The research is motivated with the background of an increasingly pervasive
sensing world today. Being the key role in the applications of pervasive computing,
sensor networks have made ubiquitous sensing, ad-hoc networking, automatic computing
and strategic communication possible and effective. The term, diffusing event,
summarizes the common characteristic of a series of environmental problems such as
fire, typhoon, flood and fume diffusion. Being a flexible reactive network, the sensor
network serves as the best choice of monitoring diffusing events and sketching the
diffusion trend in the real time. Therefore, the diffusing-event monitoring problem is
researched based on sensor networks. The major contributions are as follows:
Firstly, the data processing architecture is designed and a data discrimination
framework is presented. Specifically, in the node scope, the data processing architecture
is designed to support dynamic correlation discovery and adaptive data sampling.
The experiments on real-world datasets have proved the energy consistency and data
accuracy of the architecture. In the network scope, in order to reduce the influence of
sensor data uncertainty, a systematic discrimination framework is presented to partition
the raw data set into event, error and ordinary subsets through node-level temporal
processing, neighbor-level spatial processing, cluster-level ranking and network-
level decision fusion. The experiment has increased the distinction ratio to as
high as 97% with the error occurrence rate up to 50% in the network. Compared with
traditional event/anomaly detection, the proposed framework can considerably reduce
false-alarm rate and keep miss-hit rate at an acceptable low level.
Secondly, diffusing events are modeled. A TSEC conceptual model is presented
to completely express the temporal, spatial and event-related correlations. Based on
TSEC model and the domain knowledge of diffusing events, association modeling is
presented, including the establishment of homogeneous/heterogeneous data correlation
models, the event-abstraction model and their associations. This work is the first
to integrate the temporal, spatial and event relations into the same conceptual model,
and the first to apply such a conceptual model into the diffusing-event monitoring in
sensor networks.
Thirdly, the general process of diffusing-event monitoring is analyzed, the corresponding algorithms are presented for efficient monitoring, and both theoretical
analysis and case studies are proposed. Based on the TSEC model, the general monitoring
process is analyzed and the available solutions are proposed for the key points
in the general process. Following the general process, a series of window-based
in-network cooperation algorithms are presented for diffusing-event monitoring, involving
the linear regression on sensor nodes, the intra-/inter-cluster information exchanging
for event-boundary detection and the judgment of diffusion trend. The experiments
on real-world datasets have demonstrated the energy efficiency, report reliability
and scalability of the proposed algorithms. The theoretical analyses on the
comparison of single-source vs. multi-source diffusion and pure diffusion vs. mixed
diffusion have indicated that the basic diffusing-event monitoring strategies can be
easily extended to monitoring the two types of complex diffusing events. In addition,
the scenario-based case studies have been conducted to investigate the wind effect and
geographical influence on diffusing-event monitoring. For the wind-constrained diffusing-
event monitoring, a heterogeneous method is proposed to cooperate wind
nodes (for diffusion-trend determination) and concentration nodes (for
event-boundary detection). The experimental results have shown the event coverage
rate at 80%~95% with the heterogeneous method, while 60%~25% without such a
method. For the geography-constrained diffusing-event monitoring, GA-deployment
is presented to enhance the event detection probability. A Voronoi diagram is generated
from the layout of geographical obstacles and the network area are allocated with
different node densities according to the edge set of the Voronoi diagram. The typical
network tessellations have been extended based on the idea of GA-deployment, and
such extension has considerably saved energy, with low miss-hit rate, high fault tolerance
and strong report reliability.
In summary, the major contributions of this work involve the design of data
processing architecture for dynamic correlation discovery, the data discrimination
framework to improve data accuracy, the modeling for diffusion-event monitoring, the
presentation of a series of diffusing-event monitoring algorithms with theoretical
analysis and case studies on typical applications. The ideas, models and approaches
presented in this thesis can also be extended and applied to the related domains.
Key Words: diffusing-event monitoring, sensor network, major diffusion trend.
| Date of Award | 4 Oct 2010 |
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| Original language | English |
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| Awarding Institution | - City University of Hong Kong
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| Supervisor | Qing LI (Supervisor) & Wei Jia JIA (Co-supervisor) |
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