text: 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&#x2014;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.
  @cite_26: In this paper, we study transmission of a memoryless Laplacian source over three types of channels: additive white Laplacian noise (AWLN), additive white Gaussian noise (AWGN), and slow flat-fading Rayleigh channels under both bandwidth compression and bandwidth expansion. For this purpose, we analyze two well-known hybrid digital-analog (HDA) joint source-channel coding schemes for bandwidth compression and one for bandwidth expansion. Then we obtain achievable (absolute-error) distortion regions of the HDA schemes for the matched signal-to-noise ratio (SNR) case as well as the mismatched SNR scenario. Using numerical examples, it is shown that these schemes can achieve a distortion very close to the provided lower bound (for the AWLN channel) and to the optimum performance theoretically attainable bound (for AWGN and Rayleigh fading channels) on mean-absolute error distortion under matched SNR conditions. In addition, a non-linear analog coding scheme is analyzed, and its performance is compared to the HDA schemes for bandwidth compression under both matched and mismatched SNR scenarios. The results show that the HDA schemes outperform the non-linear analog coding over the whole CSNR region.
  @cite_7: A low-complexity and low-power all-analog circuit is proposed to perform efficiently Analog Joint Source Channel Coding (AJSCC). The proposed idea is to adopt Voltage Controlled Voltage Source (VCVS) to realize the rectangular-type mapping in AJSCC. The proposal is verified by Spice simulations as well as via breadboard and Printed Circuit Board (PCB) implementations. Field testing results indicate that the design is feasible for low-complexity and low-power systems such as wireless sensor networks for environmental monitoring.
  @cite_21: A low-power wearable wireless sensor measuring both molecular biomarkers and physiological signals is proposed, where the former are measured by a microfluidic biosensing system while the latter are measured electrically. The low-power consumption of the sensor is achieved by an all-analog circuit implementing Analog Joint Source-Channel Coding (AJSCC) compression. The sensor is applicable to a wide range of biomedical applications that require real-time concurrent molecular biomarker and physiological signal monitoring.
  @cite_6: Recently, analog joint source-channel coding has been proposed as a means of achieving near-optimum performance for high data rates with a very low complexity. However, no experimental evaluation showing the practical feasibility of this scheme has been performed to date. In this paper, we describe a software-defined radio implementation of an analog joint source-channel coded wireless transmission system. Experimental evaluation carried out in an indoor environment making use of a wireless testbed show that the performance perfectly matches that originally reported by simulations in additive white Gaussian noise channels for signal-to-noise ratio values below 20 dB.
  @cite_17: We consider the problem of sending a bivariate Gaussian source S=(S1,S2) across a power-limited two-user Gaussian broadcast channel. User i (i=1,2) observes the transmitted signal corrupted by Gaussian noise with power σi2 and desires to estimate Si. We study hybrid digital-analog (HDA) joint source-channel coding schemes and analyze the region of (squared-error) distortion pairs that are simultaneously achievable. Two cases are considered: 1) broadcasting with bandwidth compression, and 2) broadcasting with bandwidth expansion. We modify and adapt HDA schemes of and , originally proposed for broadcasting a single common Gaussian source, in order to provide achievable distortion regions for broadcasting correlated Gaussian sources. For comparison, we also extend the outer bound of from the matched source-channel bandwidth case to the bandwidth mismatch case.
summary: This paper is the first work to propose this structure for multiplexing data of AJSCC sensors. Table compares the power numbers of our circuit @cite_7 with state-of-the-art wireless sensor motes (all of which are digital). We can notice that @math is possible with our circuit, which is essential in low-power applications. The existing circuit realizations of spiral-type mapping also are all based on digital circuits and systems @cite_6 . In this scenario, it is worth noting that the Hybrid Digital Analog (HDA) coding can also perform signal compression @cite_26 @cite_17 . However, the digital part still needs digitization of the signals. Contrary to all these approaches, we propose signal compression and encoding in the analog domain with no need for ADCs DACs Microprocessors. To show the feasibility of our vision (analog compression and encoding), we previously developed a novel circuit to compress two signals @cite_7 and verified its applicability to two pathological signals (molecular biomarkers and physiological signal) @cite_21 . In this paper, we extend the theory for N-dimensional signal compression and propose novel multiplexing techniques that address the above mentioned challenges of scalability and power in the context of healthcare IoT as one of the key applications.