Abstract Localization in wireless sensor networks (WSNs) is used to determine the coordinates of the sensor nodes deployed in the sensing field. It is the process that determines the location of the target nodes relative to the location of deployed anchor nodes. These anchor nodes are deployed at known locations having GPS installed in them. However, mostly in all 3D applications, the area under observation may have a complexity in the sensing environment. In this work, a modified learning enthusiasm‐based teaching learning based optimization algorithm (LebTLBO) is proposed to deal with the 3D localization problem using single anchor and moving target nodes in anisotropic network with DOI 0.01. LebTLBO is a metaheuristic inspired by the classroom teaching and learning method of teaching learning based optimization algorithm. An improved LebTLBO algorithm aims to achieve enhanced performance by balancing the exploration and exploitation capabilities of conventional LebTLBO to improve its global performance. On the CEC2019 benchmark functions, the suggested technique is assessed, and computational findings show that it provides promising outcomes over other competitive algorithms. Also, mLebTLBO outperforms well in terms of localization error in 3D environment. The proposed technique is useful to cope up in case of rescue operations.

Localisation has become a major attraction of research in recent years in the field of wireless sensor networks (WSNs). It is required for various applications like monitoring of objects placed in indoors and outdoors environments. The main requirement in localisation is to assign a location to each node, since multiple sensor nodes in WSN are used to retrieve information. The aim of this research is to address a WSN localisation problem using various optimisation techniques. The concept of single anchor node placement at the centre of sensing field with its projection using hexagonal pattern is introduced. In this study, a novel hybrid optimisation technique named as dragonfly–firefly algorithm (DA–FA) is proposed. DA is an optimisation algorithm recently suggested based on the dragonfly's static and dynamic swarming behaviour. The suggested hybrid technique combines the exploration capability of explore DA and Firefly algorithm's to exploit to obtain ideal global solutions. To check the effectiveness of DA–FA CEC 2019 benchmark functions are used for comparison with competitive algorithms. DA–FA converges fast and provide optimum solution for most of the benchmark functions. In addition, DA–FA outperforms well in terms of localisation error in comparison to existing localisation solutions.

A study is made of several methods for reducing complex fault tree models of fault-tolerant distributed systems. For each method the authors provide bounds on the estimate of unreliability that is obtained from the reduced model. They discuss methods for truncating the solution of a model expressed as a fault tree and then develop techniques that apply to the construction of the fault tree model. The emphasis is on producing approximate (but bounded) results applicable to realistic systems. The authors also discuss methods for incorporating dynamic system behavior (error handling and redundancy management) into fault tree models, and the corresponding truncated solution. The methods are presented as they are used in modeling two distributed systems, the Cm* system and AIPS (the Advanced Information Processing System).>