# Energy and Environment-Aware Path Planning in Wireless Sensor Networks with Mobile Sink
## 2 Related Work
For efficient data collection, the authors of [22] investigated clustering algorithms using mobile sink nodes. They use a modified LEACH approach as the foundation for their clustering. ACO also determines the most direct route for a mobile sink to go to cluster heads and then return to its starting location. Reduced data loss and increased network longevity were the goals. A clustered data gathering technique for mobile sink nodes is presented by the authors of [23]. In this algorithm, the entire network is split into grids or cells with equal size. To balance the energies of the sensor nodes, the cluster head is chosen depending on the remaining node energy. Moreover, the chosen cluster head should be close to the grid centroid. To maximize network data gathering and establish the shortest path to each mobile sink, an artificial bee colony technique is used. Reference [24] suggests a WSN routing method based on ACO with mobile sinks (EARP). The WSN multi-sink cluster paradigm is offered by this energy-saving technology. The cluster head, where ACO is used to calculate the optimum route for the mobile sink, is decided by residual energy and distance from sensor nodes. The authors suggested a trustworthy technique for effective energy. However, after examining earlier studies [18; 19; 20; 21; 22; 23; 24], it is clear that they have the following limitations:  
* They ignore environmental consequences. Environmental factors may cause sensor and sink nodes to fail, causing the network to malfunction. Neglecting this challenge might make them unable to adapt to environmental changes like wildfires and rain-storms, impairing network performance. Few research publications have explored the external environment's detrimental influence on data fusion and delivery [13; 14]. Unfortunately, up to our knowledge, no research paper has studied the data collection problem of WSNs with mobile sinks under harsh environmental conditions;
* They ignored zone priority. Messages coming from sensor nodes located in high-priority zones should be transmitted immediately to the sink node. Hence, ignoring zone priority could increase urgent data delivery delay and failure rate, which negatively affects the latency and/or throughput of a high-priority data flow;
* They overlook the dependability of data transmission. WSNs depend on reliable data transfer. Ignoring such concerns might increase packet loss, waste energy, and delay packet retransmission. This clearly impacts network efficiency;
* They miss real-time data transmission. Real-time delivery of data is difficult in WSNs. Transmission of crucial real-time data within a predetermined deadline enables prompt action, whereas late delivery reduces the efficacy of the action done. Ignoring this problem might increase missed deadlines and packet drops. Table 1 provides an overall comparison of the algorithms mentioned earlier [18; 19; 20; 21; 22; 23; 24] and the proposed ones. With regard to the cluster-based routing problem for inter-cluster communication, many academics have investigated this problem, where each cluster head assigns a relay node to forward collected data to the sink node [27; 28; 29]. In the related protocols [27; 28], each cluster head picks its next-hop relay node using a cost function to reduce and balance sensor node energy consumption. Cost function incorporates energy efficiency, residual energy, and distance. The prior research of similar algorithms demonstrates that they have limitations since they ignore critical issues. The first concern is the environmental effect; disregarding it will instantly cut off WSN's data delivery. Second, they do not account for lossy links caused by fading and interference; ignoring such a problem might increase data loss, retransmission delay, and energy waste. Finally, they cannot achieve real-time communications; therefore, they cannot send data packets before the deadline. As has been said, timely data supply ensures relevant actions are made, whereas late data delivery reduces the efficacy of such activities.