Light-Harvesting Wireless Sensors for Indoor Lighting Control

IEEE SENSORS JOURNAL, VOL. 13, NO. 12, DECEMBER 2013

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Light-Harvesting Wireless Sensors for Indoor Lighting Control Ashish Pandharipande and Shuai Li

Abstract— A light-harvesting wireless sensing system is proposed for indoor lighting control. A low-power light sensor and motion sensor operates on a photovoltaic cell that charges based on ambient daylight and artificial light. These sensors, respectively, measure the illuminance level and occupancy over their sensing regions. An ambient illuminance-aware sensing protocol is used to communicate this information using a wireless radio to a central controller. The controller employs a closedloop illuminance feedback control algorithm to determine the dimming levels of the luminaires such that a desired set-point at the light sensors is achieved. The proposed system design allows operation of the wireless sensing prototype module entirely on energy harvested from the ambient environment in a complete dark environment with a lifetime of 20 h, making the solution suitable for practical indoor lighting applications. Index Terms— Light-harvesting sensors, wireless sensing, indoor lighting control.

I. I NTRODUCTION

H

ARVESTING energy from the environment to power sensors is not only attractive from the viewpoint of efficient usage of energy, but also provides additional flexibility in system design. Solar energy is one of the energy sources in abundance; however, its availability is restricted during the day and is dependent on factors like weather conditions, and position and orientation of the harvesting node [1]. Daylight has also been exploited in lighting systems to control the amount of indoor artificial lighting adapted to the amount of illuminance already provided by daylight. In this paper, we consider a wireless light-harvesting sensing module, with light and motion sensors, that operates entirely on energy harvested from both daylight and artificial light. Sensing information from the modules is in turn used to design an occupancy and daylight adaptive lighting system. Wireless sensor networks have been employed for indoor lighting control [2]– [6], to obtain relevant sensing information related to light levels and occupancy. The sensor nodes and the wireless protocols used however are not amenable for operation on harvested energy. Further, given that sensors in lighting control applications are typically ceiling-mounted, where the amount of light for harvesting may be limited, there is a need for low-power design of the sensors and wireless protocols Manuscript received May 30, 2013; accepted June 22, 2013. Date of publication July 4, 2013; date of current version October 4, 2013. The associate editor coordinating the review of this paper and approving it for publication was Prof. Octavian Postolache. The authors are with Philips Research, Eindhoven 5656AE, The Netherlands (e-mail: [email protected]; [email protected]). Color versions of one or more of the figures in this paper are available online at http://ieeexplore.ieee.org. Digital Object Identifier 10.1109/JSEN.2013.2272073

used for communicating sensed information. Part of our work addresses the design of low-power light and motion sensors that can operate on light-harvested energy and an energyefficient wireless protocol to transmit sensed information. In this paper, we consider a zone-based lighting control system with a central controller and distributed sensing modules. The indoor space is divided into multiple logical zones, with each zone being monitored by a light sensor and a motion sensor that respectively determine the light level and occupancy over the zone. Information from the sensing modules is communicated using a wireless radio to a central controller. The target illumination rendering is to achieve an average illuminance value of at least L (o) over a zone under occupancy, an average illuminance level of at least L (u) if the zone is unoccupied but there is global presence, and to turn off the luminaires given global non-occupancy. The target illumination rendering is at the workspace plane, a horizontal plane parallel to the ceiling plane wherein the luminaires are situated, and specified as corresponding set-points at the light sensors. Based on the sensing information corresponding to the zones, the dimming levels for the luminaires are determined at the central controller to achieve the target illumination rendering. Towards this end, we consider a closed-loop illuminance feedback control algorithm based on classical ProportionalIntegral-Derivative (PID) control [7]. Experimental results are used to validate that the controller design provides stable illuminance values for the lighting system. We briefly summarize work from two fields related to our work: energy-harvesting sensors and indoor lighting control. In [8], a MAC protocol implementation for a solar-harvesting wireless sensor network for aquatic monitoring applications was described. An efficient photovoltaic harvesting circuit was presented in [9] based on automatic tracking of the maximum power point. Different MAC protocols based on CSMA and polling were analyzed in [10] in terms of network performance metrics for wireless sensor networks that rely entirely on energy harvesting. In [11], the availability of light energy was characterized in indoor environments based on long-term measurements and energy allocation policies were designed for energy harvesting devices using stochastic energy models. Simple statistical energy models were presented in [12] for harvested energy based on measurements using a Texas Instruments eZ430-RF2500-SEH platform [13]. A power management circuit was designed in [14] for integrated hybrid harvesting of indoor ambient light and thermal energy. None of these works has however considered light-harvesting sensors and networking design aspects for use in indoor lighting control applications.

