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WSNs are composed of many autonomous devices using sensors that are capable of interacting, processing information, and communicating wirelessly with their neighbors. Though there are many constraints in design of WSNs, the main limitation is the energy consumption. For most applications, the WSN is inaccessible or it is unfeasible to replace the batteries of the sensor nodes makes the network power inefficient. Because of this, the lifetime of the network which has maximum operational time is also reduces. To make the network power efficient, different power saving/reduction algorithms are proposed by different authors. Some of the authors have achieved the low power consumption by modifying like encryption & decryption algorithms, Routing algorithms, Energy Efficient Algorithms, Compression and decompression algorithms, minimizing control packets, and many other different power reduction algorithms. Among many algorithms, we have chosen data compression techniques aiming different targets like memory, power & bandwidth reduction. To achieve our objectives, we have worked on Huffman coding -compression algorithm and updated the algorithm by including one's complement, XOR operations and finally named as Modified Huffman Coding. The performance of the proposed model is analyzed and it is observed that, information is maximum compressed which consumes less computational power, thereby increasing the battery life.

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Big data analytics has been the focus for large scale data processing. Machine learning and Big data has great future in prediction. Churn prediction is one of the sub domain of big data. Preventing customer attrition especially in telecom is the advantage of churn prediction.  Churn prediction is a day-to-day affair involving millions. So a solution to prevent customer attrition can save a lot. This paper propose to do comparison of three machine learning techniques Decision tree algorithm, Random Forest algorithm and Gradient Boosted tree algorithm using Apache Spark. Apache Spark is a data processing engine used in big data which provides in-memory processing so that the processing speed is higher. The analysis is made by extracting the features of the data set and training the model. Scala is a programming language that combines both object oriented and functional programming and so a powerful programming language. The analysis is implemented using Apache Spark and modelling is done using scala ML. The accuracy of Decision tree model came out as 86%, Random Forest model is 87% and Gradient Boosted tree is 85%.

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