SEARCH WITHIN CONTENT
Citation Information : International Journal on Smart Sensing and Intelligent Systems. Volume 1, Issue 4, Pages 912-921, DOI: https://doi.org/10.21307/ijssis-2017-327
License : (CC BY-NC-ND 4.0)
Published Online: 02-November-2017
With the availability of low-cost sensor nodes there have been many standards developed to integrate and network these nodes
to form a reliable network allowing many different types of hardware vendors to coexist. Most of these solutions however have
aimed at industry-specific interoperability but not the size of the sensor network and the large amount of data which is collected
in course of its lifetime. In this paper we use well studied data compression algorithms which optimize on bringing down the
data redundancy which is related to correlated sensor readings and using a probability model to efficiently compress data at the
cluster heads. As in the case of sensor networks the data reliability goes down as the network resource depletes and these types
of networks lacks any central synchronization making it even more a global problem to compare different reading at the central
coordinator. The complexity of calibrating each sensor and using an adaptable measured threshold to correct the reading from
sensors is a severe drain in terms of network resources and energy consumption. In this paper we separate the task of comparative
global analysis to a central coordinator and use a reference PMax which is a normalized probability of individual source which
reflects the current lifetime reliability of the sensors calculated at the cluster heads which then is compared with the current global
reliability index based on all the PMax of cluster heads. As this implementation does not need any synchronization at the local
nodes it uses compress once and stamp locally without any threshold such as application specific calibration values (30oC) and
the summarization can be application independent making it more a sensor network reliability index and using it independent of
the actual measured values.
 Introduction to Data compression. Khalid Sayood. 2nd Edition, Morgan Kaufmann Series in Multimedia Information and Systems (Hardcover)
 Software Stack Architecture for Self-Organizing Sensor Networks, Vasanth Iyer, G.Rama Murthy and M.B. Srinivas- ICST 2007, Palmerston North New Zealand.
 Battery drain (http : //www.techlib.com/reference/batteries.html)
 Power Law math (http : //en.wikipedia.org/wiki/Power law).
 Environmental measurement OS for a Tiny CRF-STACK Used in Wireless Network. Vasanth Iyer, G.Rama Murthy and M.B. Srinivas- Sensors & Transducers Journal-April 2008, ISSN 1726-5479 2006 by IFSA.
 Distributed Wireless Sensor Network Architecture: Fuzzy Logic based Sensor Fusion, G.Rama Murthy, Vasanth Iyer. ISBN 978-80-7368-387-0 pages 71-78 VOL II, Proceedings of the 5th EUSFLAT Conference, Ostrava, Czech Republic, 2007.
 Min Loading Max Reusability Fusion Classifiers for Sensor Data Model, Vasanth Iyer, G.Rama Murthy and M.B. Srinivas, 2008 IEEE SENSORCOMM,August 25, 2008 - Cap Esterel, France.
 B.Krishnamachari, S.S. Iyengar, IEEE.,Distributed Bayesian Algorithms for Fault-Tolerant Event Region Detection in WSN, MARCH 2004.
 Vasanth Iyer, Garimella Rammurthy and M.B. Srinivas., Training Data Compression Algorithms and Reliability in Large Wireless Sensor Networks. IEEE International Workshop on Embedded Processors, Sensors, and Actuators (EPSA-2008) to be held in Taichung, Taiwan, June 11-13, 2008.