Optimizing Transmission Energy By Clustering Size Using S-MAC in WSNs

A study of how cluster size affects energy use in wireless sensor networks using the S-MAC sleep protocol. It finds the best cluster size by comparing latency, power consumption and other parameters.

A dataset is collected or downloaded and cleaned: missing values, duplicates and outliers are handled. Features are selected and encoded, and the data is split into training and test sets. Several algorithms are trained and compared, and the best one is tuned. The final model runs behind a simple web form where the user enters values and gets the prediction with its confidence.

We compared a few suitable algorithms (for example logistic regression, decision tree, random forest and SVM) on the same train-test split and chose the one with the best balance of accuracy, recall and speed for this data.

Accuracy is correct predictions over all predictions. Precision is how many predicted positives are truly positive. Recall is how many actual positives were found. When classes are imbalanced, precision and recall matter more than accuracy.

Packet delivery ratio, end-to-end delay, throughput, packet loss and energy consumption, compared between the existing protocol and the proposed one.

Proactive protocols (DSDV, OLSR) keep routes to all nodes updated at all times. Reactive protocols (AODV, DSR) find a route only when data needs to be sent, which saves overhead in mobile networks.

We followed an iterative (incremental) model: requirements and a basic design first, then modules built and tested one by one, with the guide's feedback after each review. It suits a project where requirements become clearer as we build.

Unit testing of each module, integration testing of the complete flow and system testing with real inputs. Test cases with expected and actual results are listed in the report, including invalid inputs.

The current version is tested on a limited dataset or number of users. Future work includes deploying on the cloud, adding more features or languages, improving accuracy with more data and building a mobile version.