# Energy and Environment-Aware Path Planning in Wireless Sensor Networks with Mobile Sink
###### Abstract
Therefore, a mobile sink was proposed as one solution to the energy consumption issue in WSNs. The mobile sink may travel near the monitored area events through a planned path, which refers to scheduling the mobile sink's movement and collecting the needed data from sensor nodes. Finding the ideal path for the mobile sink involves collecting data from sensor nodes [10]. Minimizing nodes' distance from the mobile sink certainly conserves energy; it reduces multi-hop communication and addresses hotspot problems, extending network life. The battery is the mobile sink's primary power source in most applications. Thus, saving energy is crucial to a mobile sink's lifespan. The mobile sink's movement is a key source of energy consumption and costs. Most WSN path planning algorithms try to decrease energy usage to extend the lifetime of the mobile sink. It is important to design an energy-efficient path planning method [11]. The WSNs deployment environment may be heterogeneous. So, the monitored field is divided into small parts called zones, and a certain value is assigned to each zone indicating its priority (importance), namely, its zone priority [12]. The messages coming from sensor nodes located in the high-priority zones are considered urgent data. As urgent data are considered high-priority data, they must be transmitted immediately to the sink node. Suppose such messages occur in an exceptionally high-priority zone that the mobile sink has not visited yet or has visited already. In that case, they should be delivered promptly through a routing technique. Therefore, the mobile sink should move first to the high-priority zones to avoid excessive routing of urgent data. Consequently, urgent data delivery delay and the failure rate can be minimized; the urgent data packets can be delivered directly from the cluster heads that gathered them from their members to the sink node without a routing process. Hence, considering the zone priority in the path planning of the mobile sink can improve the latency and/or throughput of a high-priority data flow. Moreover, it can improve network lifetime by saving the consumed energy during the routing process. Another challenging issue is that environmental conditions also influence the WSN performance since it is commonly installed in hostile environments, exposing sensor nodes and sink nodes to failure. Thus, the network seldom works. For instance, sensor nodesand sink nodes are more likely to fail at high temperatures and might be entirely burnt out. Moreover, their short-circuit probability would be raised at extremely high humidity [13; 14]. Thus, reducing network performance consequences such as temperature and humidity is important. Therefore, the sink node should be prevented from moving through dangerous areas (for example, areas located in a harsh environment). If the mobile sink crosses a risky zone, such as a fire zone, data transmission in the whole network will be cut off due to burned sink node. Consequently, environmental awareness should be considered when designing the mobile sink's path planning algorithm. Urgent real-time data must be delivered to the mobile sink in real-time (deadline). In this scenario, timely data transmission guarantees that the appropriate actions are made, whereas late data delivery affects the action's efficacy. Therefore, this issue has to be considered in constructing the path planning of the mobile sink [14; 15]. Suppose urgent messages such as sudden emergency events occur in sub-areas where the mobile sink has not yet been visited. In that case, such urgent messages should be delivered immediately to the mobile sink through an efficient routing technique. Since the WSN's functioning relies heavily on the battery life of its sensor nodes, developing a routing algorithm that balances energy usage and energy awareness is a big challenge. In addition, as described above, it is important to reduce the effects of environmental parameters (e.g., humidity and temperature) on network efficiency.