Intelligent Optimization in Internet of Things Networks: A Comprehensive Review

The popularity of the Internet of Things (IoT) has boosted the creation of intelligent and interdependent systems in the fields of healthcare, home automation, industrial control, and transportation management. These environments rely on networks of densely interconnected devices that are constantly communicating in order to make informed monitoring and choices. Regardless of all these developments, mass IoT implementations have a number of major challenges, some of which are limited energy resource availability, dynamically evolving network topologies, network congestion, and security threats. These restrictions may have adverse effects on the network stability, scalability and the general efficacy of the network.

The traditional optimization approaches employed in IoT networks are often based on the heuristic and metaheuristic methods. Although these strategies offer some benefits in terms of network performance, they are usually computationally complex and demonstrate little ability to respond to dynamic and unpredictable network states. Further, the current solutions recognize individual optimization objectives individually and rarely consider energy efficiency, trust management and secure communication as part of a single intelligent system.

The current development in deep learning has brought forth new possibilities in enhancing the management of the IoT networks with adaptive and data-driven methods. The paper reports on the current trends in smart IoT optimization and outlines key gaps in current approaches. This analysis shows that there is a need to have lightweight, scalable, as well as learning-based frameworks capable of improving network performance, intensifying security systems, and maintaining reliable communication within the massive IoT context.