# Energy and Environment-Aware Path Planning in Wireless Sensor Networks with Mobile Sink
###### Abstract
Therefore, the urgent data packets should be transmitted over paths that would cross a dangerous zone, such as a fire zone, which would inevitably interrupt data delivery once the relay nodes in that zone are burned. Thus, environmental awareness is a crucial factor that needs to be considered when designing a routing algorithm in WSNs. Moreover, since urgent data packets must be delivered reliably and on time, the routing algorithm should prioritize reliable transmission while being suitable for real-time applications [14; 15]. For resolving the aforementioned problems, nature-inspired algorithms were developed. Swarm intelligence (SI) is one of such algorithms being explored to deliver effective optimization solutions for a wide variety of WSN situations because of their flexibility and adaptability in solving many complex problems. Almost all swarm intelligence optimization approaches are inspired by ant colonies, bees, and animal swarms. The ant colony optimization (ACO) approach resembles social ant colonies. In particular, ants use pheromomes as a chemical messenger, and the pheromone concentration is also used to indicate solution quality in ACO to find the ideal solution. Therefore, the problem's solution is usually related to pheromone concentration [9; 16; 17]. The work in this paper consists of three phases. The first phase is proposing an efficient means of mobile sink path planning as many researchers based their research on problems related to the path planning of the mobile sink. Most of them also tried to determine the shortest path with the least cost and time [18; 19; 20; 21; 22; 23; 24] without considering the environmental effect, which may be affected by path changes. Path planning under difficult situations is uncertain. Therefore, this paper uses environmental awareness to reduce mobile sink's environmental effect. Secondly, the consequence of the excessive routing of urgent data would negatively influence the latency and/or throughput of a high-priority data flow. Hence, the zone priority is considered when deciding on a new mobile sink location. In addition, delivery delay and distance are also considered. In the second phase, a cluster head is chosen. The proposed clustering strategy chooses cluster heads from normal sensor nodes based on energy efficiency and network reliability to improve network lifetime and throughput. It considers sensor node residual energy, wireless link quality, and intra-cluster distance. The third step is the routing algorithm. Up to our knowledge, numerous research publications have examined WSN routing problems. Most suggested solutions aimed at increasing data transmission reliability and energy efficiency [9; 25; 26]. Such studies do not address the environmental effect in routing protocol design. Therefore, they cannot adapt quickly to environmental disturbances like rainstorms and wildfires. To the best of our knowledge up to the time of writing this paper, only two routing protocols [13; 14] considered energy and environment awareness when seeking to increase routing reliability and energy efficiency under hostile situations. The problem with these algorithms is that they cannot be used in real time. So, this paper suggests a routing algorithm which tries to reduce the adverse environmental effects on data delivery and is also suitable for real-time applications. It also looks at the link quality so as not to send data packets down paths that are not reliable. Moreover, a novel function interrelates residual energy, and sensor node load is advocated to balance energy utilization. The path planning, cluster head selection, and routing problems are formulated in the form of 0/1 integer programming for other researchers to understand and reuse. Then, swarm intelligence is introduced as a heuristic solution for the three problems.