## 2 Sensors
### Respiration Sensors
#### 2.1.3 Design of Sensor Nodes  
Although sensors are the key components of sensor nodes, they cannot work independently. They must work together with other modules to realize signal acquisition. In order to meet the demands of low power consumption and high wearability, many issues need to be considered in the sensor node design process, including energy control, fault diagnosis, and reduction of sensor nodes. (1) Energy Control  
In order to implement long term monitoring functions, energy control has been one of the hot topics in the field of BSN sensors for a long time. Presently, primary researches include low-power architecture design, low-power processor design, low-power transceiver design, and energy acquisition design. In Reference [19], a programmable architecture of ultra-low power consumption based on dynamic time warping designed specifically for wearable inertial sensors is proposed. Provided that the sampling frequency is 3 Hz and bit resolution is 4 bits, the sensor node can run at a power consumption of 9 uW, meeting the need of recognizing motions perfectly. The power consumption of a sensor node can be reduced more than three orders of magnitude by using the proposed architecture compared to traditional low power microcontrollers, such as the MSP430. In Reference [21], researchers introduce a low-power processor design, which reduces supply voltage to below threshold voltage, in order to lower leakage power and prolong the life of logic circuits and SRAM. Consequently, it is a compelling strategy for energy-constrained systems with relaxed performance requirements. However, the effects of process variation become more prominent at low voltages, particularly in deeply scaled technologies. For this purpose, the paper presents a system-on-a-chip which demonstrates techniques to mitigate variation, utilizing timing methodology to avoid output voltage failures and propagation delays in logic gates, in order that the system can run at a low voltage. In terms of low-power transceiver design, Reference [51] presented a low power contact impedance sensor node named CIS, integrating an efficient transceiver using human body as a kind of transmission medium. The proposed CIS adopts a capacitive sensing technique based on LC resonance for detecting the parasitic capacitance between electrodes and the human body to turn a transceiver on or off automatically. It can compensate for channel quality degradation due to contact impedance variation, thus leading to significant reduction of the power consumption of a Low Noise Amplifier (LNA) by more than 70%. In addition, energy acquisition design can help the sensor nodes to collect power by themselves. For example, biological fuel cells can convert chemical energy into electric energy using a biocatalyst. In Reference [15], researchers mention that glucose biological fuel cells can collect power by enzymatic chemical reactions occurring in the human body. (2) Fault Diagnosis  
BSNs consist of multiple sensors, while any broken down sensor may affect the running of the whole system, so failure node detection and isolation cannot be ignored. One solution is to detect them by comparing the contents among neighbor nodes. Reference [20] indicates that detection accuracy decreases as the number of sensor nodes decreases. Furthermore, the number of nodes in a BSN is relatively small, and there is rarely a circumstance applying multiple sensors of the same kind to collect signals at the same position, so the method mentioned above is more suitable for WSNs rather than BSNs, and it does not perform well in the latter. Another challenge is to find the right neighbors for data validation in order to reduce false alarms. In Reference [52], authors propose a more reasonable approach, which uses sliding window techniques to split a sensor stream into segments and provides a fault detection algorithm based on these segments. The proposed fault detection algorithm can be divided into two sub-categories: history based and non-history based.