Channel-aware distributed classification in wireless sensor networks using binary local decisions

Mohammad Fanaei; Matthew C. Valenti; Natalia A. Schmid; Vinodkrishnan Kulathumani

doi:10.1117/12.884397

5 May 2011 Channel-aware distributed classification in wireless sensor networks using binary local decisions

Mohammad Fanaei, Matthew C. Valenti, Natalia A. Schmid, Vinodkrishnan Kulathumani

Proceedings Volume 8050, Signal Processing, Sensor Fusion, and Target Recognition XX; 80501L (2011) https://doi.org/10.1117/12.884397
Event: SPIE Defense, Security, and Sensing, 2011, Orlando, Florida, United States

Abstract

This paper considers the problem of distributed multi-hypothesis classification in the context of wireless sensor networks. The goal is to reliably classify an underlying hypothesis at a fusion center using simple localized decisions at individual sensors. The fusion-center classification must be performed despite the presence of faults in both local sensor decisions and transmission channels between the sensors and fusion center. Local sensor nodes make binary classifications based on their noisy observations and send their decisions to the fusion center through parallel additive white Gaussian noise channels. The fusion center then uses these noisy versions of local decisions to perform a global classification. In contrast with other similar approaches for multi-hypothesis classification based on combined binary decisions, our approach exploits the relationship between the influence fields of different hypotheses and the accumulated noisy versions of local binary decisions as received by the fusion center, where the influence field of a hypothesis is defined to be the spatial region in its surrounding in which it can be sensed using some specific modality. The main contribution of this paper is the formulation of local and fusion decision rules that maximize the probability of correct global classification at the fusion center, along with an algorithm for channel-aware global optimization of the local and fusion center decision thresholds. The performance of the proposed classification system is investigated through practical scenarios. Performance analysis results show that the proposed approach could simplify decision making at local sensors while achieving acceptable performance in terms of the global probability of correct classification at the fusion center.

Citation Download Citation

Mohammad Fanaei, Matthew C. Valenti, Natalia A. Schmid, and Vinodkrishnan Kulathumani "Channel-aware distributed classification in wireless sensor networks using binary local decisions", Proc. SPIE 8050, Signal Processing, Sensor Fusion, and Target Recognition XX, 80501L (5 May 2011); https://doi.org/10.1117/12.884397

ACCESS THE FULL ARTICLE

INSTITUTIONAL
Select your institution to access the SPIE Digital Library.

SELECT YOUR INSTITUTION

PERSONAL
Sign in with your SPIE account to access your personal subscriptions or to use specific features such as save to my library, sign up for alerts, save searches, etc.

PERSONAL SIGN IN

No SPIE Account? Create one

PURCHASE THIS CONTENT

SUBSCRIBE TO DIGITAL LIBRARY

50 downloads per 1-year subscription

Members: $195

Non-members: $335 ADD TO CART

25 downloads per 1 - year subscription

Members: $145

Non-members: $250 ADD TO CART

PURCHASE SINGLE ARTICLE

Includes PDF, HTML & Video, when available

Members: $17.00

Non-members: $21.00 ADD TO CART

PROCEEDINGS
12 PAGES

DOWNLOAD PAPER SAVE TO MY LIBRARY

GET CITATION

RIGHTS & PERMISSIONS

Get copyright permission Get copyright permission on Copyright Marketplace

KEYWORDS

Sensors

Binary data

Sensor networks

Signal to noise ratio

Classification systems

Sensor fusion

Environmental sensing

Show All Keywords

Keywords/Phrases

Search In:

Publication Years