By Topic

Capacity of Data Collection in Arbitrary Wireless Sensor Networks

Sign In

Cookies must be enabled to login.After enabling cookies , please use refresh or reload or ctrl+f5 on the browser for the login options.

Formats Non-Member Member
$33 $13
Learn how you can qualify for the best price for this item!
Become an IEEE Member or Subscribe to
IEEE Xplore for exclusive pricing!
close button

puzzle piece

IEEE membership options for an individual and IEEE Xplore subscriptions for an organization offer the most affordable access to essential journal articles, conference papers, standards, eBooks, and eLearning courses.

Learn more about:

IEEE membership

IEEE Xplore subscriptions

4 Author(s)
Siyuan Chen ; University of North Carolina at Charlotte, Charlotte ; Minsu Huang ; Shaojie Tang ; Yu Wang

Data collection is a fundamental function provided by wireless sensor networks. How to efficiently collect sensing data from all sensor nodes is critical to the performance of sensor networks. In this paper, we aim to understand the theoretical limits of data collection in a TDMA-based sensor network in terms of possible and achievable maximum capacity. Previously, the study of data collection capacity has concentrated on large-scale random networks. However, in most of the practical sensor applications, the sensor network is not uniformly deployed and the number of sensors may not be as huge as in theory. Therefore, it is necessary to study the capacity of data collection in an arbitrary network. In this paper, we first derive the upper and lower bounds for data collection capacity in arbitrary networks under protocol interference and disk graph models. We show that a simple BFS tree-based method can lead to order-optimal performance for any arbitrary sensor networks. We then study the capacity bounds of data collection under a general graph model, where two nearby nodes may be unable to communicate due to barriers or path fading, and discuss performance implications. Finally, we provide discussions on the design of data collection under a physical interference model or a Gaussian channel model.

Published in:

IEEE Transactions on Parallel and Distributed Systems  (Volume:23 ,  Issue: 1 )