# WSN Clustering using Fuzzy Logic for Increase in Residual Energy

### Fuzzy Logic Tuned with BFO

The inherent synergy of fuzzy logic in WSN becomes particularly pronounced in scenarios where precise measurements are challenging to obtain. This collaboration allows for reliable data analysis and decision-making even in the face of noise and uncertainty. To realize this, in the provided fuzzy system, each input is associated with three fuzzy memberships: Low, Medium, and High. The total number of fuzzy rules may be calculated by multiplying the number of fuzzy sets in each input variable. In this case, with three fuzzy sets for each of the three input variables, the number of fuzzy rules is determined to be 27.

This study has successfully attained the optimum values of the fuzzy controller membership function by implementing the prescribed stages in the optimization of Bacterial Foraging Optimization (BFO) algorithm according on the input circumstances. In the Bacterial Foraging Optimization (BFO) algorithm, bacteria exhibit a random movement pattern as they look for their food source. This unpredictable movement contributes to the convergence of the BFO algorithm, although over a certain period of time. Figure 9: BFO-tuned fuzzy inference in clusters through an abstract illustration.

## Outcome of integrating Fuzzy logic in WSN

The suggested methodology is implemented in MATLAB R 2016a, through modular code approach to represent the WSN, composed of a total of 100 nodes. The network is partitioned into three distinct clusters to provide efficient organization and administration, leveraging the concepts of fuzzy logic in WSN. The range of residual energy (RE) values for a node is defined as [0, 1], suggesting that all RE values will be within this interval. The range of the distance from a node to the sink is determined to be [−1, 0], indicating that values for the distance from the sink will fall within this range. The range of the distance from a node to the centroid of its cluster, also known as the Distance from Cluster Centroid (DNC), is [−1, 0]. This range signifies that DNC values will be confined to this interval.

### An Overview of Steps Involved in using Our Model

• To establish a wireless sensor network (WSN) environment, a certain number of nodes, denoted as X, need to be created. Utilize the K-means clustering algorithm to partition the dataset into three distinct groups.
• A Fuzzy Inference System (FIS) file is to be created, including three inputs and three membership functions for each input. This FIS file will consist of a collection of 27 rules, which will be used to determine the appropriate output.
• The BFO technique may be used to optimize the input parameters of a Fuzzy Inference System (FIS), to realize the unchartered potential of fuzzy logic in WSN applications.

To ascertain the efficacy of the Bacterial Foraging Optimization (BFO) tuning, a comparative analysis of simulation outcomes with those of Artificial Bee Colony (ABC) tuning is conducted for three distinct geographical areas: 50m × 50m, 100m × 100m, and 200m × 200m, validating the impact of fuzzy logic in WSN. Figures 10–18, along with the corresponding result charts shown in tables 1–3, present a compilation of the results achieved in each of the three scenarios. When applying the same set of rules and testing conditions to both BFO and ABC, BFO clearly outperforms ABC, showcasing the power of fine-tuned fuzzy logic in WSN. However, the outcomes of BFO and ABC algorithms are comparable when dealing with large geographical regions. However, when operating in a constrained area, BFO beats ABC under the same rule set, WSN environment, and other conditions.

#### Case-1: Geographical area of 50m × 50m.

###### Figure 10: The cluster consists of nodes located within a geographical region of  50m × 50m. Figure11: Residual energy plot comparison. Figure12: Standard deviation of cluster distance

Table 1: Cluster-wise comparison for BFO and ABC for case-1

 Case-1:  ???×??? Cluster 1 Cluster 2 Cluster 3 BFO 8.75456456141063 13.6266384294770 26.4814014635824 ABC 8.00970696477768 11.3888740950478 23.3974753421539

#### Case-2: Geographical area of 100m × 100m Figure13: The cluster consists of nodes located within a geographical region of 100m × 100m. Figure 14: Comparison of residual energy plots of BFO and ABC. Figure15: Standard deviation of cluster distance

Table 2: Cluster-wise comparison for BFO and ABC for case-2

 Case-2:  100m × 100m Cluster 1 Cluster 2 Cluster 3 BFO 8.75456456141063 13.6266384294770 26.4814014635824 ABC 8.00970696477768 11.3888740950478 23.3974753421539

#### Case-3: Geographical area of 200m × 200m. Figure 16: The cluster consists of nodes located within a geographical region of 200m × 200m . Figure 17: Comparison of residual energy plots of BFO and ABC. Figure 18: Standard deviation of cluster distance

Table 3: Cluster-wise comparison for BFO and ABC for case-3

 Case-3: 200m × 200m Cluster 1 Cluster 2 Cluster 3 BFO 66.2532605617461 38.5725720516026 135.643533028712 ABC 88.0227760388700 28.2006211404531 75.2897030161919

## Future Scope:

We attempted to demonstrate how the integration of fuzzy logic in WSN can bring about a paradigm shift in the realm of wireless sensor networks. This model primarily emphasizes the significance of extended network lifespan which is a critical characteristic within the WSN systems. However, ensuring a more robust and protected environment for WSNs necessitates prioritizing security measures against external threats.  With the involvement of these intelligent decision making techniques, these types of attacks may be mitigated with the use of end-to-end encryption. Additionally, the use of a Guard node is applicable in scenarios when data is being transported from a higher security level to a lower security level of transmission. As the field of fuzzy logic in WSN continues to evolve, we can expect even more breakthroughs that will shape the future of smart technologies and decision-making systems.

## Reviews

There are no reviews yet.

Only logged in customers who have purchased this product may leave a review.