## 5 Conclusions and Future Directions  
Nowadays, we are witnessing a growth in the number of AI -based systems and solutions which facilitate the optimization of services in the field of WSN. The combination of both AI methods and WSNs have now become a reality, offering benefits to the area of Internet of Things, and allow systems to learn and to monitor activities and support the decision-making process. In this paper, we have provided a review of DC, Aggregation and Dissemination challenges in WSNs. Various AI methods in WSNs are briefly introduced along with their classifications. AI techniques used by researchers to address the DC, Aggregation and Dissemination challenges in WSNs are briefly explained for the span of 2010 to 2021. AI-based solutions to various problems in WSNs have been discussed and summarized. From the findings depicted in Figure 5 and Table 2, we can notice that Swarm intelligence is adopted as the best suited technique or acceptable method by the research community for solving DC, aggregation and dissemination challenges in WSNs. 36% of articles applied swarm intelligence while 20%, 18%, and 5% for FL, Hybrid, Reinforcement learning respectively. It can be noted that SI methods are popularly applied in WSN. Some AI algorithms have been used frequently to solve WSN challenges. GA, PSO, ACO, ABC, BA, DL and fuzzy have been used more than other algorithms by the research community to deal with WSN challenges. These algorithms are selected based on problem nature or algorithm features. If problem parameters have fuzzy feature and membership function, FL method is suitable to deal with this problem. When problem size is too vast, and needs to be adapted to a particular case and with respect to time, we need to solve it using DL. GA is very efficient and stable in exploring the search space for global optimal solutions. It helps to solve general, unconstrained and bound constrained optimization problems, and it performs very well for large scale optimization problems. Both con tinuous and discrete parameters can be used [99; 100; 101]. So researchers use GA for these characteristics that help to solve a wide variety of WSN challenges. From Table 2, we can notice that PSO is used commonly in WSN. This is due to the fact that PSO has a number of distinct advantages over other optimization strategies. The PSO algorithm is a derivative-free method. It can be used in conjunction with other optimization strategies. It is less sensitive to the objective function's nature and can handle stochastic objective functions. PSO has a small number of parameters, can execute parallel computations, converges quickly, and is simple to implement [101]. Working of ABC is focussed on the behavior of bees in visiting and selecting best source of food (flowers). So, it works well in selection problems such as choosing best CHs in WSN. ABC is easy to use, available for hybridization with other algorithms [101]. As we have described above, population based meta-heuristic algorithms are used extensively because of their efficiency in providing best results. Their individuals don't use any symbolic reasoning and don't maintain a plan about the future, both of which are computationally expensive. Moreover, these techniques involve only limited memory requirements as they don't need to keep a lot of previous information. It can be robust, and maintain good quality performance in rapidly changing and diverse environments. As we notice from Figure 5 and Table 2, some AI methods such as RL. The deep reinforcement learning is also used rarely in WSN. This is because DRL faces main challenges like non-stationary environment, partial observability of the environment, continuous action spaces and is computationally expensive [102]. For enhancement of WSN, new AI algorithms as well as different strategies to embed these algorithms in WSNs have to be encouraged in future. Currently, the most of the solutions presented here used AI to solve specific challenges in some areas.