The Deployment, Dispatch, and Packet-scheduling
Issues of Mobile Wireless Sensor Networks
The Deployment, Dispatch, and Packet-scheduling Issues of Mobile Wireless Sensor Networks
StudentYou-Chiun Wang
AdvisorYu-Chee Tseng
A Dissertation Submitted to Department of Computer Science
College of Computer Science National Chiao Tung University
in partial Fulfillment of the Requirements for the Degree of
Doctor of Philosophy in
Computer Science
October 2006
Hsinchu, Taiwan, Republic of China
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The Deployment, Dispatch, and Packet-scheduling
Issues of Mobile Wireless Sensor Networks
Student: You-Chiun Wang Advisor: Dr. Yu-Chee Tseng
Department of Computer Science
National Chiao Tung University
ABSTRACT
Wireless sensor networks have become one emerging technology that greatly enrich our
life. Such a network consists of many tiny, wireless devices that can gather information
from the environment and communicate with each other. In this dissertation, we will
study the deployment, dispatch, and packet-scheduling issues of a mobile wireless sensor
network, in which some or all nodes in the network have a mobile capability. In particular,
the deployment issue discusses how to determine the minimum number of sensors and their
locations to be placed in the region of interest so that every point in the region can be
covered by sensors and the network is connected. The dispatch issue addresses how to
efficiently schedule mobile sensors to reach certain locations to perform some missions so
that their energies can be conserved as much as possible. After the network is constructed
or mobile sensors arrive at their destinations, the packet-scheduling issue considers how
to manage the messages reported from sensors so that the delays of important real-time
messages can be bounded while other non-real-time messages will not be starved.
For the deployment issue, we first propose a general deployment solution that allows
the deployed region to be arbitrary-shaped and possibly contain obstacles. Our solution
also allows an arbitrary relationship of sensors communication distances rc and their
sensing distances rs, which is ignored by previous works. Our solution first computes
the positions to place the least number of sensors according to the condition of deployed
region and the relationship of rc and rs. Then we dispatch sensors to these locations
under certain constraints of energy consumptions. In this way, our solution can relax the
limitations of previous works and is more complete to the deployment problem.
In this dissertation, we further investigate how to deploy a sensor network for multi-
level coverage, which is an essential assumption required by many applications and proto-
cols in wireless sensor networks. For this deployment problem, we also propose a general
solution in which the relationship of rc and rs can be arbitrary. Our solution can use
iii
fewer sensors compared with other schemes. In addition, we also propose two distributed
dispatch schemes to help deploy sensors.
For the dispatch issue, we propose an efficient dispatch method for mobile sensors to
visit event locations in a hybrid sensor network. Our dispatch method is general in which
the numbers of event locations and mobile sensors can be arbitrary. Our dispatch method
can balance the moving distances of mobile sensors while preserve their energies as much
as possible during each round of dispatch. In this way, we can maximize the system time
for mobile sensors to perform their missions with their limited energies.
For the packet-scheduling issue, we propose two wireless packet fair scheduling algo-
rithms, Traffic-Dependent wireless Fair Queuing (TD-FQ) and Multi-Rate wireless Fair
Queuing (MR-FQ). TD-FQ takes traffic types of flows into account when scheduling pack-
ets. It gives a higher priority for real-time flows to alleviate their queuing delays, but still
guarantees the fairness among all flows. MR-FQ considers a more complicated multi-rate
environment in which sensors can adopt different modulation techniques to transmit their
packets under different channel conditions. MR-FQ adjusts a flows transmission rate
according to the flows channel condition and its lagging degree, so that both fairness and
system performance can be taken care of.
In this dissertation, we also implement a mobile sensor platform, called the integrated
mobile surveillance and wireless sensor (iMouse) system. The iMouse system integrates
the context-aware capability of wireless sensor network into surveillance system so that
the real critical information in the environment can be retrieved and immediately send
to users. In this way, the overheads of traditional visual surveillance systems can be
reduced. We demonstrate the iMouse system with a home/office security scenario in this
dissertation.
Keywords: connectivity, coverage, dispatch, fair queuing, mobile computing, mo-
bile sensors, network deployment, packet fair scheduling, QoS management, surveillance
applications, topology control, wireless sensor networks.
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Contents
i
Abstract iii
v
Contents vi
List of Figures x
List of Tables xv
1 Introduction 1
1.1 Background and Motivations . . . . . . . . . . . . . . . . . . . . . . . . . . 1
1.2 Contributions of the Dissertation . . . . . . . . . . . . . . . . . . . . . . . 4
1.3 Organization of the Dissertation . . . . . . . . . . . . . . . . . . . . . . . . 8
2 Preliminaries 10
2.1 Deployment Issue . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 10
2.1.1 Related Computation Geometric Problems . . . . . . . . . . . . . . 10
2.1.2 Placements of Wireless Sensor Networks . . . . . . . . . . . . . . . 13
2.1.3 Self-deployments with Mobile Sensors . . . . . . . . . . . . . . . . . 16
2.2 Dispatch Issue . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 20
2.3 Packet-scheduling Issue . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 26
2.3.1 Algorithms with Error-free Reference Models . . . . . . . . . . . . . 27
2.3.2 Algorithms with Explicit Compensation Mechanisms . . . . . . . . 29
2.3.3 Algorithms with Weight Adjustment Mechanisms . . . . . . . . . . 33
2.3.4 Algorithms that Consider Traffic Types of Flows . . . . . . . . . . . 34
2.4 Implementations of Mobile Sensor Platforms . . . . . . . . . . . . . . . . . 36
vi
3 Deployment of a Wireless Sensor Network for Single-level Coverage 41
3.1 Problem Statement . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 42
3.1.1 The Sensor Placement Problem . . . . . . . . . . . . . . . . . . . . 42
3.1.2 The Sensor Dispatch Problem . . . . . . . . . . . . . . . . . . . . . 42
3.2 Solutions to the Sensor Placement Problem . . . . . . . . . . . . . . . . . . 44
3.2.1 Partitioning the Sensing Field . . . . . . . . . . . . . . . . . . . . . 46
3.2.2 Placing Sensors in Single-row Regions . . . . . . . . . . . . . . . . . 47
3.2.3 Placing Sensors in Multi-row Regions . . . . . . . . . . . . . . . . . 47
3.2.4 Adapting to the Probabilistic Sensing Model . . . . . . . . . . . . . 50
3.3 Solutions to the Sensor Dispatch Problem . . . . . . . . . . . . . . . . . . 52
3.3.1 A Centralized Dispatch Solution . . . . . . . . . . . . . . . . . . . . 53
3.3.2 A Distributed Dispatch Solution . . . . . . . . . . . . . . . . . . . . 57
3.4 Experimental Results . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 58
3.4.1 Effectiveness of the Proposed Placement Schemes . . . . . . . . . . 58
3.4.2 Evaluations of the Proposed Dispatch Schemes . . . . . . . . . . . . 59
3.5 Summary . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 59
4 Deployment of a Wireless Sensor Network for Multi-level Coverage 63
4.1 Problem Statement . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 64
4.2 k-Coverage Sensor Placement Schemes . . . . . . . . . . . . . . . . . . . . 65
4.2.1 A Naive Duplicate Scheme . . . . . . . . . . . . . . . . . . . . . . . 65
4.2.2 An Interpolating Placement Scheme . . . . . . . . . . . . . . . . .
