Abstract:
A wireless sensor network (WSN) consists of small sensor nodes, a base station, and a data center. Its goal is to collect and monitor a variety of environmental or system parameters such as humidity, temperature, air quality, and others. However, as the use of wireless sensor networks technology grows, WSN confronts a number of issues. One of the key issues that WSNs encounter is the sensor nodes’ limited energy resources. Because of this constraint, energy efficiency becomes a significant concern, and finding strategies to optimize energy use becomes essential. Furthermore, wireless sensor network routing methods can be complex and require sophisticated algorithms to maintain successful communication between nodes. The concept of smart cities introduces a new layer of challenges for WSNs. Smart cities integrate various systems and components to enhance the living environment for citizens. However, this integration introduces challenges such as managing and providing services to users in the smart city environment, ensuring the security of smart city networks, and dealing with the high mobility of nodes within the network. Designing a comprehensive solution for smart cities involves addressing several other critical factors, including data security, efficient data handling, heterogeneity (the presence of diverse devices and technologies), sustainability, and analysis of the vast amounts of data generated by the smart city infrastructure. To tackle these complex issues, researchers have turned to nature-inspired optimization solutions. These solutions draw inspiration from nature, which has proven to be a reliable source for addressing complex problems. Nature-inspired optimization techniques leverage the principles observed in natural systems and phenomena to develop efficient algorithms and protocols. Nature-inspired optimization techniques can be used in the design of routing protocols for wireless sensor networks, for example. These protocols aim to optimize the utilization of limited network resources while ensuring reliable and efficient data transmission between sensor nodes. Another area of focus is load balancing in high-speed networks. Load balancing techniques distribute the network traffic evenly across nodes to prevent congestion and maximize network performance. Nature-inspired optimization methods can be used to develop intelligent load balancing algorithms that adapt to dynamic network conditions. By leveraging nature-inspired optimization solutions, researchers can address the intricate and evolving challenges posed by smart cities and complex network systems. These solutions pave the way for the development of intelligent systems and novel optimization techniques, providing valuable insights and opportunities for new researchers to explore the potential of nature-inspired approaches.
