## 2 Background Information
### Artificial Intelligence Techniques
With the ability to mimic biological neural network and human attributes, ANNs have been successful in solving complex challenging problems. ANN consists of small interconnected devices known as nodes inspired from the biological neurons in a brain. Information is passed from these interconnected devices using links represented by an arrow. Input and weight are the two values associated with an incoming connection, whose summation will generate the unit's output. After training an ANN using training data sets, new data sets can be introduced so that the trained ANN can be used further for prediction and classification purposes. The key advantage of using ANNs over other methods lies in its ability to model non-linear and complex processes without much interruption between input and output variables. It is used to solve many problems related to prediction and validation, optimization, function approximation, clustering, time series analysis and pattern recognition. Several architectures of ANN are present in literature which include: Radial Basis Function network, Multi-Layer Perception (MLP), Back-Propagation and Recurrent Neural Network (RNN) [14]. RL is a branch of AI concerned with how intelligent agents should interact in a given environment in order to maximize the concept of cumulative reward. Learning is accomplished by interaction between learning entities and their surrounding environment in the RL process. Objects attempt to learn through trial and error. The value function, the environment, and the reinforcement function are the three main components of RL. The RL environment is generally dynamic, with a range of possible states. There is a set of viable actions for each condition at any given time [21]. With the ability to learn without human supervision, drawing from data that is both unstructured and unlabeled, DL is an attractive AI function based on representation learning. DL mimics the human brain in processing data and creates patterns that are used in decision making. The architecture of DL includes several layers in between input and output layers and non-linear information processing units. DL is considered as a universal learning scheme and it is used to find solution to all kinds of problems in various application areas [22]. DL is also used to solve problems of big data analytics which include determining the volume of input information necessary to represent DL algorithms and obtaining good data abstractions and representations [23]. Feature extraction is represented in multiple hierarchical levels, which distinguishes DL from other machine learning approaches. DL is used in various situations where machine intelligence is useful:  
* People can't explain their expertise (sound and speech recognition, language understanding and vision). * The solution needs adaptation to a particular case (e.g., personalization, biometrics). * If the solution to a problem changes over time (e.g., stock and price prediction, tracking, weather prediction). * Human expert is absent (e.g., navigation on Mars). * The problem size is huge for limited reasoning capabilities (e.g., finding matching ADs to Facebook, calculating webpage ranks, sentiment analysis). Currently, DL is practically used in almost every field. Hence, this technique is often termed as Universal Learning Technique [22]. 3. **Fuzzy Logic (FL)** FL is another AI technique that imitates the way of human decision making. It is used for uncertain reasoning or managing incomplete information [14]. The possibilities are either True (T) or False (F). FL works on the basis of 'truth-value' between 0 and 1 [24]. Fuzzy set membership can take any value between 0 and 1. Examples include centroid defuzzification, maximum and mean-of-maxima [21]. These are some of the recent AI techniques that have been popularly used in addressing the DC, Aggregation and Dissemination challenges. In the upcoming sections, we present an up-to-date survey of existing research trends with respect to AI methods applied in WSN.