مقاله انگلیسی رایگان در مورد پیاده سازی الگوریتم محلی بهبود یافته مبتنی بر PSO – تیلور و فرانسیس 2018

Nowadays, internet of things (IoT) for information sharing plays a vital role for improving the day-to-day life. In IoT, the multi-functional wireless sensors are installed in several applications. Wireless sensor network (WSN) is comprised of enormous amount of low-priced wireless nodes enabled with sensors [1,2]. These intelligent sensors can detect, compute, and communicate via suitable sensor technology. Ease of deployment and small price of sensors make WSN suitable for lots of applications like: health care, transportation, smart building, military affairs, reconnaissance, and environmental monitoring [3–6]. The forest fire forecast system based on WSN is shown in Figure 1. For such applications, the physical locations of sensors are needed to detect the events in monitored area. So, the node’s location information in WSN is crucial and needed to develop context aware applications. Hence, position estimation (localization) of nodes is a key issue in WSNs. The location assessment by adding global positioning system (GPS) to every wireless node is the easiest manner [7,8]. Lots of applications involve large number of nodes; thus usage of GPS in every node is costly as well as consumes more energy. Hence, such constraints suggest that use of GPS in localization is not feasible in sensor networks. Therefore, suitable localization algorithms are required. Various localization schemes for sensor network have been used for position estimation of wireless nodes [9,10]. Based on range measurement, localization methods are partitioned into range-based and range-free schemes. Range-based schemes exploit absolute distance or direction info among neighbor node for the unknown nodes’ location estimation. To determine the distance or direction, the subsequent measurement procedures have been utilized like: received signal strength indicator, time or time difference of arrival, and angle of arrival [11,12]. These schemes use added hardware, thus making it more costly. Conversely, range-free schemes do not require distance or orientation info. [13–15]. Thus, they do not need added expensive hardware, which makes it cost efficient and a good choice over range-based schemes. Because of ranging errors, range-free methods give less precise results as compared to ranging methods, which is, they be able to assure various applications requisites.