Energy-Aware Object Tracking Sensor Networks
Yingqi Xu
Department of Computer Science and Engineering
Pennsylvania State University
University Park, PA 16802
E-Mail: yixu@cse.psu.edu
Advisor: Wang-Chien Lee
Abstract
and aggregation. These concepts are not simply juxtaposed, but fitting into each other to justify an integrated research topic.
In order to fully realize the potential of sensor networks , energy awareness should be incorporated into
every stage of the network design and operation. As
a representative application of sensor networks, object
tracking sensor network faces many system and design challenges in terms of energy management. By
studying the role of semantic location in object tracking queries, data dissemination and sensing data storage, we propose a power-aware networking paradigm
involving sensor nodes, networks protocols and application softwares for object tracking sensor networks.
1
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Prior Research
In order to realize energy-awareness in object tracking
sensor networks, many issues from different aspects
need to be addressed and integrated. These issues
include:
• data storage model, which defines the logical
schema for data storage and distribution,
• data dissemination model, which describes
the mechanisms information (including sensing
data and query) flows in the network, and
• data model and query language, which provides the interface between users and the physical
world.
The above models are interrelated. For example, the
distribution of the sensing data directly affects the efficiency and effectiveness of queries; on the other hand,
the way users query the network plays important roles
in defining the data schema. The Models should also
satisfy the requirements of energy consumption, computational complexity, and other aspects of object
tracking (e.g., latency and precision of detection).
Many groups and researchers have been working on
these topics in the context of general sensor networks.
Three data storage models, namely Data-centric storage, Local storage and External storage are discussed
in [3, 4]. They claim that using naming data in data
storage and communication can significantly enhance
the lifetime of sensor networks.
Directed diffusion proposed in [2] is a userinitiated data dissemination protocol. By incorporating energy-aware techniques, such as data-centric dissemination, reinforcement-based adaptation, and innetwork data aggregation and caching, directed diffusion enables queries to extract the desired data with
minimum number of transmissions, thus reducing the
Introduction
The rapid development of wireless technologies and
computer-embedded devices has made it possible for
people to monitor, control and interact with the physical world via sensor networks. While holding promise
in a wide variety of applications, sensor networks are
driven by extremely frugal battery resources, which
necessitates the network design and operation to be
done in an energy-aware manner. Object tracking sensor network, as a distinctive application of sensor networks, is widely used for military area intrusion detection and for wildlife animal monitoring. Because it
involves a large amount of cooperation among sensors,
object tracking sensor network provides significant research opportunities in terms of energy management.
In order to maximize the lifetime of sensor networks,
the system needs aggressive energy optimization techniques, ensuring that energy awareness is incorporated
not only into individual sensor nodes but also into
groups of cooperating nodes and into an entire sensor
network. Based on the remarks, we plan to investigate the nature of the data flow in object tracking
sensor networks, and focus on providing an energyefficient data management model for these networks.
This involves query processing, network communication, sensor node cooperation, and data flow diffusion
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power consumption. Two-tier Data Dissemination
Model(TTDD) proposed by Ye et al. in [6] takes
into account the mobility and scalability of sensor
networks. As a source-initiated data dissemination
model, TTDD handles the ”handoff” problem of mobile sinks in a power-efficient way by avoiding to flood
the network with the location information of the moving sink.
Prediction-based monitoring paradigm (PREMON)
[1] investigates the problem of sensing data aggregations by exploring temporal and spatial relationships
among sensor readings. The authors claim that sensors in close proximity are likely to have correlated
readings. By predicting the future readings at a sensor based on given reading history and knowledge of
surrounding sensors, PREMON reduces the number
of transmissions at the cost of more receptions.
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abstracting detection areas. With the abstraction of
the physical world, the data model that describes the
way data are stored, accessed, and operated is to be
explored. After establishing logical schema for sensor
data storage and a data model for applications, how
the location information directs data flow inside the
object tracking sensor network will be studied. The
ultimate goal of our research is to build up a locationoriented data management model, which incorporates
both user-initiated and sensor-initiated data diffusion
and adapts to object behaviors and environmental
changes.
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Conclusion
Through our study of data management and communication issues in object tracking sensor networks, we
believe that semantic location may provide rich information for directing data dissemination and extracting data of sensors in an power-efficient way. This
inspires us to propose a semantic location-based data
model for object tracking sensor networks in order to
achieve network-wide energy optimization. We envisage that this original idea may lead to new findings that significantly enhance the lifetime and performance of object tracking sensor networks.
Proposed Research
Many other related mechanisms exist, but limited
space does not permit a thorough survey. To the
best knowledge of the authors, no existing study addresses the research issues of data management in object tracking sensor networks. For example, due to
the simplicity of the temporal and spatial prediction
models, PREMON is more suitable for simple sensor networks monitoring than object tracking. We
pointed out its deficiency and proposed a Dual Prediction approach for object tracking sensor networks
in a previous work [5]. Furthermore, existing work in
object tracking sensor networks uses IP-address, geometric location of sensor nodes, or description of tasks
as keys for queries and data diffusion, but semantic locations1 have never been investigated as the predicates
of queries. We believe incorporating semantic location
information into data management model may become
beneficial to energy consumption and network performance.
As a next step to our previous study, we plan
to investigate adaptive prediction models for object
tracking sensor networks and how to intelligently and
dynamically switch between various prediction models based on different movement patterns of mobile
objects and application requirements. Moreover, to
achieve power-aware prediction, we will identify and
exploit the various performance-energy tradeoffs.
Furthermore, we will study the role of the semantic
location in object tracking sensor networks. For incorporating semantic location into sensor data management, an appropriate location model is necessary for
References
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ACM Computer Communication Review, 31(5), October 2001.
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[3] B. Krishnamachari, D. Estrin, and S. Wicker. Modelling data-centric routing in wireless sensor networks.
[4] S. Ratnasamy, D. Estrin, R. Govindan, B. Karp,
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[5] Y. Xu and W. C. Lee. On localized prediction for power
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[6] F. Ye, H. Luo, J. Cheng, S. Lu, and L. Zhang. A twotier data dissemination model for large-scale wireless
sensor networks. In Proceedings of the eighth annual
international conference on Mobile computing and networking. ACM Press, 2002.
1 semantic locations is not the exact geometric location of
individual sensor nodes, but a application specific area which
covers a set of sensor nodes
2