In wireless sensor networks (WSNs), pinpointing the location of a sensor node is a crucial task. However, acquiring accurate spatial information about the location of a sensor node in a WSNs can be challenging, especially when the structure of the network is continually changing. And the sensor nodes are dispersed unevenly because of the topographical requirements of the environment where the communication is to be performed. To intelligently predict the position of the sensor node in our network, we opted for a Distance Vector Hop (DV-HOP) method. However, because of its sluggish convergence and propensity to settle on a locally optimal solution. The traditional DV-Hop method, which positions sensor nodes based on average hop distance, performs poorly in such dynamic situations. So, in an effort to overcome the discrepancies within the typically opted DV-Hop algorithm. In this paper, we propose two improved versions of the DV-Hop methodology: (i). Quantum Golden Jackal Optimization (QGJO) and (ii). Quantum Bullhead Shark Optimization (QBSO). Golden Jackal Optimization (GJO) is a newly developed algorithm that was inspired by the cooperative behavior of golden jackals during their natural prey-hunting activities. Nevertheless, the GJO method has a propensity to quickly become trapped in an ideal local area. In this study, we address this drawback by integrating a quantum inspiration function on GJO to interrupt when the optimal location of a sensor node in a WSNs is achieved. This reduces the positioning estimation error, and an optimal modification of the learning rate is achieved. While as the name suggests, the Bullhead shark optimization (BSO) is also developed by being inspired with the behavior of bullhead sharks in nature. However, we use a comparable path identification mechanism based on quantum inspiration functions in BSO for improved algorithmic authenticity. Which eventually maintains accuracy and resilience while when changing network circumstances are observed. And to make the results more authenticated/trustworthy we had also introduced a swarm intelligence communication function for both the QGJO and QBSO. This ultimately facilitates information flow between nodes and a position update function is introduced, which repeatedly refines the node’s position estimation. Many simulations with various test functions were run, including variations in the coverage area of the network, the number of sensor nodes observed, proportions of the beacon coverage area, and the impact of an abrupt shift in the topology of the anchor/beacon nodes owing to a change in the communication environment/habitat of a WSNs. Comparative analysis of the proposed algorithms has shown that, in the mean positioning error, QGJO-DV-HOP has an error of 16.79%, with standard deviation of 2.59%. Its performance has a 95% range of 15.66–17.93%. Conversely, the mean positioning error of 26.23% was observed in QBSO-DV-HOP with a standard deviation of 4.38%. It has a 95% confidence interval of between 24.31 and 28.14%.
Khan et al. (Sat,) studied this question.