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Level set estimation (LSE) is the process of classifying the region(s) that the values of an unknown function exceed a certain threshold. It has a wide range of applications such as spectrum sensing or environment monitoring. In this paper, we study the the optimal LSE of a linear random field that changes with respect to time. A linear sensor network is used to take discrete samples of the spatially-temporally...
Level set estimation (LSE) is the process of using noisy observations of an unknown function to estimate the region(s) where the function values lie above a given threshold. It has a wide range of applications in many scientific and engineering areas, such as spectrum sensing or environment monitoring. In this paper, we study the optimum LSE of a time-varying random field under a total power constraint...
The optimum sensor node density for one- and two-dimensional (1-D and 2-D) wireless sensor networks (WSNs) with spatial source correlation is studied in this paper. The WSN attempts to reconstruct a spatially correlated signal field by collecting the location-dependent measurements from the distributed sensor nodes. The WSN is designed to minimize the mean square error (MSE) distortion between the...
The optimum space and time sampling in a wireless sensor network (WSN) with spatial-temporally correlated data is studied in this paper. The impacts of the node density in the space domain and the sampling rate in the time domain on the network performance are investigated asymptotically by considering a large network with infinite area, infinite time period, but finite node density and finite temporal...
Random on-off accumulative transmission (R-OOAT) is a cross-layer technique that can achieve collision-tolerance in the media access control (MAC) layer by leveraging on the signal processing capability in the physical (PHY) layer. In this paper, a new PHY/MAC cross-layer design is proposed for the R-OOAT framework. In the PHY layer, we propose an iterative method for the detection of R-OOAT signals...
A new ultra-low power (ULP) wireless sensor network (WSN) structure is proposed to monitor the vibration properties of civil structures, such as buildings and bridges. The new scheme integrates energy harvesting, data sensing, and wireless communication into a unified process, and it is fundamentally different from all the existing WSNs. In the new WSN, piezoelectric sensors are employed to harvest...
In this paper, a random on-off accumulative transmission (R-OOAT) scheme is proposed to achieve collision-tolerant (CT) media access control (MAC) for asynchronous wireless sensor networks. Unlike conventional MAC schemes that discard packages with collisions at receivers, the CT-MAC extracts the salient information from the colliding signals by using the R-OOAT scheme in the physical layer. Nodes...
The optimum sensor node density in a large linear wireless sensor network with spatial source correlation is studied. Unlike most previous works that rely on the design metric of network capacity with an error-free communication assumption, this paper performs analysis under a distortion-tolerant communication framework, where controlled distortion in the recovered information is allowed as long as...
In this paper, a cross-layer collision-tolerant (CT) media access control (MAC) scheme is proposed to achieve reliable low power communications for one-hop asynchronous wireless sensor networks (WSNs). Unlike conventional MAC schemes that discard and retransmit signals colliding at a receiver, the CT-MAC extracts the salient information from the colliding signals by leveraging on the signal processing...
Wireless sensor networks (WSNs) developed for the monitoring of critical military or civilian infrastructures are expected to have long life cycle with ultra-low power consumption. An ultra-low power wireless sensing scheme is developed by exploiting the unique features of infrastructure monitoring systems, which usually have long latency tolerance, low data rate, and strong correlation among data...
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