# A Review of Machine Learning-Based Routing Protocols for Wireless Sensor Network Lifetime +
## 7 Open Issues
Because anomaly detection methods vary depending on the application, picking an algorithm to use in heterogeneous WSNs is no easy feat. The detection algorithm must meet the requirements of detection speed and accuracy. In terms of routing, the majority of the currently available routing techniques were designed to gather data from a singular source and send them to a singular destination. Packet collision is a potential issue in WSNs that have numerous sources and destinations. A growing area of interest in WSN research is the development of cooperative routing protocols that do not cause collisions when there are several sources and targets. The nodes' locations in mobile WSNs are subject to constant alteration. The nodes in a WSN can move around for many reasons that are outside their control. Building routing protocols that can adapt to the ever-changing network is, thus, essential. _Data aggregation:_ The majority of academics are concerned with approaches for data aggregation in WSNs where the sensor nodes have a uniform data rate. When it comes to WSNs, additional investigation into sensor nodes with inconsistent data rates is necessary. With nonuniform sensor nodes, data collecting becomes more complicated in mobile WSNs. The mobile sink has the potential to enhance data collection and energy efficiency. It is difficult to schedule the mobile sink in WSNs when the data are not uniform. Consideration of energy efficiency, scalability, and low cost are the primary objectives of the efficient data aggregation process using mobile sinks. _Congestion control and avoidance:_ Congestion control and avoidance methods need to be resilient and incredibly resistant to internal disturbances, external stimuli, and data loss in order to keep up with the ever-changing nature of WSN operations. Because of the energy and memory limitations of WSNs, it is necessary to minimize the transmission rate between nodes and implement simple congestion management techniques at each level. When it comes to autonomous and decentralized tactics, WSNs need a way to control and avoid congestion that is quick, efficient, and effective. It is recommended that congestion control implement a self-learning strategy to adapt to the ever-changing network conditions. In order for a network to self-adopt a congestion control method, it must react by adding or removing nodes when congestion is observed. To prevent network congestion, traffic estimation methods are required to detect a quick and dynamic route change. Instead of transferring data across nodes, effective mobile agent approaches are needed to collect data from sensor nodes [42; 43]. _Energy harvesting:_ Wireless sensor networks rely on small, low-power sensor nodes dispersed across the environment and powered by batteries. The sensor nodes have limited energy resources, so a tiny, cheap, and highly efficient wireless harvesting system (WHS) is needed to keep the network running for a long period. Wireless sensor networks (WSNs) require effective protocols for wireless energy harvesting across multiple layers. There has to be an ML-based reevaluation of the dependability of routing protocols since most current ones are energy efficient. There needs to be a system in place for large-scale sensor networks that can charge and discharge themselves in response to environmental changes. To extend the life of the network, effective methods of distributing power are required. As a result, adjusting the duty cycles requires synchronization between the physical layer's power control and the MAC layer [43; 44]. _Quality of service:_ Providing what users and applications need should be the primary focus of quality of service in WSNs. Varied needs (e.g., sensor type, data rate, traffic management methods, data kinds) and applications for WSNs necessitate varied quality-of-service criteria. Consequently, it is difficult to define the standards of quality of service for various requirements or applications. It is possible to enhance the QoS by creating efficient cross-layer protocols.