A hybrid Harrison Hawk optimization based on differential evolution for the node localization problem in IoT networks

Abstract

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.

A Study on Wireless Sensor Node Localization and Target Tracking Based on Improved Locust Algorithm

“…As the spacing between individuals gradually increased,𝑠is gradually not possible to generate community forces. Therefore, the algorithm restricts the position of individuals in the population to between [1,4]. If all individuals are in their comfort zone, the position remains constant.…”

“…To acquire the location information of these environmental data, it is essential to solve the self-localization problem of sensor nodes first. Node localization refers to the technical means by which the sensor nodes collaborate to determine their position coordinates [4]. With the position information of nodes, target tracking, movement trajectory prediction, etc.…”