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- Anymac Adapting The Sunspot Architecture For Mac
- Anymac Adapting The Sunspot Architecture For Machine
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Wireless sensor networks (WSNs) could potentially help in the measurement and monitoring of noise levels, an important step in mitigating and fighting noise pollution. Unfortunately, the high energy required by the noise measurement process and the reliance of sensor motes on batteries make the management of noise-sensing WSNs cumbersome. Giving motes energy harvesting (EH) capabilities could alleviate such a problem, and several EH-WSNs have already been demonstrated. Nevertheless, the high-frequency nature of the data required to measure noise places significant additional challenges to the design of EH-WSNs.
In this paper, we present a design and prototype for a mote extension which enables the mote to detect noise levels while being powered by energy harvesting. The noise level detection carried out by the system relies primarily on the concept of peak detection.
Results of performance testing are presented. Aside from the hardware design and prototype, we also discuss methods of assigning charge times for application scenarios where there are multiple pulse loads. We also propose a new opportunistic method for charge time determination. Experiments demonstrate that the new method could improve analytically-derived duty cycles by at least 350%. Noise pollution is becoming an increasing concern in many urban regions all over the world (for example).
An important step in fighting and mitigating noise pollution is its classification and quantification. Efforts being made towards this goal include cellphone applications that measure noise and noise-related legislations (such as the European Union’s Environmental Noise Directive and the New York City Noise Code ). Wireless sensor networks (WSNs) could potentially help with these efforts, as they could enable the simultaneous and continuous gathering of data over wide geographic regions. Several WSNs for noise pollution monitoring have been demonstrated –. WSN nodes (‘motes’) however, are usually powered by batteries which have to be frequently replaced. The length of time between battery replacements depends on the energy demand of the application: for those nodes that have energy-intensive sensors, or do lots of routing, this could be days, while for nodes that have simple sensors with very small duty cycles, this could be many months. The task of replacing these batteries on a regular basis makes the maintenance of such networks difficult.
As an alternative to batteries, WSNs could also be powered through energy harvesting (EH). Several EH-WSNs have been demonstrated – ; nevertheless, despite these past successes, creating an EH-WSN for noise pollution monitoring is not straightforward. The difficulty lies in the nature of the data being gathered.
While a temperature-sensing WSN could probably take a reading every minute and not lose accuracy, to measure noise, sound samples have to be taken. The human ear can hear frequencies of up to 20,000 Hz - to be able to digitally reconstruct a human-audible signal without missing any frequency component, samples should then be taken at the rate of the Nyquist frequency, or around 40,000 Hz.
Sampling at such a high frequency is highly energy consuming, and the processing of the data gathered is challenging to implement on resource-constrained motes. In this paper, we present a mote extension design which would enable a mote to detect noise while being powered by energy harvesting, specifically, solar energy harvesting. While the system does not conform with the standards of measure utilized by any of the existing noise codes, such a network could still be immensely useful in many urban settings - in contrast to cellphone readings taken by individuals, the network could provide data and information that is uniform (since the the measuring devices would be of the same type) and properly contextualized (since each mote’s location and orientation would be known). Such data could help in building noise maps, a goal shared by many ‘smart city’ initiatives all over the world (for example). In terms of administration, our design makes for a relatively low-cost and low-maintenance system, something that may not be currently achievable with WSNs that are designed to comply with existing noise codes.
A noise-sensing mote would have to regularly sample the microphone at high frequency, an operation that consumes a significant amount of current for an extended period of time. Using the radio for transmitting or receiving messages, or even just idle listening, also consumes a significant amount of energy. Such operations are called pulse loads, as they draw pulses of high current from the energy storage device.
Since both operations have to be performed in a noise-sensing mote, it can be considered to be running a multiple-pulse load application. Energy-harvesting WSN motes usually have a boost capacitor between itself and the energy storage device. This is to mitigate for the limits to the level of current that could be drawn from the energy storage device, imposed by the device’s internal impedance. Since the capacitor has a finite capacity, it has to undergo repeated cycles of charging (from the battery) and discharging (to the load). This means that while a high level of current draw is now possible, such a draw could still not be sustained for an indefinite period of time - after the boost capacitor charge is depleted, it has to be allowed to recharge or recover (in comparison, without a boost capacitor, the current draw may not be possible at all). When running pulse load applications, the charge time has to be carefully determined to ensure that the boost capacitor is sufficiently charged before drawing any current from it. Utilizing the right charge time is of paramount importance: set it too long and duty cycle (hence, performance) suffers, set it to a value too low and the system may not function at all.
