Recent Studies Utilizing Artificial Intelligence Techniques for Solving Data Collection, Aggregation and Dissemination Challenges in Wireless Sensor Networks: A Review  
[PERSON]  
1 Computer Science Department, Faculty of Computers and Artificial Intelligence, Benha University, Benha 13513, Egypt; [EMAIL_ADDRESS]  
[PERSON]  
2 Computer Science Department, University of Sharjah, Sharjah 27272, United Arab Emirates; [EMAIL_ADDRESS]  
[PERSON]  
3 Mathematics Department, Zagazig University, Zagazig 44523, Egypt; [EMAIL_ADDRESS] or [EMAIL_ADDRESS]  
[PERSON]  
5 Information Systems Department, University of Sharjah, Sharjah 27272, United Arab Emirates; [EMAIL_ADDRESS]  
[PERSON]  
1 Computer Science Department, Faculty of Computers and Artificial Intelligence, Benha University, Benha 13513, Egypt; [EMAIL_ADDRESS]  
[PERSON]  
5 Information Systems Department, University of Sharjah, Sharjah 27272, United Arab Emirates; [EMAIL_ADDRESS]  
[PERSON]  
1 Computer Science Department, Faculty of Computers and Artificial Intelligence, Benha University, Benha 13513, Egypt; [EMAIL_ADDRESS]  
[PERSON]  
5 Information Systems Department, University of Sharjah, Sharjah 27272, United Arab Emirates; aalib@sharjah. such as wind, air, humidity, pressure, vibration, detecting gases and chemicals, earthquake and so on [2; 4]. The increasing benefits of IoT applications in daily life have revolutionized people's lifestyle choices. For effective communication of data among nodes, many such IoT-based applications demand exact identification of node position and location [5]. WSN is regarded as the core component of IoT and enables a diverse set of applications [6; 7; 8; 9; 10]. The integration of WSN into the IoT allows the dynamic connection of sensor devices to the internet and achieves the tasks assigned. WSN provides valuable capabilities to applications such as in science, military, engineering, area monitoring, forest fire detection, disaster prediction and so on, but at the same time faces different challenges due to its constrained resources and capabilities [11]. The key purpose of this survey is to give insight into up-to-date applications of Artificial Intelligence (AI) methods with respect to different WSN challenges. We highlight various challenges related to Data Collection (DC), aggregation and dissemination in WSNs and present a comprehensive discussion of the recent studies that utilize various AI methods to meet specific objectives of WSN, during the span of 2010 to 2021. Then, we provide a general evaluation and comparison of different AI methods used in WSNs, which will be a guide for the research community in identifying the most adapted methods to address the different WSN challenges, and the benefits of using various AI methods for solving each of these challenges related to WSNs. In this survey, we review most of the existing AI techniques and their applications in WSNs for overcoming the DC, Aggregation and Dissemination challenges of WSNs. We primarily present an overview of these WSN challenges and a review of various AI techniques. Then, we present the applications of AI techniques for solving these challenges and enhancing WSN performance in terms of packet transfer rate, WSN lifetime, energy efficiency etc. This study will provide the reader with adequate understanding about the challenging concerns of WSN and the power of AI techniques in solving the WSN challenges. While survey papers exist on analyzing different challenges facing WSNs, most of them focused on applying AI methods to solve a particular problem, such as routing or energy usage, while others focused on solving some of the challenges faced by WSN. There has been related work that discussed or partially surveyed the literature related to AI based protocols and algorithms solving different WSN challenges. The work in this survey is different from others as our intention is to provide a contemporary survey of the more recent literature.