Informatics Publishing Limited

Nikunj Saholia ¹, Shraddha Joshi ²

Affiliations
1 Computer Engineering, Marwadi Education Foundation's Group of Institutions, IN
2 Computer Engineering Department, Marwadi Education Foundation's Group of Institutions, IN

Abstract

The study of wireless sensor networks is such a challenging task as it is necessary to gain lots of knowledge from various disciplines. Wireless sensor network is characterized by its small size that contains sensors, transceiver, power supply, battery, microcontroller and memory. Routing algorithms, addressing mechanisms, proper balance between communication, signal processing and data processing capabilities must be found to fulfill the requirements for low device complexity together with low energy consumption. These areas motivate a huge effort in research activities. In order to efficiently construct a WSN consisting of many sensor nodes, it is necessary to apply some addressing mechanisms. When low cost small area network is required, ZigBee wireless sensor network can be used. ZigBee is an IEEE 802.15.4 wireless communication standard which specifies some addressing mechanisms. Some addressing mechanisms are distributed address assignment mechanism (DAAM), stochastic addressing assignment mechanism (SAAM), long thin wireless sensor network (LT-WSN) and distributed borrowing address assignment (DIBA). In this thesis I propose to incorporate any one clustering algorithm of wireless sensor network with an addressing mechanism proposed in ZigBee standard to enhance the performance. Performance of the sensor network will be evaluated in two cases, with implementing ZigBee addressing mechanism in our clustered wireless sensor network and without using it.

Keywords

ZigBee, Addressing, DIBA, Wireless Sensor Network.

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Faran Kazi ¹, Shraddha Joshi ¹, Sahista Machchhar ¹, Krupa Mandaviya ¹

Affiliations
1 Department of Computer Engineering, MEF Group of Institutions, Rajkot-360003, IN

Abstract

Social network, online forums, blogs and various sites where people can hold conversation in the form of messages, is recently become a more valuable resource for mining in various fields like customer relationship management, public opinion tracking and other text mining entities. The knowledge obtained from these public forums is extremely valuable for marketing research companies. This paper also include information of various tools available for crawling data from online forums, sites or blogs. In first part emotional polarity for extracted text is obtained using python script. In second part combined approach using EM (Expectation Maximization) clustering and SVM classification algorithm is applied to detect weather given forum is hotspot or non-hotspot for given time window. This novel approach gives better result than previous approaches to detect hotspot forums. Among them EM gives better result instead of K-means Clustering Technique.

Keywords

SVM (Support Vector Machine), EM (Expectation Maximization), Sentiment Analysis, Hotspot Detection, K-Means.

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