Testing the methods for computing charge time in multiple-

multiple-pulse load applications

To test the methods, we create a dual-pulse load application using LPL. The mote alternates between two kinds of sends, the first (Load 1) sends to a re- ceiver with a wake-up interval of 0.26 s, and the second (Load 2) sends to a receiver with wake-up interval of either 0.63, 1.02, or 1.42 s, making for a to- tal of three setups. The sends are configured as broadcasts, so the node upon sending will attempt the send for as long as the amount of time specified by the receiver wake-up interval - thus, the length of the receiver wake-up interval is equivalent to the sender’s current draw length for that load. In contrast, when doing acknowledgement-enabled unicasts, the sender will stop the send opera- tion upon receiving an acknowledgement from the receiver, to save energy. The analytically-derived capacitor sizes and charge times for the four loads (Load 1, Load 2A, Load 2B, and Load 2C) when each is considered in isolation are tabulated in Table 2.9.

Table 2.9: Load table

Draw length (s) Capacitor size (µF) Charge time (s)

Load 1 0.26 2,000 29.18

Load 2A 0.63 5,000 70.7

Load 2B 1.02 8,000 114.5

Load 2C 1.42 11,000 157

To test that the setups are working, all the packets are received by the base station, which indicates packet reception through a terminal printout. The sys- tem is powered by energy harvesting during the experiments, with the Cymbet EnerChip fully charged beforehand. In each setup, the capacitor utilized is that of the larger load: for example, in the Load 1-Load 2A setup, the capacitor used

is 5,000µF. Each experiment is run for a period long enough so that 25 packets

from each type of send are sent by the mote.

All methods are tested on all three setups and found to be functional. For the Opportunistic method,thresh_countis set to 1, andvolt_limitis set to 3.35

V, for both loads. The application running on the mote is slightly modified for the experiment used to test the Opportunistic method - to enable us to keep track of the charge times, the length of the charge time preceding a send is sent within the packet as payload. The length of the charge time can be determined by the number of iterations of the loop embodied in Lines 5-22 of Algorithm 2. To ensure that the system is in continuous operation (not restarting), each packet also contains a sequence number that is regularly incremented after a send operation.

For the Load 1-Load 2B setup, Figure 2.15 plots the opportunistically- determined charge times for both loads, along with the charge times determined by the Concatenation method.

It can be seen in Figure 2.15 that the opportunistically-derived charge times vary across time, indicative of the constantly changing charge rate of the energy- harvesting system. It must be noted that in the opportunistically-derived pair, the charge time for the large load is always smaller than that of the small load, in contrast with the analytically-derived pair. To understand this, one must

0 5 10 15 20 0 20 40 60 80 100 120

Packet sequence number

Charge

time

(

seconds

)

Small Load Opport. Large Load Opport. Large Load Concat. Small Load Concat.

Figure 2.15: Charge times: Opportunistic method vs Concatenation method.

remember our definition of charge time: it is the period of time before a current draw (or active period). A small load is preceded by the large load, which draws a significant amount of current from the capacitor. Thus, charging the capacitor to an acceptable voltage level will take time. By the same token, the large load is preceded by the small load, which relatively does not draw much current. There- fore, it may be more prudent to compare the opportunistically-derived large load charge time with the analytically-derived small load charge time, and vice versa. Even when such comparisons are made however, the opportunistically-derived values are still significantly smaller than the analytically-derived values. For instance, the average opportunistically-derived charge time for the small load is 19.25 s, and the analytically-derived charge time for the large load is 114.5 s.

The duty cycles associated with each method for the three setups are nor- malized against that of the Concatenation method and plotted in Figure 2.16 and Figure 2.17. Figure 2.16 plots the duty cycles associated with the three an- alytical methods, while Figure 2.17 plots the average duty cycle measured when the Opportunistic method is used. The data for the Opportunistic method is plotted separately since its numbers are significantly larger than that of the

other three. 1-2A 1-2B 1-2C 0 0.5 1 1.5 Load combination Normalized dut y cyc le Concatenated Lazy Aggressive

Figure 2.16: Normalized duty cycles, analytical methods.

1-2A 1-2B 1-2C 0 2 4 6 Load combination Normalized dut y cyc le

Figure 2.17: Normalized duty cycles, Opportunistic method.

Figure 2.16 shows the Lazy method and the Aggressive Analytical method being inferior to the Concatenation method. This is to be expected. As already mentioned, the Lazy method is guaranteed to work but has the drawback of performance loss, which grows proportionately with the disparity between the sizes of the small load and the large load. This too, is apparent when the results

are compared across setups. The Aggressive Analytical method performs better than the Lazy method, but still worse than the Concatenation method. The Aggressive Analytical method is also guaranteed to work, but even with the other charge time tailor-fitted for the voltage drop caused by the small load, the value is still larger than that of its counterpart in the Concatenation method. The voltage level from which the charge period has to recover is higher (because the capacitor is larger), but the capacitor is now also more difficult to charge.

Figure 2.17 shows normalized data for the Opportunistic method. The Op- portunistic method performs significantly better than all three other methods, with the lowest in the three setups showing a 350% improvement over the Con- catenation method. These results signify that if the requirements of perfect data regularity and predictability can be relaxed, significantly more performance can be had from the system.

2.6.3

Summary

We present and discuss several methods for computing charge times for energy- harvesting nodes that run multiple-pulse load applications. We propose and test an opportunistic method for use in cases where the requirement of predictability and regularity in data generation can be relaxed. Our method is shown to exhibit 350%-400% improvement over analytical methods when it comes to the

generated duty cycle. While the Opportunistic method is presented in the

context of multiple-pulse load applications, it can readily be applied to single- pulse load applications.

It must be noted that the methods and protocols presented in Chapter 2.5

and this subchapter arenotmutually exclusive. They actually work on different

parts of an operation cycle. The protocols from Chapter 2.5 ensure that receiver wake-up interval is as long as it can be, resulting in energy savings for the

receiver. This is actually done by maximizing the current draw time at the

sender. The methods presented (specifically the Opportunistic method), on the

is when an energy-harvesting WSN node wants to send data that will not fit in a single packet. The maximum receiver wake-up interval that can be supported can be derived using the protocols in Chapter 2.5. The receiver can set its wake- up interval to this value, maximizing its energy savings. The sender can then opportunistically derive the charge time for each send, potentially resulting in the multi-packet data getting sent faster.

Technical requirements: Energy-harvesting noise-sensing

WSNs and the noise metric required by noise codes

3.1

Overview

In this chapter, we examine how currently-prevailing technical requirements affect the design, performance, and applicability of energy-harvesting WSNs. Specifically, we consider the noise measurement metric specified in many of the currently-enforced noise codes (such as the European Union’s Environmental Noise Directive [33] and the New York City Noise Code [95]), and how it affects the applicability of low power or energy-harvesting WSNs to the problem of noise pollution sensing. We empirically derive or demonstrate the limitations of energy-harvesting WSN nodes as they carry out the noise code-compliant noise sensing process and based on these limitations propose an alternative design or architecture.