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_18: Some patients, especially patients with chronic diseases such as heart disease, require continuous monitoring of their condition. Wearable devices for patient monitoring were introduced many years ago – for instance, wearable ambulatory ECG (electrocardiogram) recorders commonly known as Holter monitors (Figure 1) are used for monitoring cardiac patients. However, these monitors are quite bulky and can only record the signal for a limited time. Patients are often asked to wear a Holter monitor for a few days and then return to the clinic for diagnosis. This often overlooks transient but life-threatening events. In addition, we don’t know under what condition the signals are acquired, and this often leads to false alarms. For example, a sudden rise in heart rate may be caused by emotion, such as watching a horror movie, or by exercise, rather than by a heart condition. Body sensing To address these issues, the concept of Body Sensor Networks (BSN) was first proposed in 2002 by Prof. Guang-Zhong Yang from Imperial College London. The aim of the BSN is to provide a truly personalised monitoring platform that is pervasive, intelligent, and Figure 1 A patient wearing a Holter monitor
  @cite_8: 
  @cite_0: Objective: This paper discusses the evolution of pervasive healthcare from its inception for activity recognition using wearable sensors to the future of sensing implant deployment and data processing. Methods: We provide an overview of some of the past milestones and recent developments, categorized into different generations of pervasive sensing applications for health monitoring. This is followed by a review on recent technological advances that have allowed unobtrusive continuous sensing combined with diverse technologies to reshape the clinical workflow for both acute and chronic disease management. We discuss the opportunities of pervasive health monitoring through data linkages with other health informatics systems including the mining of health records, clinical trial databases, multiomics data integration, and social media. Conclusion: Technical advances have supported the evolution of the pervasive health paradigm toward preventative, predictive, personalized, and participatory medicine. Significance: The sensing technologies discussed in this paper and their future evolution will play a key role in realizing the goal of sustainable healthcare systems.
  @cite_19: Body Sensor Networks aim to capture the state of the user and its environment by utilizing from information heterogeneous sensors, and allow continuous monitoring of numerous physiological signals, where these sensors are attached to the subject's body. This can be immensely useful in activity recognition for identity verification, health and ageing and sport and exercise monitoring applications. In this paper, the application of body sensor networks for automatic and intelligent daily activity monitoring for elderly people, using wireless body sensors and smartphone inertial sensors has been reported. The scheme uses information theory-based feature ranking algorithms and classifiers based on random forests, ensemble learning and lazy learning. Extensive experiments using different publicly available datasets of human activity show that the proposed approach can assist in the development of intelligent and automatic real time human activity monitoring technology for eHealth application scenarios for elderly, disabled and people with special needs.
  @cite_27: This paper analyses gait patterns of patients with Parkinson;s Disease (PD) based on the acceleration data given by an e-AR sensor. Ten PD patients wearing the e-AR sensor walked along a 7m walkway and each session contained 16 repeated trials. An iterative algorithm has been proposed to produce robust estimations in the case of measurement noise and short-duration of gait signals. Step-frequency as a gait parameter derived from the estimated heel-contacts is calculated and validated using the CODA motion-capture system. Intersession variability of step-frequency for each patient and the overall variability across patients demonstrate a good agreement between estimations from the e-AR and CODA systems.
  @cite_11: The basic requirement of wireless healthcare monitoring systems is to send physiological signals acquired from implantable or on-body sensor nodes to a remote location. Low-power consumption is required for wireless healthcare monitoring systems since most medical sensor nodes are battery powered. The emergence of new technologies in measuring physiological signals has increased the demand for high data rate transmission systems. Ultra-wide band (UWB) is a suitable wireless technology to achieve high data rates while keeping power consumption and form factors small. Although UWB transmitters are designed based on simple techniques, UWB receivers require complex hardware and consume comparatively higher power. In order to achieve reliable low power two-way communication, a sensor node can be constructed using a UWB transmitter and a narrow band receiver. This paper proposes a new medium access control (MAC) protocol based on a dual-band physical layer technology. Co-simulation models based on MATLAB and OPNET have been developed to analyze the performance of the proposed MAC protocol. We analyzed the performance of the MAC protocol for a realistic scenario where both implantable and wearable sensor nodes are involved in the data transmission. Priority-based packet transmission techniques have been used in the MAC protocol to serve different sensors according to their QoS requirements. Analysis is done with regard to important network parameters, such as packet loss ratio, packet delay, percentage throughput, and power consumption.
summary: In the IoT healthcare and BAN domain, all of the existing solutions do sensing and communication in the digital domain (using ADCs DACs Microprocessors), which needs more power than analog sensing and communication. For example, Yang's group @cite_18 developed a miniaturized node that incorporates wireless communication, on-board processing, nine-axis motion tracking, and other sensors @cite_0 . It also developed an e-AR sensor @cite_27 , a small device to be worn behind the ear that captures information about the balance of the wearer such as gait, posture, skelet al joint shock-wave transmission, and activity of the individual. Yuce's group developed techniques based on Ultra Wide Band (UWB) wireless technology to reduce the power consumption of body-worn sensors @cite_11 @cite_8 . Another important example is the activity recognition of the user using various body sensors viz., accelerometer and gyroscope data. The data from the sensors is digitally processed and stored into a wrist-band device that syncs the data with the mobile phone using Bluetooth technology @cite_19 . Unlike these approaches, we adopt an entirely different approach based on analog sensing and communication that does not use any power-hungry ADCs (see Table ).