Comprehensive Analysis of Congestion Control Techniques for WSNs

Since Wireless Sensor Networks (WSNs) have limited resources for data processing, data storage, transmission capacity, and most crucially energy supply, congestion is one of the pervasive problems for WSNs. Over the past fifteen years, numerous survey operations have been conducted to look into and remedy a variety of congestion-related concerns and challenges. The majority of review articles that were published in the literature focused on traditional methods of congestion area management, such as resource-based, traffic-based, and hybrid methods. In this survey article, an effort has been made to provide a systematic evaluation of recent initiatives that have helped to improve the congestion control methodology in WSNs by taking into consideration traditional procedures, machine learning based approaches and nature inspired techniques. A comparative analysis of these techniques with other approaches has also been done to provide a clarity regarding the performance of the algorithms. In this paper, we have tried to highlight the key areas of the application of the algorithms along with their advantages and limitations.