Fortunately, an analytical method is available for deriving the charge time of single-pulse applications. Such a method however, has not been derived before for multiple-pulse load applications. This paper presents two primary contributions: firstly, a mote extension design for noise sensing in urban environments, and secondly, a discussion of charge time determination methods for applications with multiple pulse loads. Aside from deriving analytical-computational methods for charge time determination, we also propose our own method, called the Opportunistic method. The remainder of the paper is organized as follows.
The next section discusses the ‘traditional’ sound level measurement process as carried out by WSN motes. This is followed by a discussion of our design, the rationale behind it, and the derivation of the parameter values that were utilized in it. This is followed by a discussion of power management algorithms. We then discuss the implementation of the design, how it was tested and evaluated, and the results of the tests. In the second part of the paper, we discuss the methods available for assigning charge times to applications with multiple pulse loads, including our own proposed method. We then proceed to expound and describe the experiments carried out to test the effectiveness of the methods and the results of the said experiments.
An overview of related and future work is then provided, after which we summarize and conclude the paper. Sound is a mechanical wave which uses air as a medium. As the wave travels, it induces pressure fluctuations which are then detected by the human ear, or in mechanical/electronic devices, a transducer. Greater energy in the wave translates to bigger pressure fluctuations, which humans then experience as the loudness of a sound. The human ear is an extremely sensitive device: the difference between the smallest and biggest pressures that the human ear can sense vary by 12-13 orders of magnitude. Representing such a huge range can be cumbersome in the linear scale, so sound pressure level (or loudness) is usually defined in a logarithmic scale, with the unit of decibels,.
For the microcontroller of the mote to be able to calculate Equation, p should be in a form that is digital in nature: the pressure waveform therefore has to go through transformations. Firstly, there is the microphone/transducer, which senses the pressure fluctuations in the air using a thin membrane. The physical fluctuation of the membrane induces voltage fluctuations. The voltage fluctuation induced by the pressure fluctuation is defined by the microphone sensitivity, denoted by S in Equation.
(4) E ref is conventionally set to 1 V while p 0 is set to 1 Pa. E is the resulting voltage swing when pressure changes by p. With S defined (through a datasheet, for example), E could be easily derived from Equation. The output of the microphone is a continuous voltage waveform. In most noise-measuring systems, the microphone output is also A-weighted, meaning its frequency components are attenuated or amplified to match how the human ear perceives different frequencies (since the human ear does not perceive frequencies equally ). The voltage waveform is also usually preamplified using an operational amplifier (op-amp)-based active amplifier before being processed by the microcontroller’s analog-to-digital converter (ADC). The ADC samples the voltage waveform in discrete time steps and discretizes the voltage level into binary integers.
The output of the ADC is a stream of binary integers, which are then stored in the microcontroller’s memory. Two parameters define the operation of the ADC: the sampling rate and the word width. A higher sampling rate translates to a more faithful representation of the original signal. The word width is the number of bits available for representing the sampled value. For instance, if the word width is only two bits, the ADC would only be able to differentiate between four levels (since 2 2=4). If the continuous voltage values vary between 0 and 1 V, values between 0 and 0.25 V would be encoded as 00 and values higher than 0.25 V but no higher than 0.5 V would be encoded as 01, etc.
The output of the ADC could already be processed by the microcontroller’s CPU and used as input to Equation. While Equation requires the sound pressure level, the ADC output would suffice as an input. Barring the precision loss introduced by the ADC, the relationship between the integer value and the original pressure level is linear: the output could be scaled later.
Alternatively, the programmer could also choose to convert the ADC output into its Pa equivalent before having the CPU do the computation. The downsides of this approach are the extra computational steps and the need for floating point numbers, which are not supported by all microcontroller platforms. Most noise codes rely on L eqT values.
It must be noted however, that L eqT is not a perfect measure of noise, especially as experienced by people in an urban area. Since L eqT takes the time average of noise measurement values, it is possible for intense but short noisy periods to be hidden by the measure. Another important reason for not ignoring these short but noisy periods is the increasing amount of evidence which suggests that impulsive noise could be more damaging than noise that is more widespread temporally.
As an example, take an area that is generally very quiet but regularly suffers from aircraft noise. The L eqT measurement in this case would be very low and would not reflect the disturbance caused by the aircraft passing overhead. In such a situation, the peak noise level detected within a time period might actually be a more informative and useful metric. Design rationale While most of the previous work that involved sound sensing has done so by recording the sound waveform , , we have opted for an approach that is centered on a peak detector, for the reasons cited above. This means that our system does not produce SPL values, but the peak noise level recorded within a period of time. Such information is insufficient for the requirements of most existing noise codes, but as discussed, such information, combined with an energy-harvesting/battery-free operation feature, would suit noise sensing in many urban settings. Our decision to adapt a peak detector-based design is also motivated by the limited capabilities of the TelosB - in a previous work , we demonstrated how at 10,000 Hz sampling rate, the limited memory space of the MSP430 microcontroller only enables continuous sampling of up to 0.4 s.
