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Efficient resource utilization in wireless sensor networks is an important issue. Clustering structure has an important effect on the efficient use of energy, which is one of the most critical resources. However, it is extremely vital to choose efficient and suitable cluster head (CH) elements in these structures to harness their benefits. Selecting appropriate CHs and finding optimal coefficients for each parameter of a relevant fitness function in CHs election is a non-deterministic polynomial-time (NP-hard) problem that requires additional processing. Therefore, the purpose of this paper is to propose efficient solutions to achieve the main goal by addressing the related issues.

This paper draws inspiration from three metaheuristic-based algorithms; gray wolf optimizer (GWO), incremental GWO and expanded GWO. These methods perform various complex processes very efficiently and much faster. They consist of cluster setup and data transmission phases. The first phase focuses on clusters formation and CHs election, and the second phase tries to find routes for data transmission. The CH selection is obtained using a new fitness function. This function focuses on four parameters, i.e. energy of each node, energy of its neighbors, number of neighbors and its distance from the base station.

The results obtained from the proposed methods have been compared with HEEL, EESTDC, iABC and NR-LEACH algorithms and are found to be successful using various analysis parameters. Particularly, I-HEELEx-GWO method has provided the best results.

This paper proposes three new methods to elect optimal CH that prolong the networks lifetime, save energy, improve overhead along with packet delivery ratio.

Natural disasters such as earthquake, flood and hurricane always threaten human life and societies. A major challenge is technological hazards triggered by such disasters, especially in metropolises and urban areas. Thus, these hazards have been the focus of interest in many countries, and suitable crisis management plans have been made to address them. The purpose of this study was to cluster technological hazards caused by natural disasters in urban areas.

According to literature, a set of 15 technological hazards was identified whose magnificence and interrelations were analysed using interpretive structural modelling technique. DEMATEL technique was used to determine internal relations among the hazards and to draw a network relation map.

The results revealed that dam failing, water supply disruption and building collapse form the base of the structural model.

The authors developed a structural model representing the hierarchy and interrelations among various elements of technological hazards caused by natural disaster. To the best of the authors’ knowledge, this was the first attempt to reveal internal relations of Natech factors. Finally, some recommendations were proposed for crisis management according to research findings.