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Top1. Introduction
Wireless Sensor Networks (WSN) is composed of a large number of inexpensive micro sensor nodes deployed in the monitoring area, it can cooperatively sense, collect and transmit the information of the perceived objects in the network coverage area. Wireless sensor networks have broad application prospects, including: military reconnaissance and monitoring, emergency disaster relief,signal acquisition and processing in areas that cannot or should not enter, monitoring of large equipment, disease monitoring and ambulance in medical fields. In monitoring and tracking the target, routing based on the position information, load balancing of the network, the network topology, and other applications (Peng Y and Wang D, 2001), these are required to know the network node own position in advance, the position information is applied in the communication and collaboration process, and these applications are completed. Thus, the wireless sensor network positioning technology is the foundation of the entire network, which achieve a variety of functions.
Sensing and data aggregation capabilities of wireless sensor networks (WSNs) depends on efficient deployment of sensor nodes (SNs) in an area. In a large surveillance space, there is a need for more SNs to cover important crucial events despite of the optimum coverage. An event-based efficient deployment algorithm (EEDA) was proposed for relocation of redundant sensors to the event location to achieve full coverage (Kaushik A,Yakkali R T, et al., 2019). They divide the deployment region into small square cells that allows individual cells to be efficiently monitored, instead of considering the whole scenario as one unit. EEDA ensures efficient coverage of the entire deployment region and senses the occurrence of any static or dynamic event with an optimum number of sensors. Early death of cluster heads (CHs) located near the sink due to excessive data relay load causes energy holes in wireless sensor networks (WSNs). A widely adopted solution to energy hole problem is to divide the deployment region into multiple sub regions and use mobile sink (MS) to aggregate data from each sub-region. However, inside a sub-region, CHs close to MS dissipate their energy quickly and die despite of the sink mobility. The problem of distributing data relay load optimally is mapped to multiple CHs and locating MS near these multiple CHs using metaheuristic algorithm biogeography-based optimization (BBO) (Kaushik A, Y, et al., 2019). Furthermore, there is a need of optimum routing of data to the MS inside each sub-region of a MS WSN. Mobile sink distributed load routing algorithm (MSDR-BBO) selects the optimum routing CHs in MS WSN as per data transfer requirements of sensor nodes (SNs) and CHs.
Existing node localization algorithm can be divided into two categories in the different locating ways (Swami A, et al., 2007), there are algorithm based on the distance (range-based) and algorithm without distance (range-free). In positioning algorithm without ranging, DV-Hop (distance vector-hop) algorithm is one of the typical positioning algorithm, it is one of the distributed localization methods, which are proposed in advantage of distance vector routing and positioning beacon nodes, the method is simple, and there is good scalability (Niculescu D and Nath B, 2001;Niculescu D and Nath B, 2003), but it is the use of jumping distance instead of straight line distance, ideal positioning effect is achieved only in the isotropic dense network.
SDP (Semi - definite Programming algorithm) was proposed to solve the wireless sensor network node localization algorithm in Literature (Zhu L, et al.,2016; Wang Z, et al.,2008;Shamsi S, et al.,2013;So A M C, Ye Y Y, 2006; Pong T. K., Tseng P., 2011)., The semidefinite programming (SDP) approach to localization was pioneered by Doherty et al (Doherty L., et al., 2001). In this algorithm, geometric constraints between nodes are represented as linear matrix inequalities (LMIs). Once all the constraints in the network are expressed in this form, the LMIs can be combined to form a single semidefinite program. This is solved to produce a bounding region for each node, which Doherty et al simplify to be a bounding box. Unfortunately, not all geometric constraints can be expressed as LMIs. In general, only constraints that form convex regions are amenable to representation as an LMI. Thus, angle of arrival data can be represented as a triangle and hop count data can be represented as a circle, but precise range data cannot be conveniently epresented, since rings cannot be expressed as convex constraints. This inability to accommodate precise range data may prove to be a significant drawback.