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By increasing the complexity of the Internet of Things (IoT) applications, fault prediction become an important challenge in interactions between human, and smart devices. Fault prediction is one of the key factors to achieve better arranging the IoT applications. Most of the current research studies evaluated the fault prediction methods using simulation environments. However, formal verification of the correctness of a fault prediction method has not been reported yet. This paper presents a behavioral modeling and formal verification of a hybrid machine learning-based fault prediction model with Multi-Layer Perceptron (MLP) and Particle Swarm Optimization (PSO) algorithms. In particular, the PSO is used for feature selection. Then, the fault prediction is considered as a behavior to be verified formally. The fault prediction behavior is divided into two types of behaviors: dimension reduction behavior and prediction behavior. For each of the behaviors, one formal model is designed. The behavioral models designed are mapped into the Labeled Transition System (LTS). The Process Analysis Toolkit (PAT) model checker is employed to evaluate the behavioral models. The accuracy of the fault prediction method is done by some existing specifications such as deadlock-free and reachability properties in terms of linear temporal logic formulas. Also, the verification of the fault prediction behaviors is used to detect the defect metrics of information-centric IoT applications. Experimental results showed that our proposed verification method has minimum verification time and memory usage for evaluating critical specification rules than other research studies. INDEX TERMS Internet of Things applications, fault prediction, formal verification, process analysis toolkit, multi-layer perceptron, particle swarm optimization.

Combinatorial optimization has been used in different research areas. It has been employed successfully in software testing fields to construct minimum set of combinations (i.e., in terms of size) which in turn represents the minimum number of test cases. It was also found to be a successful approach that can be applied to solve other similar problems in different fields of research. In line with this approach, this paper presents a new application of the combinational optimization in the design of PID controller for DC servomotor. The design of PID controller involves the determination of three parameters. To find optimal initial PID parameters, different tuning methods have been proposed and designed in the literature. The combinatorial design is concerned with the arrangement of finite set of elements into combinatorial set that satisfies some given constraints. Consequently, the proposed method takes the interaction of the input parameters as a constraint for constructing this combinatorial set. The generated sets are then used in the proposed tuning method. The method proved its effectiveness within a set of experiments in a simulated environment.

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