## 3 General Strategies for Energy Efficient BSN Hardware
### Communication versus _Computation_  
As is the case in most WSNs, wireless transmission of sensed data is the largest power consumer in most current BSNs [9]. This problem is particularly acute in medical BSN applications, in which sensor data rates may be high relative to many WSN applications. Figure 7 illustrates this relationship with the COTS TEMPO platform as an example, where the high power consumption of the Bluetooth transceiver swamps the low power consumption of the TI MSP430 microcontroller during raw data transmission. We could improve this situation by using a lower power radio (e.g., COTS implementing a different protocol, or a custom design), by duty cycling and sending data in bursts, or by other strategies. In this section, however, we focus on the strategy of using computation on the node to reduce the cost of communication, which can influence all types of BSN design regardless of hardware choice. Significant power reduction can be achieved through the development of on-node signal processing and data management which can dramatically reduce the number of bits to be transmitted. By reducing the number of bits to transmit, we effectively allow more substantial duty cycling of the radio (e.g., leaving it off for a larger fraction of the time). Methods to reduce communication data include traditional compression along with advanced signal processing techniques such as pattern classification and feature detection algorithms. Low power signal processing therefore becomes increasingly  
Figure 6: Energy-delay curves for DVS in a COTS microcontroller and a custom design. Figure 7: TEMPO 3.1 power consumption breakdown (with gyroscopes off). important to BSN power efficiency. We can quantify the impact of this tradeoff on the overall node energy using a simple energy model. Assume that \(E_{r}\) is a ratio of the average energy to transmit one bit (\(E_{tx}\)) to the average energy to process one bit (\(E_{proc}\)). This ratio is typically large (_i.e._, \(E_{r}>>1\)) and is determined by a number of factors, including processor energy per operation, the signal processing algorithm and implementation, the packet organization and coding, the networking protocol, transmit power, _etc._ Also assume that the compression ratio (\(CR\)) achieved by on-node signal processing is the ratio of the number of raw bits to the number of transmitted bits. The ratio of average processing energy (\(E_{proc}\)) to average total energy (\(E_{total}\)) is therefore:  
\[\frac{E_{proc}}{E_{total}}=\frac{E_{proc}}{CR}+E_{proc}=\frac{1}{E_{r}}+1 \tag{1}\]  
Figure 8 plots this ratio as a function of \(CR\) for different values of \(E_{r}\). It is clear that the importance of low power signal processing increases with more effective pre-transmission compression techniques, even at high \(E_{r}\) ratios. As a point of reference, 25 nJ/bit is typical for state of the art custom Bluetooth radios targeting 1 Mb/s [10, 11], and an MSP 430 consumes roughly 1 nJ/bit. If the system had no other processing costs (e.g., ignoring memory, _etc._), it would have an \(E_{r}\) of only 25, indicating that processing energy becomes quite important if the CR is even 10. Applying simple generic compression schemes could compress raw data streams by this amount. Even more substantial compression is possible by extracting important features on chip and only transmitting those instead of the raw data. This motivates the need for reducing the hardware energy costs of on-node computation, especially with custom radio solutions. As we described above, sub-threshold operation is one excellent method for decreasing \(E_{proc}\) by over 10\(\times\) compared to operation at the nominal V\({}_{\rm DD}\). Figure 8: Percentage of total energy contributed by on-node signal processing for different \(E_{r}=E_{tx}/E_{proc}\) ratios as a function of the bit compression ratio (\(CR\)) [9]. The power required to transmit data wirelessly can be high even with reduced duty cycle, so the radio remains a critical component even with on-die data compression.