Predictive Duty Cycling of Radios and Cameras using Augmented Sensing in Wireless Camera Networks

<p>Energy efficiency dominates practically every aspect of the design of wireless camera networks (WCNs), and duty cycling of radios and cameras is an important tool for achieving high energy efficiencies. However, duty cycling in WCNs is made complex by the camera nodes having to anticipate the arrival of the objects in their field-of-view. What adds to this complexity is the fact that radio duty cycling and camera duty cycling are tightly coupled notions in WCNs.</p> <p>Abstract In this dissertation, we present a predictive framework to provide camera nodes with an ability to anticipate the arrival of an object in the field-of-view of their cameras. This allows a predictive adaption of network parameters simultaneously in multiple layers. Such anticipatory approach is made possible by enabling each camera node in the network to track an object beyond its direct sensing range and to adapt network parameters in multiple layers before the arrival of the object in its sensing range. The proposed framework exploits a single spare bit in the MAC header of the 802.15.4 protocol for creating this beyond-the-sensing-rage capability for the camera nodes. In this manner, our proposed approach for notifying the nodes about the current state of the object location entails no additional communication overhead. Our experimental evaluations based on large-scale simulations as well as an Imote2-based wireless camera network demonstrate that the proposed predictive adaptation approach, while providing comparable application-level performance, significantly reduces energy consumption compared to the approaches addressing only a single layer adaptation or those with reactive adaptation.</p>

Name thesis:degree_name

Doctor of Philosophy (PhD)

Level thesis:degree_level

Dissertation

Discipline thesis:degree_discipline

Electrical and Computer Engineering

Year

2013

Author dc:creator

• Shin, Joonhwa

Contributors dc:contributor

• Avinash C. Kak
• Johnny Park
• Xiaojun Lin
• Saurabh Bagchi

• applied sciences
• duty cycle
• predictive network adaptation
• wireless camera networks
• radios
• augmented sensing
• Computer Engineering
• Computer Sciences

Repository record dc:identifier

https://docs.lib.purdue.edu/open_access_dissertations/59

OAI identifier oai:identifier

oai:docs.lib.purdue.edu:open_access_dissertations-1157