## 2 Background Information
### Artificial Intelligence Techniques  
AI refers to a computer system's ability to accomplish activities that need human intelligence while imitating human minds or ideas. It is considered as one of the important area of computer science that tries to make machine \"smart\". The most widely used techniques of AI include: search methods, learning methods, fuzzy system, knowledge based and reasoning. AI is applicable to resolve many complex concerns in various sectors such as security, finance, health care, transportation and so on, through its ability in handling deficient and noisy data, dealing with non-linear issues, and are suitable for use in prediction and faster generalization once trained [14]. We have studied and classified various AI techniques used in WSN as depicted in Figure 3. Different AI techniques used in addressing WSN challenges include Fuzzy Logic, Artificial Neural Network, Evolutionary Computation, Nature-inspired, Multi-Agent Systems, Trajectory based, Physical Computation, Swarm Intelligence, Deep Learning, Reinforcement Learning and Hybrid. An overview of these techniques is presented as follows. 1. **Metaheuristics** Metaheuristics are the most common type of algorithms that use a degree of randomness to achieve optimal solutions to hard problems (or as optimal as possible) [15]. Metaheuristics are applied to a large number of areas. Metaheuristic algorithms can be categorized in various ways. For example, one scheme of classification is: trajectory-based and population-based approaches [16]. Trajectory-based schemes typically aim to locate a single optimal solution through piecewise style movement in the design (search) space (e.g., simulated annealing). While population-based schemes use multiple solution through search space and cooperate with each other to reach the final solution (e.g., evolutionary computation, physical inspired computation and nature inspired computation). Evolutionary computation is inspired by biological evolution and natural selection, crossover or recombination and mutation (e.g., Genetic Algorithm, Differential Evolution and Memetic Algorithm). Physical inspired computation is inspired by physical areas such as classical and quantum mechanics, thermodynamics, electromagnetism, relativity, and optics [17] (e.g., Central Force Optimization, Gravitational Search Algorithm, Intelligent Water Drops and so on). Nature inspired computations imitate colonies, birds, flocks, insects in their living method or individuals communication (e.g., harmony, bat algorithm, cuckoo search etc.). The collective behavior that arises from a group of social insects has been called Swarm Intelligence (SI). SI deals with the cooperation of numerous homogeneous individuals in the environment [18]. Such techniques involve strategies and share information among the individuals for self-organization, learning and co-evolution during iterations to provide high efficiency. The individuals follow very simple rules and as there is no central infrastructure available to show how individuals behave,interaction can take place between individuals and these individuals as a population can exchange related data using any message-carrier [19]. Multiple interacting intelligent agents can solve a problem that is hard to solve by an individual agent or monolithic system, by searching and interacting with environment. Agents search for other neighboring agents and interact with them or with the environment to learn new things and to make decisions. To complete their assigned mission, agents utilize their knowledge, make decisions and conduct actions in the environment [20]. 2. **Learning Methods** One of the most important feature in the human (or animals) is learning. Learning is the ability to automatically acquire new information and improve it via experience without requiring any explicit programming. So, learning is a part of AI like Artificial Neural Network (ANN), Reinforcement Learning (RL) and Deep Learning (DL).