Gradient calculation in sensor networks

• Published in: 2004 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS) (IEEE Cat. No.04CH37566) Vol. 2; pp. 1792 - 1795 vol.2
• Main Authors: Henderson, T.C., Grant, E.
• Format: Conference Proceeding
• Language: English
• Published: Piscataway NJ IEEE 2004
• Subjects: Applied sciences; Cities and towns; Computer architecture; Computer networks; Computer science; control theory; systems; Computer systems and distributed systems. User interface; Control theory. Systems; Exact sciences and technology; Humans; Information security; Intelligent networks; Mobile agents; Monitoring; Operating systems; Polynomials; Software; Distributed processing; Gradient; Polynomial approximation; Measurement sensor; Information processing; Quadratic approximation; Data processing; Distributed system; Sensor array; Optimization; Network operating systems
• ISBN: 9780780384637; 0780384636
• DOI: 10.1109/IROS.2004.1389656

Sensor networks are comprised of devices having the ability to communicate, compute and sense the environment. A wide range of information processing tasks has been studied for such networks, including operating systems, issues, architecture optimization, and distributed data processing. In this paper, we analyze and compare four different techniques to estimate the gradient of the function represented by the sensor samples. These include: (GA1) a simple device ID defined direction, (GA2) directional derivative, (GA3) polynomial approximation with a plane, and (GA4) polynomial approximation with a quadratic. We compare these based on density of devices per unit area, and noise in the position and sensed data. The interesting result is that GA3 significantly outperforms the other algorithms, although GA1 performs very well and is much easier to compute than the others.