1 research outputs found
Analog Signal Compression and Multiplexing Techniques for Healthcare Internet of Things
Scalability is a major issue for Internet of Things (IoT) as the total amount
of traffic data collected and/or the number of sensors deployed grow. In some
IoT applications such as healthcare, power consumption is also a key design
factor for the IoT devices. In this paper, a multi-signal compression and
encoding method based on Analog Joint Source Channel Coding (AJSCC) is proposed
that works fully in the analog domain without the need for power-hungry
Analog-to-Digital Converters (ADCs). Compression is achieved by quantizing all
the input signals but one. While saving power, this method can also reduce the
number of devices by combining one or more sensing functionalities into a
single device (called 'AJSCC device'). Apart from analog encoding, AJSCC
devices communicate to an aggregator node (FPMM receiver) using a novel
Frequency Position Modulation and Multiplexing (FPMM) technique. Such joint
modulation and multiplexing technique presents three mayor advantages---it is
robust to interference at particular frequency bands, it protects against
eavesdropping, and it consumes low power due to a very low Signal-to-Noise
Ratio (SNR) operating region at the receiver. Performance of the proposed
multi-signal compression method and FPMM technique is evaluated via simulations
in terms of Mean Square Error (MSE) and Miss Detection Rate (MDR),
respectively.Comment: 9 pages, IEEE MASS 201