Decision tree based data classification for marine wireless communication

Wireless data communication along with data classification techniques has got wider acceptance in various marine wireless applications. This paper exploits the power of machine learning algorithm to classify wireless communication dataset for effective decision making in marine sector. Fishing is among the most risky of professions in the world because once out on the sea, the fishermen are subject to various oceanographic conditions. The unreliable communication between the fishing fleets and to the shore is a serious problem when they face emergency situations like bad weather, border attacks, natural calamities etc. This paper is intended to develop an algorithm to determine the most influential parameters by considering signal strength, wind speed etc. which helps to track, classify and disseminate information to the fishing fleets while they are in deep sea. A decision tree based classification is proposed to find the best node based on the signal strength and the environmental conditions and the scenario has been simulated using NS2 platform. An ensemble based learning algorithm with bagging and adaptive boosting in C4.5 is also employed for improving the performance. The performance comparison has been done and the result shows that the boosted decision tree algorithm has got highest classification accuracy of 95.73%.