Low-Power Distance Estimation

The selected solution is based on a decentralized approach that needs no local installations of additional power supplies, coordinating access points, or a central server. Figure 6.3 depicts an example setup of four sensors worn by three residents and one carer. Resident 1 and carer A are in

Component State Current consumption

(mA) at VDD= 3.0V Radio module TX (-8.2 dBm) 15.8 RX (active transmission) 17.24 IDLE 1.7 SLEEP/WOR 0.0002 Microcontroller Active (8 MHz) 1.55 Low-Power Mode LPM0 0.64 Low-Power Mode LPM3 0.0021

Table 6.1: Power consumption of the Chronos eZ430 hardware accord- ing to [165, 166]

proximity. Hence, we assume that carer A is providing care to resident 1. Although resident 2 is very close, she, unlike resident 2, is not within proximity. However, if carer A starts to walk, there will probably be a short period of proximity between resident 2 and carer A. The resulting patterns of co-location and assumed care activities can be captured without a localization of each participant. This distributed sensing approach is similar to [106, 12, 115].

There are two main options to estimate the distance between sensors and to decide whether or not a sensor is in proximity: TOF and RSSI. Both mechanisms have been discussed in the context of indoor localization technologies in Section 2.2.2. The TOF is not applicable for our case because the distances are too short and the clocks of the Chronos eZ430 are not precise enough. The firmware would have to send a high number of packets back and forth to accumulate a significant time difference that can be measured by the Chronos. Moreover, frequent synchronization packets would be needed. In contrast, RSSI-based methods can be realized with only a few sent packets. However, RSSI signals tend to vary across several packets and multipath propagation may result in a large variation. Therefore, filter mechanisms are used that require additional processing, therefore increasing power consumption.

In our system, proximity between two sensors is detected based on the practical limited range of a radio module. According to Friis (see Section 2.2.2), the free-space path loss of the signal rises with the square of the distance until the signal-to-noise ratio (SNR) makes it impossible to

Resident 1

Carer A

Resident 3 Resident 2

Figure 6.3: Schematic example of the proximity between three residents and one carer

receive a sent packet. The underlying assumption of this approach is that the noise will be relatively constant for distance measurements. Moreover, the orientation of the sensors will influence the distance. Finally, the body will shield the signal. We assume that the effects due to orientation and shielding will dominate because the Chronos has only one antenna. Therefore, the presented approach allows only a raw estimation, but any further effort to be more precise will be obsolete because of movement and shielding effects.

The signal strength of the Chronos radio module can be reduced to limit the range to the desired proximity distance (3m in our case). This has three advantages:

• Data transmissions with a smaller signal strength require less energy. • Sensor nodes that are not within range do not have to keep their

radio modules activated to receive packets that are later filtered. • Furthermore, the radio module is already detecting signals from

background noise in the RX state by monitoring the signal strength. In our approach, we reuse this process instead of implementing an additional filter at a later stage, which would require additional processing power.

In summary, this proximity-detection method is similar to RSSI-based methods. It requires less computing power, but is also less precise. More- over, the reduced range decreases the noise induced by other sensors that are not in the desired proximity distance or are received by reflection. However, first tests showed that the range of a radio module fluctuates, so further filtering is necessary. A sensor can receive a packet from a sending sensor that is clearly outside of the proximity area (false-positive detection), but single packets are lost, even though a sensor is clearly within the proximity area (false-negative detection). The reduced signal strength reduces the number of false positives.

The false-negative detection was tackled by reducing the send power of the radio module only so far that it was able to send slightly past the proximity range. Furthermore, multiple packets are sent and received so that single missing packets can be filtered. This increases the false positive rate (i.e., more out-of-range sensors are detected). The system uses a simple filter to detect sensors that are not in the desired proximity area by

analyzing the packet loss over several packets. Sensors in the system send several packets in a row and an amount of nrecv packets must be received to detect that the sender is within proximity range (e.g., if four out of five packets have to be received, a packet loss of 20 percent will be used to discriminate between sensors within proximity against sensors that are too far away). The mechanism can be understood as a simple low-pass filter. An energy-efficient filter will require a minimal number of packets to reduce the time sensors have to send and receive packets. The required number of packets and the required power can be minimized by choosing a filter that allows only one missing packet. The filter counts the number of packets and the contained sequence numbers and rejects a signal if more than one packet is missing before nrecv are received.

A high value of nrecv would reliably filter packets of sensors outside of the proximity range, but each sensor would have to send and receive more packets. A lower value would decrease the power consumption that is necessary to detect proximity, but would also increase the error. This tradeoff was evaluated in a pre-test. Figure 6.4 shows the proximity detection for different filter parameter values for a maximum radio range of approximately 4 meters. Even if one packet of the nrecv packets is not received, the sensor will still be detected as being in proximity. The filter significantly increases the robustness of the detection algorithm.

A value of 10 packets decreases the error to approximately 10 percent in the range of 3.5-4 meters. The number of packets must be doubled to decrease the error to almost 0 percent. Sensors within 3 meters are

2 Packets, 1 necessary for proximity 10 Packets, 9 necessary for proximity 20 Packets, 19 necessary for proximity 0 10 20 30 40 50 60 70 80 90 100 100 1.0 1.5 2.0 2.5 3.0 3.5 4.0 4.5

Detected proximity probability (%)

reliably detected as being in proximity range. Based on these results, we decided that 10 packets decrease the error to an acceptable level, but 20 packets require too much energy in the longer send/receive states for only slightly decreased error rates.