IEEE Access (Jan 2023)
GFCO: A Genetic Fuzzy-Logic Channel Optimization Approach for LR-WPAN
Abstract
The objective of IEEE 802.15.4 standard is to establish the foundation for a low-rate wireless personal area network that focuses on ubiquitous communication between devices while maintaining a reasonable data rate. Its popularity has increased significantly as a result of its implementation at low power and cheap cost, and the need to improve its performance has become a necessity. The most persistent issues are throughput, packet delivery ratio (PDR), packet loss ratio (PLR), and packet delay (PD). The advances in wireless technology place a strong emphasis on overcoming these problems. To accomplish stated goals, GFCO: A genetic fuzzy-logic approach to optimize channel of IEEE 802.15.4 LR-WPAN is proposed. It employs the $Fuzzy Logic Controller$ (FLC), and the $Genetic Algorithm$ (GA), by doing so, GA optimally modifies the FLC. For this, five algorithms are presented, Algorithm-1: GFCO for LR-WPAN, Algorithm-2: $GA_{1}$ for GFCO, Algorithm-3: $FLC_{1}$ for GFCO, Algorithm-4: $GA_{2}$ for GFCO, and Algorithm-5: $FLC_{2}$ for GFCO. The suggested GFCO approach is assessed for $Random Exponential Backoff$ (REB) algorithm, which was chosen as a fundamental algorithm, along with the $Survivability~Aware~Channel~Allocation$ (SACA) algorithm, taken as a benchmark study. Two scenarios are implemented in NS-3.20 in conjunction with fuzzylite in a hospital environment. First scenario is implemented in randomly deployed 10 sensors on a person’s body ( $2\times 2\,\,m^{2}$ area), whereas second scenario is implemented in $20\times 20\,\,m^{2}$ area of a ward in hospital having 10 to 50 persons. The simulated outcomes of both scenarios were recorded for REB, SACA, and GFCO. Simulated testing demonstrated that the proposed GFCO greatly enhanced performance of throughput 15.11%, SR 3.11%, PLR 3.11%, and PD 5.52% on average for scenario-I, whereas throughput 12.06%, SR 9.0%, PLR 9.0%, and PD 2.23% on average for scenario-II, as compared to SACA. Following that, these results are used to calculate the throughput, PDR, PLR, and PD and to draw a graphical representation. The proposed GFCO technique significantly improved efficiency, according to the results of the simulated testing.
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