Towards Sustainable and Efficient Data Transmission in Duty-Cycling Sensor Networks

The Hong Kong University of Science and Technology
Department of Computer Science and Engineering


PhD Thesis Defence


Title: "Towards Sustainable and Efficient Data Transmission in Duty-Cycling 
Sensor Networks"

By

Mr. Zhenjiang Li


Abstract

To bridge the gap between the increasing demand of deploying sustainable 
sensor networks for practical applications and the limited energy supply 
of each low-profile sensor node, recent research studies suggest operating 
sensor nodes in a duty-cycling work mode to save energy. Although the 
duty-cycling technique turns out to notably increase the lifetime of 
sensor nodes, the network lifetime can still be largely limited due to the 
unevenly distributed network traffic load in many applications. In 
addition, excessive challenges are introduced for implementing a variety 
of basic operations with the duty-cycling technique, which could 
deteriorate the performances of a series of important network services, 
like information dissemination, data acquisition, end-to-end packet 
delivery, etc. In this thesis, we aim at studying fundamental challenges, 
and further achieving a sustainable and efficient communication design in 
duty-cycling sensor networks.

We first investigate the problem of controlling node sleep intervals so as 
to achieve the min-max energy fairness to maximize the network lifetime. 
We theoretically formulate the Sleep Interval Control (SIC) problem and 
find it a convex optimization problem. By utilizing the convex property, 
we decompose the original problem and propose a distributed algorithm, 
called GDSIC. In GDSIC, sensor nodes can tune sleep intervals through a 
local information exchange such that the maximum energy consumption rate 
in the network approaches to be minimized. After balancing the 
network-wide energy consumption, we further optimize the data collection 
service in duty-cycling networks. We propose a novel approach for 
collecting the network-wide data. The routing structure of data collection 
is additively updated with the movement of the user. With this approach, 
we only perform a local modification to update the routing structure while 
the routing performance is bounded and controlled compared to the optimal 
performance. Next, although the routing structure can be efficiently 
constructed, the routing structure formation process itself cannot 
completely ensure the system QoS in data transmissions. Due to limitations 
of the duty-cycling operation and interference, not all data transmissions 
tasks can be guaranteed to be scheduled within required delay deadlines. 
We thus investigate the multi-task schedulability problem to determine the 
maximum number of tasks that can be scheduled within their deadlines. We 
formulate the multi-task schedulability problem, prove its NP-Hardness, 
and propose an approximate algorithm. We further extend the proposed 
algorithm by explicitly altering duty cycles of certain sensor nodes so as 
to fully support applications with stringent delay requirements to 
accomplish all tasks. Finally, time synchronization is required to support 
many duty-cycling protocols and applications. We propose a novel 
synchronization approach called FLIGHT, which leverages the fact that the 
fluorescent light intensity changes with a stable period that equals half 
of the alternating current's. By tuning to the light emitted from indoor 
fluorescent lamps, FLIGHT can intelligently extract the light period 
information and achieve network wide time calibration by referring to such 
a common time reference. FLIGHT can achieve tightly synchronized time with 
low energy consumption. In addition, FLIGHT does not occupy radio for the 
synchronization, which is greatly beneficial for a large number of indoor 
applications in duty-cycling sensor networks.


Date:			Tuesday, 14 August 2012

Time:			3:00pm – 5:00pm

Venue:			Room 3501
 			Lifts 25/26

Chairman:		Prof. Allen Moy (MATH)

Committee Members:	Prof. Yunhao Liu (Supervisor)
 			Prof. Cunsheng Ding
 			Prof. Lionel Ni
 			Prof. Yang Xiang (MATH)
                         Prof. Bin Xiao (Computing, PolyU)


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