Abstract
Advances in wireless technologies and small computing devices, wireless sensor networks can be superior technology in many applications. Energy supply constraints are one of the most critical measures because they limit the operation of the sensor network; therefore, the optimal use of node energy has always been one of the biggest challenges in wireless sensor networks. Moreover, due to the limited lifespan of nodes in WSN and energy management, increasing network life is one of the most critical challenges in WSN. In this investigation, two computational distributions are presented for a dynamic wireless sensor network; in this fog‐based system, computing load was distributed using the optimistic and blind method between fog networks. The presented method with the main four steps is called Distribution‐Map‐Transfer‐Combination (DMTC) method. Also, Fuzzy Multiple Attribute Decision‐Making (Fuzzy MADM) is used for clustering and routing network based on the presented distribution methods. Results show that the optimistic method outperformed the blind one and reduced energy consumption, especially in extensive networks; however, in small WSNs, the blind scheme resulted in an energy efficiency network. Furthermore, network growth leads optimistic WSN to save higher energy in comparison with blinded ones. Based on the results of complexity analysis, the presented optimal and blind methods are improved by 28% and 48%, respectively.
Highlights
A sensor network consists of many sensor nodes interacting strongly with the physical environment, which receives and responds to environmental information through the sensor
(i) Checking the equipment remaining energy and determining to inactivate equipment due to the depletion of energy (ii) Determining cluster heads (CHs) based on the Fuzzy Multiple Attribute Decision-Making (MADM) methods (iii) Clustering of the remaining nodes according to the shortest distance to one of the CHs (iv) Transfer of information from nodes to CHs and to access points based on radio transmission relationships that lead to energy consumption in nodes (v) If termination is not done, return the mentioned loop, i.e., check the remaining energy in the nodes and determine the inactive nodes
The number of rows of the matrix is the number of nodes N, and columns are equal to three numerical criteria of decision as follows: C1: distance between each node and access point
Summary
A sensor network consists of many sensor nodes interacting strongly with the physical environment, which receives and responds to environmental information through the sensor. The connection between these nodes is wireless. According to the data collection methods, the wireless sensor network can be divided into two categories: homogeneous sensor networks, including base stations and sensor nodes equipped with the same capabilities (e.g., computing power and capacity). Heterogeneous sensor networks have a base station (complex sensor nodes equipped with advanced processing and communication capabilities) compared to conventional sensor nodes [2]
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