## 11 Conclusions and Future Research Recommendations  
The problem of wireless network congestion is a major obstacle for wireless communication networks. Wireless sensor networks (WSNs) are gradually becoming congested as Internet of Things (IoT) devices proliferate in networks. When the data demand of Internet of Things (IoT) devices exceeds the capacity of the network, wireless sensor networks (WSNs) become congested. This leads to inefficient use of resources and risks network outages. Therefore, researchers continue to develop effective algorithms that can reduce congestion. The data requirements of devices on wireless networks have led to a number of solutions. However, the introduction of state-of-the-art artificial intelligence (AI) solutions offers a potentially promising future. In wireless sensor networks (WSNs), many artificial intelligence techniques have been recognized for their potential use in managing congestion for data-intensive Internet of Things (IoT) devices, both at the wireless node and tower infrastructure levels. Additionally, the scope and implementation criteria are identified. By using various techniques, including congestion prediction, network infrastructure optimization, resource allocation optimization, and interference control, the aforementioned methods are able to successfully reduce the problems caused by congestion in wireless sensor networks (WSNs). The application of machine learning (ML), deep learning (DL), artificial intelligence (AI), artificial neural networks (ANNs), deep neural networks (DNNs), and game theoretic approaches can reduce congestion, increase throughput, improving quality of service (QoS), improving overall performance, enhancing user experience, and increasing capacity in densely populated wireless sensor networks (WSNs) based on the Internet of Things (IoT). In the quest for better congestion minimization in WSNs, there are several research directions, some of which include:  
1. In extremely dense WSNs, dynamic wireless nodes with changing topologies and placements predominate. The development of congestion mitigation algorithms must take this dynamic nature into account. These network requirements should be addressed through efficient congestion mitigation techniques. 2. Numerous wireless sensor nodes with different capabilities, data requirements, and communication protocols form extremely dense wireless IoT sensor networks. Managing traffic congestion in a highly complex environment is a challenging topic for scientists and is, therefore, a well-known research area. 3. There are often constraints on the power, memory, and bandwidth of wireless nodes. These limitations must be prioritized in newly developed congestion mitigation methods used in IoT. 4. IoT devices that process sensitive data are found in highly populated WSNs. These devices face security and privacy threats due to congestion. In addition, these devicesneed to collect sensitive and personal information in order to use new approaches for congestion mitigation. When using congestion mitigation approaches, it becomes increasingly important to ensure the security and privacy of IoT-sensitive data. In this situation, the distributed learning approach known as federated learning can protect sensitive data. * WSNs must be able to process data and make decisions for many IoT devices in real time. Giving these high-priority devices an immediate reprieve requires innovative approaches to eliminate congestion. Striking a balance between reducing latency and successfully managing congestion is a difficult task. Congestion reduction in WSNs presents a great opportunity for hybrid AI algorithms that combine established conventional and AI-based emergent algorithms. Combining two or more AI techniques, such as prediction and learning or optimization and learning, leads to a variety of hybrid algorithms. This area should be further explored [129,130]. While there are many potential future opportunities to reduce congestion in WSNs, only a few have been highlighted above.