In this paper, we propose a framework for the real-time monitoring of wireless biosensors. This i... more In this paper, we propose a framework for the real-time monitoring of wireless biosensors. This is a scalable platform that requires minimum human interaction during set-up and monitoring. Its main components include a biosensor, a smart gateway to automatically set up the body area network, a mechanism for delivering data to an Internet monitoring server, and automatic data collection, profiling
The quality of coverage of any wireless network design depends on the accuracy of the propagation... more The quality of coverage of any wireless network design depends on the accuracy of the propagation model. For accurate designs, the propagation models are estimated from signal strength measurements taken in the service area. Even though it is known that modeling error is introduced when only a few signal strength measurements are processed, this is not completely understood. In this paper, we investigate the impact of a limited number of signal strength measurements on the accuracy of coverage prediction and estimation of propagation parameters. We find that when there are fewer than 150 independent signal strength measurement samples, which corresponds approximately to 3% of the cell area, better results are obtained by fixing the propagation slope and calculating the intercept via method of least-squares
In this paper, we propose a framework for the real-time monitoring of wireless biosensors. This i... more In this paper, we propose a framework for the real-time monitoring of wireless biosensors. This is a scalable platform that requires minimum human interaction during set-up and monitoring. Its main components include a biosensor, a smart gateway to automatically set up the body area network, a mechanism for delivering data to an Internet monitoring server, and automatic data collection, profiling
The quality of coverage of any wireless network design depends on the accuracy of the propagation... more The quality of coverage of any wireless network design depends on the accuracy of the propagation model. For accurate designs, the propagation models are estimated from signal strength measurements taken in the service area. Even though it is known that modeling error is introduced when only a few signal strength measurements are processed, this is not completely understood. In this paper, we investigate the impact of a limited number of signal strength measurements on the accuracy of coverage prediction and estimation of propagation parameters. We find that when there are fewer than 150 independent signal strength measurement samples, which corresponds approximately to 3% of the cell area, better results are obtained by fixing the propagation slope and calculating the intercept via method of least-squares
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Papers by LAKSHMAN TAMIL