Optimizing Algorithms for Simulation of the Hurricane Minimizer

Hurricanes, when they occur, devastate homes and lives due to their sheer intensities. This paper presents five algorithms (1) prediction at sensor node (2) prediction at cluster head (3) leader selection (4) leader node selection and (5) controller, suitable for Emergence in Wireless Sensor Actuated Networks (WSANs) for monitoring and controlling a tropical depression through parameters such as temperature and pressure. These algorithms are suitable for use in the Hurricane Minimizer [1] project. The general idea is to use the optimal choice, which is Cluster with computation [2] wherein a small amount of information sent to the Observer. In cluster head (leader) configuration, two algorithms employed: (1) a primary sensor node that receives data from the environment and distributes it to (2) the cluster head that aggregates and distribute the data in a network. The leader algorithm will send data to the observer. In WASNs, bandwidth and power consumption are scarce commodities, which represents the life span of the network. Simulation testbed using Maple Software generates Python and Java code to test algorithms for ECOLI (Extensible Calculus of Local Interaction) [3], as algorithms are not language-dependent. Presented here are the algorithms necessary for monitoring and controlling a tropical depression, using code in Python, however comparative analysis done using Java.