Recover the Missing Data in IoT by Edge Analytics

Jodi lakshmi*, B. Lalitha**
*PG Scholar, Jawaharlal Nehru Technological University Anantapur, Andhra Pradesh, India.
**Assistant Professor, Department of Computer Science and Engineering, Jawaharlal Nehru Technological University Anantapur, Andhra Pradesh, India.
Periodicity:October - December'2018


Due to advancements in information technology, the Internet of Things (IoT) has been emerging as the next big move in our daily lives. The IOT is rapidly transforming into a highly heterogeneous ecosystem that provides interoperability among different types of devices and communication technologies. The proposed system for recovery of incomplete sensed data by using IOT. So, to recognize and identify all the data automatically IoT requires new solutions for the different physical objects into a global ecosystem. IOT applications collect huge amount of data from all connected sensors. IOT recovers the missing data from IOT sensors by utilizing data from related sensors. To recover missing data an algorithm MapR Edge is introduced. MapR Edge more powerful clustering algorithm which has the ability to send data back to cloud for a faster and more significant data. In this project only three nodes are being used where automatically computations are performed at the sensor, where each sensor is connected independently to the cloud. Whenever the data crosses its destiny value at the nodes, that particular data will be sent to the cloud server. Missing values can be estimated from neighboring nodes.


IoT (Internet of Things), Sensors, GPRS Technology, MapR Edge Clustering Algorithm

How to Cite this Article?

Lakshmi, J., Lalitha, B. (2018). Recover the Missing Data in IoT by Edge Analytics, i-manager's Journal on Software Engineering, 13(2), 25-28.


[1]. Allison, P. D. (2002). Missing Data. Thousand Oaks, CA: Sage.
[2]. Cool, A. L. (2000). A review of methods for dealing with missing data. Annual Meeting of the Southwest Educational Research Association.
[3]. Halatchev, M., & Gruenwald; L. (2005, January). Estimating missing values in related sensor data streams. Int'l Conf. on Management of Data (pp. 83-94).
[4]. Jover, R. P. (2015). Security and impact of the IoT on LTE mobile networks. In Security and Privacy in the Internet ofThings (IoT): Models, Algorithms, and Implementations. New York: Taylor & Francis LLC, CRC Press.
[5]. Kulakov, A., & Davcev, D. (2005, April). Tracking of unusual events in wireless sensor networks based on artificial neural-networks algorithms. In Proceedings. ITCC 2005 International Conference on Information Technology: Coding and Computing (pp. 534-539). IEEE.
[6]. Kune, D. F., Koelndorfer, J., Hopper, N., & Kim, Y. (2012, February). Location leaks on the GSM air interface. ISOC NDSS.
[7]. Maksymyuk, T., Dumych, S., Brych, M., Satria, D., & Jo, M. (2017, January). An IoT based monitoring framework for software defined 5G mobile networks. In Proceedings of the 11th International Conference on Ubiquitous Information Management and Communication (p. 105). ACM.
[8]. Manyika, J., Chui, M., Bisson, P., Woetzel, J., Dobbs, R., Bughin, J., & Aharon, D. (2015). The Internet of Things: Mapping the value beyond the hype. McKinsey Global Institute, McKinsey & Company.
[9]. MAPR Technologies, Inc. (2017). MAPR Edge for the Internet of Things [White paper]. Retrieved from mapr-edge-for-iot/assets/ mapr-edge-whitepaper.pdf
[10]. Minerva, R., Biru, A., & Rotondi, D. (2015). Towards a definition of the Internet of Things (IoT). IEEE Internet Initiative, 1, 1-86.
[11]. Taouil, R., Pasquier, N., Bastide, Y., & Lakhal, L. (2000, February). Mining bases for association rules using closed sets. In ICDE'2000 International Conference (p. 307).
[12]. Troyanskaya, O., Cantor, M., Sherlock, G., Brown, P., Hastie, T., Tibshirani, R., ... & Altman, R. B. (2001). Missing value estimation methods for DNA microarrays. Bioinformatics, 17(6), 520-525.
[13]. Why MapR? (2019). Retrieved from why-mapr/
[14]. Williams, D., Liao, X., Xue, Y., & Carin, L. (2005, August). Incomplete-data classification using logistic regression. In Proceedings of the 22nd International Conference on Machine learning (pp. 972-979). ACM.

Purchase Instant Access

Single Article

North Americas,UK,
Middle East,Europe
India Rest of world
Pdf 35 35 200 20
Online 35 35 200 15
Pdf & Online 35 35 400 25

If you have access to this article please login to view the article or kindly login to purchase the article
Options for accessing this content:
  • If you would like institutional access to this content, please recommend the title to your librarian.
    Library Recommendation Form
  • If you already have i-manager's user account: Login above and proceed to purchase the article.
  • New Users: Please register, then proceed to purchase the article.