A wide variety of sensors have been incorporated into a spectrum of wireless
sensor network (WSN) platforms, providing flexible sensing capability over a
large number of low-power and inexpensive nodes. Traditional signal processing
algorithms, however, often prove too complex for energy-and-cost-effective WSN
nodes. This study explores how to design efficient sensing and classification
algorithms that achieve reliable sensing performance on energy-and-cost-effective
hardware without special powerful nodes in a continuously changing physical
environment. We present the detection
and classification system in a cutting-edge surveillance sensor network, which
classifies vehicles, persons, and persons carrying ferrous objects, and tracks
these targets with a maximum error in velocity of 15%. Considering the demanding
requirements and strict resource constraints, we design a hierarchical classification
architecture that naturally distributes sensing and computation tasks at different
levels of the system. Such a distribution allows multiple sensors to collaborate
on a sensor node, and the detection and classification results to be continuously
refined at different levels of the WSN. This design enables reliable detection
and classification without involving high-complexity computation, reduces network
traffic, and emphasizes resilience and adaptation to the realistic environment.
We evaluate the system with performance data collected from outdoor experiments
and field assessments. Based on the experience acquired and lessons learned
when developing this system, we abstract common issues and introduce several
guidelines which can direct future development of detection and classification
solutions based on WSNs.
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Section 4.3, Equation (3) should be:
v_{2,t} = \alpha_2(E_t - m_{2,t})^2 + (1-\alpha_2)v_{2,t-1)
Initially, m_2,0 = 0 and v_2,0 = 0