# Improved Dragonfly Optimization Algorithm for Detecting IoT Outlier Sensors
###### Abstract  
Things receive digital intelligence by being connected to the Internet and by adding sensors. With the use of real-time data and this intelligence, things may communicate with one another autonomously. The environment surrounding us will become more intelligent and reactive, merging the digital and physical worlds thanks to the Internet of things (IoT). In this paper, an optimal methodology has been proposed for distinguishing outlier sensors of the Internet of things based on a developed design of a dragonfly optimization technique. Here, a modified structure of the dragonfly optimization algorithm is utilized for optimal area coverage and energy consumption reduction. This paper uses four parameters to evaluate its efficiency: the minimum number of nodes in the coverage area, the lifetime of the network, including the time interval from the start of the first node to the shutdown time of the first node, and the network power. The results of the suggested method are compared with those of some other published methods. The results show that by increasing the number of steps, the energy of the live nodes will eventually run out and turn off. In the LEACH method, after 350 steps, the RED-LEACH method, after 750 steps, and the GSA-based method, after 915 steps, the nodes start shutting down, which occurs after 1227 steps for the proposed method. This means that the nodes are turned off later. Simulations indicate that the suggested method achieves better results than the other examined techniques according to the provided performance parameters. Internet of things; sensor detection; improved dragonfly optimization algorithm +
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0000-0001-9991-4000]M.M. The location and proper communication of sensors are essential in sensor-based computer networks to perform optimal tasks. In addition, location information is helpful in geographic routing and joint signal processing [7]. Using different methods, researchers try to choose the best location for sensors so that service delivery in sensor-based networks can be adequately carried out [8]. One of the suitable approaches to removing network outlier sensors is the use of location knowledge. However, in the meantime, a sensor or sensors with incorrect placement may challenge the network [9]. Such sensors will be called outlier sensors in this article. Location is the best place to establish service providers' facilities, and they are also trying to reduce lost demand by rationally allocating the demand centers to them. Sensor positioning is also a subset of these issues [10]. The placement of sensors in sensor-based networks seems impractical due to the nature of such networks; in addition to many sensors, their position is inaccessible in some applications [11]. Although the global locating system may be utilized to determine the location of sensors, its use is not always possible, in addition to it being expensive [12]. Therefore, alternative methods should be used. The Internet of things is a subset of computer networks that have recently gained popularity. In this network, a set of sensors is placed in a defined workspace, providing services by establishing communication [13]. In the meantime, a sensor with a lousy placement may challenge the network. Technological advances in low-power integrated circuits have made it possible to build low-cost as well as small-sized sensor nodes and create wireless sensor networks by connecting them. Sensor nodes can sense, process, and send data [14]. The significant discrepancy between communication networks and these sensor networks is their data-centric nature and minimal energy as well as processing resources. In a sensor network, many small nodes autonomously monitor and interact with the environment. These sensors take information from the environment and transfer it to a data collection center [15]. One of the characteristics of these sensors is independence and operation without human intervention.