs to class j .…”

FDIA System for Sensors of the Aero-Engine Control System Based on the Immune Fusion Kalman Filter

“…Ghosh and Srinivasan used an AIS for troubleshooting and process monitoring [14]. rough theoretical analysis, Mohapatra et al made it possible to solve the fault diagnosis in wireless sensor networks through the AIS [15]. Jiang and Chang developed a novel antibody population optimization-based AIS for rotating equipment anomaly detection [16].…”