In comparison, for many systems that monitor L eqT, p RMS is usually computed over 1-s intervals. It must be noted that L eqT could still be computed despite this limitation because the 1-s sampling interval is not standard, and the ADC could be simply be activated again for another set of readings. There would potentially be a gap between the sets of readings (which would be a function of the microcontroller processing speed), but even that could be minimized by parallelizing processing and sampling using direct memory access (DMA). Even with DMA however, two problems still remain.
Firstly, the need to compute the RMS of the samples every 0.4 s would consume a lot of energy over time. Secondly, and more significantly, the number of such readings that could be accumulated would be severely constrained by the amount of space - it must be remembered that space is one of the factors that contributed to the 0.4-s limit in the first place. This could be alleviated by shorter sampling windows (i.e., shorter sampling sequences) or by frequently sending data to the sink.
The former, however, would negatively affect the accuracy of the readings, while the latter would be costly in terms of energy. In comparison, a peak detector-based system does not record the waveform at all and only generates a digital reading at the end of a sampling period. A significantly larger number of readings could then be stored, resulting in greater flexibility in how and when such readings are processed. For example, assuming that time bounds requirements are met, readings could be accumulated so that several are sent to the base station in a single packet. The timing of sending could also be dynamically set to coincide with periods of high energy generation, helping to keep the system in energy-neutral operation.
Alternatively, the packet sent could be externally triggered, with the data pulled out of the mote by a mobile sink or data mule. Such options are simply very limited or not available in systems that are severely constrained in the amount of data that they could store.
It must be noted that a peak detector-based design does not necessarily consume less power than a design which carries out wave sampling. The system unfortunately still has to duty cycle, meaning it cannot be in the active state all of the time. Ways of alleviating this limitation will be discussed later.
While the ADC is no longer continuously active and sampling the microphone at a high frequency, a new sub-circuit is added to the system, one which continuously runs during the active part of the operation cycle. Our measurements show that the power consumption of the sub-circuit is at par with (or just slightly smaller than that of) an ADC at a high-frequency sampling operation. Nevertheless, the flexibilities afforded by a peak detector-based system gives significantly more options for power management algorithms which could lead to energy savings in the long run. Basic design For our work, we used the ultra-low-power wireless sensor module TelosB.
TelosB was designed at UC Berkeley with three goals in mind: minimal power consumption, ease of use, and increased software and hardware robustness. It uses the 16-bit Texas Instruments MSP430 microcontroller and the 2.4 GHz IEEE 802.15.4 compliant RF transceiver Chipcon CC2420 radio. The TelosB motes that we used for this work were manufactured by Advanticsys and marketed as CM5000 motes. Our TelosB motes run TinyOS, or Tiny Microthreading Operating System ,. For the energy-harvesting component of our setup, we utilized the CBC-EVAL-09. The CBC-EVAL-09 is an evaluation kit manufactured by Cymbet Corporation (Elk River, MN, USA). It features several energy-harvesting transducers, along with with the EnerChip EP CBC915 Energy Processor and the EnerChip CBC51100 100 uAh solid state battery module (with two EnerChip CC CBC3105 solid state batteries connected in parallel).
The EnerChip EP CBC915 Energy Processor serves as an interface between the transducers and the energy storage device. It employs advanced maximum power point tracking tracking algorithms, constantly matching the output impedance of the energy-harvesting transducers, thus ensuring high-efficiency energy conversion. The EnerChip EP CBC915 Energy Processor also facilitates communication with the microcontroller, providing information such as state-of-charge estimates and a calibration function. For the sensor design, we utilized an ADMP401 MEMS microphone. Compared to conventional electret microphones, MEMS microphones offer the advantage of minimal size, lower power usage, and a better signal-to-noise ratio (SNR). The microphone output is preamplified by an op-amp-based inverting amplifier with a gain of 100. The preamplified output is then fed into a peak detector circuit with a load-isolated storage capacitor.
This peak detector design is more suited for long hold times than the simpler single op-amp-based alternative. The details of the peak detector design are discussed in detail in –. The output of the peak detector is connected to ADC channel 0 of the TelosB, where it is sampled at the end of a sensing period.
