IJSRSET151639 | Received: 05 December 2015 | Accepted: 09 December 2015 | November-December 2015 [(1)6: 182-184] © 2015 IJSRSET | Volume 1 | Issue 6 | Print ISSN : 2395-1990 | Online ISSN : 2394-4099 Themed Section: Engineering and Technology 182 A Survey on Prediction of Missing Sensor Data Using Association Rule Hitarth Chauhan * , Bakul Panchal Department of Information Technology, L. D. Engineering College, Ahmedabad, Gujarat, India ABSTRACT Missing values is major problem in sensor network. Currently we have many existing approach to predict missing values in stream of data. But for pre fetched existing data we can’t use such techniques. So while querying in such data will lead to wrong results. So in this paper we will try to predict such missing data in existing sensor data using association rule mining techniques. Keywords: Window Association Rule Mining, K-nearest Neighbour Estimation, WSN, Data Reduction Mechanism, Data Mining, Sensor Data I. INTRODUCTION Currently there are many applications working on sensors. Sensors are now not just limited to weather forecasting. It is now used in many mobile devices and also many health care devices also uses sensors. At every second very large amount of sensor data are gaining generated. But gathering data from senor have many hurdle. As most of the time sensors are working to track peripheral environment it also faces many weather disturbances. It may also face power failure. Because of such reasons sensor data will always have some missing values. And when we try to query such missing data then gathered results will not be accurate. So we need some mechanism to retrieve those missing data. We can always request such missing sensor data again but it will work only on data continues stream of data. For data with are already gathered this action will not work. So in this paper we will review some techniques to predict these missing data of sensor network stream and also to predicting such data from previously gathered sensor data. II. METHODS AND MATERIAL LITERATURE REVIEW A. Determining Missing Values in Dimension Incomplete Databases using Spatial-Temporal Correlation Techniques [1] In this paper author has provided technique to predict missing data of pre fetched data using association rule. This technique uses techniques like WARM (Window Association Rule Mining) and AKE (Applying K- nearest Neighbour Estimation). Querying dimension incomplete databases could lead to obtaining incomplete results. Considering this limitation this paper proposes to incorporate the above avoidance methods as a part of searching dimension incomplete databases and also proposes newer version to the existing WARM method. The advantage of the proposed approach is that the result of the user query will always have complete and accurate data. This paper proposes to the above avoidance methods as a part of searching dimension incomplete databases. The advantage of the proposed approach is that the result of the user query will always have complete and accurate data.