## 1 Introduction
Therefore, this issue has to be considered in constructing the routing paths. Another challenging issue in WSN is the reliability of data transmission, which is the data delivery guarantees reaching its destination. Due to wireless communication's inherent nature in sensor networks, packet losses are quite inevitable. Wireless connections are prone to network disturbances due to some environmental parameters, such as interference and fading. This would increase the retransmission possibility of lost packets and thus causes more energy consumption and more delivery delay. Therefore, the quality of wireless links is another factor that needs to be considered when designing the routing technique for WSNs [7,8]. For resolving the problems mentioned above, swarm intelligence (SI) approaches are developed 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. The pheromone concentration indicates solution quality in ACO to find the ideal solution. Usually, the problem is connected to pheromone concentration [19,20]. The work in this paper consists of two phases. The first phase is the selection of the optimal sink. The first phase is proposing an efficient sink node selection strategy, as many researchers based their research on problems related to optimal sink selection. Most of them also tried to determine the optimal sink based on nodes' energy and the transmission energy consumption [21,22] without considering the environmental effect, which may be affected by path changes. Sink node selection under difficult situations is uncertain. Therefore, this paper uses environmental awareness to reduce the sink's environmental effect. Secondly, the delivery delay of real-time data packets would negatively influence the real-time data flow's latency and/or throughput. Thirdly, when choosing the appropriate sink algorithm to reduce the packet miss ratio, it is vital to balance the energy costs against the advantages of achieving the delivery time (percentage of delivered packets that missed their deadline). Last, the sink load metric is proposed to maximize network lifetime by balancing all sink nodes' energy consumption. The second phase is the routing algorithm, up to our knowledge; numerous research publications have examined the routing problem in multi-sink WSNs. Most suggested solutions aim to increase energy efficiency [5,6,23,24,25,26,27,28,29]. Such studies do not address the environmental effect of routing protocol design. Therefore, they can not adapt quickly to environmental disturbances such as rainstroms and wildfires. To the best of our knowledge, up to the time of writing this paper, only one routing protocol [15] considered energy besides environment awareness to increase routing reliability and energy efficiency under hostile situations. The problem with such an algorithm is that it cannot be used in real-time. So, this paper suggests a routing algorithm that 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 that it does not send data packets down paths that are not reliable. Additionally, a novel function that interrelates residual energy and sensor node load is advocated to balance energy utilization. The major contributions are as follows:  
1. Proposing an energy-efficient, reliably environmentally aware for a real-time routing for multi-sink WSNs considering the environmental data, link quality, delay, energy, and load balancing. 2. Describing the routing problem in large-scale multi-sink WSN in the form of 0/1 integer programming. 3. The routing problem is formulated in the form of 0/1 integer programming for other researchers to understand and reuse. 4. Swarm intelligence is introduced as a heuristic solution.