Abstract
Recent research efforts show that Wireless Sensor Networks (WSN) for acoustic sensing have the potential for ubiquitous sensing. The key challenge in delivering such WSN architecture is the trade-off between the cost of wireless sensors and acoustic analysis complexity. In this paper, we are proposing a source traffic modelling considering a system in which sensors are connected to a cloud based processing service. Acoustic sensing in practice generates great amount of traffic, and therefore a new model of sensing in a reverberant environment is necessary for better understanding of traffic nature and its volume. To lower the sensor cost, we have considered low-complexity acoustic event detection on the sensor side for preventing unnecessary traffic. The proposed acoustic sensor acts like a noise gate. Sound propagation in reverberant environment is modelled using the modified Image-Source Modelling (ISM) method. The source traffic model is validated in Matlab simulation and in real reverberant environment. Results show an example of bandwidth used by a simple acoustic sensor. DOI: http://dx.doi.org/10.5755/j01.eee.21.5.13335
Highlights
The main paradigm of the internet of things is providing services using low-resource devices connected over the conventional internet network
simple acoustic sensor with a noise gate (SASwNG) proposed in this paper focuses on the design aspect of source traffic modelling if sensor processing power is very low
Traffic modelling is an important part of a network design
Summary
The main paradigm of the internet of things is providing services using low-resource devices connected over the conventional internet network. IEEE 1588 packet-based timing mechanism can be used on an embedded device to synchronize clocks with the resolution of under a microsecond [5]. This can provide good temporal fidelity on the receiver side, which is essential for the analysis typical of microphone array systems. The existing research of source traffic modelling is not applicable to WSN for acoustic sensing. Reference [7] shows the traffic of the medical sensors data that is modelled on the basis of an empirical data set Both models are important contributions in this field but they cannot be used for the WSN that we have in consideration. In order to analyse properties of WSN source traffic, before the described system is deployed, a new model has to be derived
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