Summary Despite the close of a tumultuous 2020 and the start of 2021, connected devices will continue to shape the future of numerous industries, and businesses are confident that the Internet of Things (IoT) will play a key role in the future success of their trade. The growing Internet of Things (IoT) is connecting devices to a variety of sensors, applications, and other IoT elements to automate business processes and support human efficiencies in business and the home. WSN along with node localization algorithms can play a critical role in IoT applications. Nevertheless, in IoT applications, the context of real‐time location‐based services is gaining an overwhelming interest. To do this, several approaches are proposed in the recent literature based mainly on computational intelligence algorithms. This paper proposes a node localization algorithm based on swarm intelligence algorithms, that is, a hybrid Harris Hawks optimization based on differential evolution (HHODE).HHODE algorithm relies on Euclidian Distance as objective function to evaluate best‐fit coordinates of sensor nodes in a wireless sensor network. Moreover, several experimentations are performed depending on the network size, communication range of sensors, geographical distribution, and the beacon nodes' density to demonstrate the efficiency of the HHODE algorithm. Compared to Standard DE, HOO, PSO, and Bat Algorithm, HHODE shows higher performance with regard to node localization.

For the last decade, there has been an intensive research development in the area of wireless sensor networks (WSN). This is mainly due to their growing interest in several applications of the Internet of Things (IoT). Several issues are thus discussed such as node localization, a capability that is highly desirable for performance evaluation in monitoring applications. The localization aim is to look for precise geographical positions of sensors. Recently, swarm intelligence techniques are suggested to deal with localization challenge and localization is seen as an optimization problem. In this article, an Enhanced Fruit Fly Optimization Algorithm (EFFOA) is proposed to solve the localization. EFFOA has a strong capacity to calculate the position of the unknown nodes and converges iteratively to the best solution. Distributing and exploiting nodes is a chief challenge to testing the scalability performance. the EFFOA is simulated under variant studies and scenarios. in addition, a comparative experimental study proves that EFFOA outperforms some of the well-known optimization algorithms.

Recently, Node localization has played a vital role in wireless sensor networks (WSN), where the node's coordinates have mostly required before sensing operation. Sensor devices are randomly deployed in the network for monitoring task. The crucial objective of localization is to determine the position of the sensor nodes based on a few landmark nodes. Node localization problem is formulated as an optimization problem and metaheuristic techniques are called to deal with the issue in multidimensional space, in this paper, the Whale Optimization Algorithm (WOA) is proposed to deal with the challenge, furthermore, the capability of auto localization is highly requested. WOA computes iteratively the node's positions based on a multistage approach to browse the whole region of the network. Deploying this algorithm on a large WSN with hundreds of sensors shows good performance in terms of node localization. the simulation result proved the good performance of WOA in term of localization error and computing time . CCS CONCEPTSCCS → Networks → Network types → Wireless access networks.