# Fuzzy Logic and Bio-Inspired Firefly Algorithm Based Routing Scheme in Intrabody Nanonetworks
## 2 Related Work
### Bio-Inspired Based Schemes
\begin{table}
\begin{tabular}{p{113.8 pt} p{113.8 pt} p{113.8 pt}} \hline \hline
**Protocol Name** & **Purpose of Using FLBDS** & **Impact on Performance** \\ \hline FuzzyLogic-Based Energy Optimized Routing for WSNs [23]. & Optimize next-hop selection considering the energy of the next-hop and closeness to the shortest path and the sink. & Extended network lifetime, energy efficiency and energy balance. \\ \hline Cluster-head Election using Fuzzy Logic for WSNs [24]. & Selection of cluster heads on the basis of energy and concentration. & A significant increase in network lifetime. \\ \hline Multi-Sensor Data Fusion in Cluster-based WSNs Using Fuzzy Logic Method [25]. & Data processing and fusion is carried out using fuzzy rule-based system. Cluster heads process collected information using the fuzzy rule-based system. & Reduce false alarm rate by improving the reliability and accuracy of the sensed information. \\ \hline A Fuzzy Logic-Based Clustering Algorithm for WSN to Extend the Network Lifetime [26]. & Appropriate cluster head selection based on residual energy, base station mobility and cluster centrality. & Improved network lifetime and stability. \\ \hline Cluster Head Selection in WSNs under Fuzzy Environment [27]. & Cluster head selection on the basis of residual energy, neighbors density, and the distance. & Prolonged network lifetime. \\ \hline Swarm intelligence based fuzzy routing protocol for clustered WSNs [28]. & Balanced clustering of nodes and efficient selection of cluster head based on residual energy, distance, and cluster centroid. & Support heterogeneous applications, improved network lifetime and balanced cluster generation. \\ \hline Improving on LEACH Protocol of WSNs Using Fuzzy Logic [29]. & Sink calculates the chance of each node to become a cluster head. & Make a better selection of cluster head for energy saving. \\ \hline CHEF Cluster Head Election mechanism using Fuzzy logic in WSNs [30]. & Optimize cluster head selection based on the residual energy and local distance. & Prolonged network lifetime \\ \hline Fuzzy-Logic-Based Clustering Approach for WSNs Using Energy Prediction [31]. & Improved cluster head selection on the basis of its residual energy and expected energy for the next round. & Energy efficiency \\ \hline Energy Efficient Cross-Layer Routing Protocol in WSNs Based on Fuzzy Logic [32]. & Fuzzy logic-based next-hop routing decision. & Maximize network lifetime \\ \hline A clustering routing protocol for WSNs based on type-2 fuzzy logic and ACO [33]. & Fuzzy logic-based cluster head selection based on residual energy, neighbor nodes density and distance. & Improved load balancing and network lifetime \\ \hline An energy-aware fuzzy approach to unequal clustering in WSNs [34]. & Using fuzzy logic approach to handle uncertainties in cluster-head radius estimation. & Energy-efficient data aggregation \\ \hline MOPCA: Multi-objective fuzzy clustering algorithm for WSNs [35]. & Energy-based fuzzy competition is carried out for cluster head nomination. & Prolonged network lifetime \\ \hline \hline \end{tabular}
\end{table} 
Table 1: The impact of employing a Fuzzy Logic-Based Decision System (FLBDS) in improving performance of Wireless Sensor Networks (WSNs). In the WSNs, firefly algorithm has also been adapted for handling different problems such as node localization [51; 52], energy-efficient data collection [53] and cluster head selection [54; 55; 56]. From the contributions brought by the firefly algorithm in WSNs, we comprehend that the incorporation of a bio-inspired firefly algorithm in IBNNs ensures a higher probability of achieving low-complexity energy-efficient data routing. In Figure 1, we have further emphasized the impact of the firefly algorithm in improved WSNs performance that is further paving the way for its adaptation in IBNNs.