Advances in wireless systems have encouraged the growth and improvement of tiny, low-cost, efficient, and multi-tasking smart sensors. Wireless sensor networks are employed for sensing, collecting data and information, analysing it, and sending it to the main center to be considered quickly. During the performance, a wide range of issues appear, and the existence of some restrictions on the movement of sensors, reliable data collection mechanisms and transferring them properly and securely to the main center, and issues in the sensor network topology. In fact, all traditional methods need more ability to deal with and solve these situations. In this regard, the authors decided to highlight the importance and role of artificial intelligence technologies in designing wireless sensor networks by proposing a hybrid model that helps to construct a more practical sensor network and solve all the situations it faces. These techniques significantly improve network performance, extend battery life, develop smart nodes, and improve network functionality.
TopIntroduction
Artificial intelligence techniques are characterized by the growth of approaches that simulate the intellectual and interactive capabilities of the human brain in facing challenges (Aggarwal et al., 2022; Paul et al., 2021). Artificial intelligence is significantly concentrating on one dream is the human mind (Peprah et al., 2020; Jarrahi,2018; Wireko et al., 2021; Shukur, et al., 2023). These techniques depend on their job on the concept of multi-agent systems. This system consists of intelligent systems called agents that can be executed as a software program, as an active computer, or as a robotic machine. The agents in the system interact with each other to systematize a proper situation for them and exchange knowledge and information safely and soundly. Smart sensor networks are distributed in a way that can be noticed from a multi-factor perspective that contains sensor nodes that work collectively in an organized manner (Gou, et al., 2020; Khan et al., 2022; Dhillon and Kalra, 2018). Its primary function is to gather data from the environment in which it lives. In other words, wireless sensor networks are multi-agent systems as well as artificial communities with an intelligent ability to absorb data of any size and transmit it to the main center or receive it from the same center as it works through sensor nodes (Shin and Lee, 2020; Ramesh et al., 2021).
Artificial intelligence techniques execute mechanisms within the borrowing device to enable it to interact with the systems that collect data from it, as a group of agents is designed and devised and interacts between them in a stable and updated manner (Mijwil et al., 2022; Osamy et al.,2022; Vanitha et al., 2022; Sharma et al., 2021; Priyanka et al., 2021; Majid et al., 2022). Through artificial intelligence techniques, algorithms can be utilized and executed to enhance the role of the wireless sensor network and enable it to work in an intelligent and autonomous manner at the same time. Moreover, these networks must be distributed in an effective way to achieve the critical goals of the environments in which they live because the data collection process requires borrowing devices that are distributed in an organized manner and work individually, with the presence of artificial intelligence techniques that contribute particularly to data transfer (Chandnani and Khairnar, 2022; Srinivasulu et al.,2022). A sensor network (SN) is a procedure consisting of tiny devices called sensor nodes whose primary function is to monitor a specific environment and record all cases and conditions that occur (for instance, temperature, wind direction, humidity, chemical concentrations, the spread of viruses and pollutants, and many others) in different locations and send them immediately to the concerned centers. Moreover, they are fundamental devices in that their components are tiny, have excellent processing capabilities, and work as a group in collecting and transmitting data.