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With the advance of wireless communication technologies, small-size and high-performance computing and communication devices are increasingly used in daily life. After the success of second generation mobile system, more interest was started in wireless communications. A Mobile Ad hoc Network (MANET) is a wireless network without any fixed infrastructure or centralized control; it contains mobile nodes that are connected dynamically in an arbitrary manner. The Mobile Ad hoc Networks are essentially suitable when infrastructure is not present or difficult or costly to setup or when network setup is to be done quickly within a short period, they are very attractive for tactical communication in the military and rescue missions. They are also expected to play an important role in the civilian for as convention centers, conferences, and electronic classrooms. The clustering is an important research area in mobile ad hoc networks because it improves the performance of flexibility and scalability when network size is huge with high mobility. All mobile nodes operate on battery power; hence, the power consumption becomes an important issue in Mobile Ad hoc Network. In this article we proposed an Energy Aware Clustered-Based Multipath Routing (EACMR), which forms several clusters, finds energy aware node-disjoint multiple routes from a source to destination and increases the network life time by using optimal routes.

Prediction of rainfall is the essential problem to be solved. The variation in the rainfall is primarily attributed to its association with Humidity, Temperature, Pressure, Wind Speed and Dew Point etc. Several works have been done in this field over the past few decades. An accurate prediction of rainfall events can aid in accurate financial planning of the economy of nation. The unpredictable natural disasters like floods and droughts not only affected the economy of a country but also the lifestyle of people of the countryside. Data mining is a influential approach which helps in extracting hidden information from huge databases and allows decisions to be taken on knowledge mining basis. This paper highlights Supervised Learning in Quest (SLIQ), decision tree algorithm using Gini Index in order to predict the precipitation with an accuracy of 72.3% and is completely based on the historical data. The decision tree is constructed and the classification rules are generated.

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