AdaM: An adaptive monitoring framework for sampling and filtering on IoT devices

Real-time data processing while the velocity and volume of data generated keep increasing, as well as, energy-efficiency are great challenges of big data streaming which have transitioned to the Internet of Things (IoT) realm. In this paper, we introduce AdaM, a lightweight adaptive monitoring framework for smart battery-powered IoT devices with limited processing capabilities. AdaM, inexpensively and in place dynamically adapts the monitoring intensity and the amount of data disseminated through the network based on the current evolution and variability of the metric stream. Results on real-world testbeds, show that AdaM achieves a balance between efficiency and accuracy. Specifically, AdaM is capable of reducing data volume by 74%, energy consumption by at least 71%, while preserving a greater than 89% accuracy.

[1]  Arun Kejariwal,et al.  A Novel Technique for Long-Term Anomaly Detection in the Cloud , 2014, HotCloud.

[2]  Marios D. Dikaiakos,et al.  JCatascopia: Monitoring Elastically Adaptive Applications in the Cloud , 2014, 2014 14th IEEE/ACM International Symposium on Cluster, Cloud and Grid Computing.

[3]  Nick Roussopoulos,et al.  Compressing historical information in sensor networks , 2004, SIGMOD '04.

[4]  Marimuthu Palaniswami,et al.  Internet of Things (IoT): A vision, architectural elements, and future directions , 2012, Future Gener. Comput. Syst..

[5]  Jennifer Widom,et al.  Adaptive filters for continuous queries over distributed data streams , 2003, SIGMOD '03.

[6]  Chi Harold Liu,et al.  The Emerging Internet of Things Marketplace From an Industrial Perspective: A Survey , 2015, IEEE Transactions on Emerging Topics in Computing.

[7]  Margaret Martonosi,et al.  Wattch: a framework for architectural-level power analysis and optimizations , 2000, Proceedings of 27th International Symposium on Computer Architecture (IEEE Cat. No.RS00201).

[8]  James Brusey,et al.  Edge Mining the Internet of Things , 2013, IEEE Sensors Journal.

[9]  Matt Zwolenski,et al.  The Digital Universe , 2014, Journal of Telecommunications and the Digital Economy.

[10]  Li Xiong,et al.  Real-time aggregate monitoring with differential privacy , 2012, CIKM.

[11]  T. Başar,et al.  A New Approach to Linear Filtering and Prediction Problems , 2001 .

[12]  Suman Nath,et al.  Differentially private aggregation of distributed time-series with transformation and encryption , 2010, SIGMOD Conference.

[13]  Matti Siekkinen,et al.  A System-Level Model for Runtime Power Estimation on Mobile Devices , 2010, 2010 IEEE/ACM Int'l Conference on Green Computing and Communications & Int'l Conference on Cyber, Physical and Social Computing.

[14]  Christos Faloutsos,et al.  RainMon: an integrated approach to mining bursty timeseries monitoring data , 2012, KDD.

[15]  Kevin M. Carter,et al.  Probabilistic reasoning for streaming anomaly detection , 2012, 2012 IEEE Statistical Signal Processing Workshop (SSP).

[16]  Shicong Meng,et al.  Enhanced Monitoring-as-a-Service for Effective Cloud Management , 2013, IEEE Transactions on Computers.

[17]  Lida Xu,et al.  The internet of things: a survey , 2014, Information Systems Frontiers.

[18]  George Pavlou,et al.  Monitoring, aggregation and filtering for efficient management of virtual networks , 2011, 2011 7th International Conference on Network and Service Management.

[19]  Wanlei Zhou,et al.  Low-Rate DDoS Attacks Detection and Traceback by Using New Information Metrics , 2011, IEEE Transactions on Information Forensics and Security.