All three operational amplifiers (op-amps) utilized in the design are OPA344s. To facilitate duty cycling, the supply lines of the microphone, the preamplifier, and the peak detector are gated by the high-side switch ADP194. The ADP194 is digitally controlled by the TelosB using a digital I/O pin.
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During the sensing period, the Enable pin of the ADP194 is set to high by the TelosB, enabling the current to flow to the microphone, the preamplifier, and the peak detector. The design also features a MAX323 digital switch, which provides a digitally switched line between the storage capacitor and a discharge resistor (whose other end is connected to the ground). The switch is added since the sudden surge of power to the peak detector causes it to have an overshot output in the beginning of a sampling period. To return the output to its normal level, a ‘corrective discharge’ is carried out by connecting the capacitor to the discharge resistor for a few milliseconds.
Like the ADP194, the MAX323 is digitally controlled by the TelosB using a digital I/O pin.The schematic of the expansion board (which contains the microphone, preamplifier, and peak detector) is shown in Figure. The first design parameter that needed to be derived was the value of C Ch, or the storage capacitor. The storage capacitor stores the voltage that corresponds to the maximum loudness of sound that has been observed within an active period. It is connected directly to the ADC port of the microcontroller, which samples the voltage level of C Ch at the end of an active period. The C Ch is discharged after sampling so that it could start at a very low level in the next active period. It must be noted that the storage capacitor is different from the boost capacitor, the value of which would also be derived in a later subsection. The size of the storage capacitor depends on the charging speed required of the system, which in turn depends on the desired sound frequency range covered by the detector.
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The maximum rate at which the the voltage across C Ch could change is either. Whichever is smaller. The values of SR 1 and IO max, or the slew rate and short-circuit current of OP-AMP 2 in Figure, are specified by the datasheet as 0.8 V μ s and 15 mA, respectively. The C Ch is then dependent on the desired rate of voltage change.
To derive this, we assume a maximum sound frequency of 10,000 Hz and require the system to be able to swing from the quiescent level to the upper rail (a 1.5 V swing) within a single cycle. A maximum frequency of 10,000 Hz was chosen for the initial design because most of the frequencies to which the human ear is sensitive can be found below 10,000 Hz. Referring to Figure, assuming that V Swing≈ V Amplitude and t 1≈ t 2, we can simplify the computation of d V Ch dt as. (8) where I lk is the leakage current. There are five sources of leakage current, namely: capacitor leakage, printed circuit board leakage, op-amp leakage, diode leakage, and reset switch leakage.
With the exception of op-amp leakage and diode leakage, all components of I lk are difficult to quantify. Diode leakage is negligible since an ultra-low-leakage diode was utilized for the design. That leaves the op-amp leakage or the input bias current of the op-amp, which the datasheet gives as 10 pA maximum. Thus, I lk = 10 pA. Using Equation and the value derived for C Ch, this gives us a voltage droop of 0.00002 V s. The voltage droop simply gives the rate at which the output of the peak detector decays over time.
The actual decrease in the output depends on how much time has elapsed since the peak value was stored by the circuit. Since we do not know the exact time at which the peak detector stored a new peak value, this introduces uncertainty in the output. This output is further affected by the length of the active period (which is dictated by the duty cycle, to be discussed later). Since the microcontroller does not sample the peak detector output until the end of an active period, the longer the active period, the more opportunity there is for the inaccuracy of the peak detector output value to increase - this is especially true for peak values that are recorded very early on in the active period. Design parameters - power supply side The amount of current that could be drawn from an energy storage device is limited by the device’s internal impedance.
To compensate for this, a capacitor is usually inserted between the energy storage device and the load. This capacitor would be called the boost capacitor, to differentiate it from the storage capacitor whose size was derived in a previous subsection. For the sake of brevity however, all references to ‘capacitor’ should be taken to mean ‘boost capacitor’. It must be noted that since the electrical charge has to be transferred from the energy storage device to the capacitor, it is still impossible to supply a high amount of current to the load for indefinite periods of time. For the same level of current draw, a longer draw period would necessitate using a larger capacitor to store a greater amount of electrical charge from the primary energy storage device.
However, a larger capacitor also takes longer to charge - therefore, the intervals between current draws, which we also call charge time, would also increase. The relationship between the capacitor size, the level of current draw (in milliamperes), the length of the current draw, and the interval between draws could be derived analytically and computed: for instance, the energy storage system that we utilize for our work, the EnerChip CC CBC3105 , specifies through an application note a formula for determining the capacitor size needed for supporting a specified level of current draw for a specified length of time. We state this in Equation. The equation for R Load, which is a variable in Equation, is defined in Equation. Equation and Equation ’s variables are defined in Table. For variables whose values remain constant across different computations, their values are specified in Table.