## 5 Fuzzy Logic-Based WSN Power Management and Optimization
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Mechanism** & **Objectives** & **Modeling Parameters** & **Fuzzy Logic Input Variables** & \begin{tabular}{c} **Minimize Power** \\ **Consumption Strategy** \\ \end{tabular} \\ \hline DSA [50] & Increase lifetime of the individual nodes batteries & Deadline miss ratio, deadline miss ratio desired and remaining battery capacity & Fuzzy logic system with input parameters (deadline miss ratio desired, old sampling time, and remaining battery capacity) & Timing of sleep and wakeup policy \\ \multirow{2}{*}{DPMA [51]} & \multirow{2}{*}{Increases the whole network lifecycle} & Current sampling time and the temperature value detected & Fuzzy logic system evaluates the temperature values. Input parameters: sampling time and data. Fuzzy logic controller to calculate sleeping time according to the battery level and throughput-to-workload & \begin{tabular}{c} **Minimize Power** \\ **Consumption Strategy** \\ \end{tabular} \\ \hline Power consumption reduction mechanism using fuzzy logic [52] & Improves the lifetime of WSN nodes & Sleeping time, battery level, and ratio of throughput & Fuzzy logic system with input parameters (deadline miss ratio, deadline miss ratio desired, old sampling time, and remaining battery capacity) & Timing of sleep and wakeup policy \\ \multirow{2}{*}{Distributed fuzzy logic-based sink selection algorithm [53]} & \multirow{2}{*}{Reduces the delay and improves the lifetime of WSN} & Distance, energy, and congestion & \begin{tabular}{c} **Fuzzy logic system evaluates the temperature variations. Input parameters: sampling time and data.** \\ \end{tabular} \\ \multirow{2}{*}{Distributed fuzzy logic-based sink selection algorithm [53]} & \multirow{2}{*}{Reduces the delay and improves the lifetime of WSN} & Distance, energy, and congestion & \begin{tabular}{c} **Fuzzy logic system evaluates the temperature variations. Input parameters: sampling time and data.** \\ \end{tabular} \\ \multirow{2}{*}{Fuzzy logic-based mechanism utilizing particle swarm optimization algorithm [54]} & \multirow{2}{*}{Inverses the whole network lifecycle} & \multirow{2}{*}{\
\begin{tabular}{c} **Sleeping time, battery level, and** \\ **network in industrial wireless sensor networks** \\ \end{tabular} } &
\begin{tabular}{c} **Fuzzy logic system evaluates the temperature variations. Input parameters: sampling time and data.** \\ \end{tabular} \\ \multirow{2}{*}{FLC-PSO [55]} & \multirow{2}{*}{Infinite the number of dead nodes} & \multirow{2}{*}{Remaining energy and the number of dead nodes} & Fuzzy logic input variables are remaining energy and the number of dead nodes \\ \multirow{2}{*}{A fuzzy logic-based algorithm for smart homes [56]} & \multirow{2}{*}{Overcome energy consumption in battery nodes an in IEEE 802.15A} & \multirow{2}{*}{Throughput to workload, the battery level of the device, and sleeping time} & Fuzzy logic controller with \\ \multirow{2}{*}{A fuzzy logic-based algorithm for smart homes [56]} & \multirow{2}{*}{Investor for smart homes and design a system that can be executed using off-the-shelf hardware} & \multirow{2}{*}{Throughput to workload, the battery level of the device, and sleeping time} & Fuzzy logic controller with \\ \hline \hline \end{tabular}
\end{table} 
Table 5: The main features of the surveyed fuzzy-based power management algorithms for WSNs.