1530-437X © 2013 IEEE

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Fig. 1.

Lighting control system.

For a centralized lighting control system, a simplex algorithm was used in [15] to solve the resulting optimization problem for achieving occupancy adaptive illumination rendering, with the system extended in [16] to take into account spatio-temporal daylight variations. A distributed optimization algorithm for lighting control with daylight and occupancy adaptation was proposed in [3], under networking and information exchange constraints. Under different system settings, linear programming and sequential quadratic programming approaches were proposed for centralized lighting control [4], [6]. In [17], lighting systems were evaluated in an experimental testbed under different control schemes. None of these works considered energy harvesting sensors. Further, an implicit assumption in these lighting control schemes was that the sensors sample the environment fast enough, the control adaptation rate is high and that the luminaires can be driven at a high rate so that occupants do not experience visible light fluctuation during actuation. The lighting control problem was hence treated as an optimization problem in these works. However, in practice, with hardware limitations it is not possible to implement fast control adaptations especially when considering energy-harvesting sensors that may only be able to sample at low rates. As such, the control design needs to take availability of low-rate sensing data into account, with the constraint that occupants do not find light adaptation visually disturbing. The rest of the paper is organized as follows. Section II provides an overview of the lighting control system, including the light-harvesting wireless sensing module. In Section III, we present a prototype implementation of the light-harvesting wireless sensing module, including practical considerations in designing a low-power ceiling-mounted sensor that can harvest ambient light. The wireless sensing protocol to communicate sensed information to a central controller is described in Section IV. A closed-loop illuminance feedback control algorithm based on a PID controller using Ziegler-Nichols weight settings [18] is presented in Section V. Experimental results to evaluate the performance of the lighting control system are shown in Section VI. Conclusions and discussions are provided in Section VII. II. S YSTEM D ESCRIPTION In Fig. 1, we illustrate the indoor lighting control system driven by light-harvesting wireless sensing modules.

Fig. 2. Light-harvesting wireless sensing module: Light sensor and motion sensor on eZ430-RF-2500-SEH.

The ceiling-mounted light-harvesting wireless sensing module consists of a light sensor, a motion sensor, an energy harvesting device, an energy storage device, a micro-controller unit (MCU) and a wireless transceiver. A ceiling-mounted configuration ensures that light sensor measurements are less sensitive to reflectance changes induced by occupant movement, object placements etc. A ceiling-mounted motion sensor allows for good detection coverage in such a configuration. Both sensors draw power from the energy storage device. The energy storage device is driven by the energy-harvesting device, with the amount of electrical energy depending on amount of light intensity. Sensor sampling is controlled by the MCU. Sensing information is communicated to a central controller via a wireless transceiver. This information consists of light levels and occupancy over zones, where a zone is a logical partitioning of the physical space. Based on light level and occupancy information, the central controller determines the dimming levels of the luminaires in the lighting system to achieve daylight and occupancy adaptive lighting control. The control objective is to achieve an average illuminance level of L (o) given local zone occupancy, an average illuminance level of L (u) given local zone non-occupancy and global presence, and turning off the luminaires given global nonoccupancy. The workspace illuminance levels are specified in terms of set-points at the corresponding light sensor that the controller seeks to achieve. The controller should further result in stable illuminance values for the lighting system for a good illumination experience for occupants. III. L IGHT-H ARVESTING W IRELESS S ENSOR P ROTOTYPE The light-harvesting wireless sensing module with light and motion sensors is depicted in Fig. 2. We implemented the light and motion sensors needed for daylight and occupancy adaptive lighting control on the eZ430-RF-2500-SEH development kit [13]. We first summarize the key features of this development board [13], [19]. The core of the energy harvesting module is the CBC-PV-01 photovoltaic cell that converts ambient light into electrical energy. The energy is managed and stored in a pair of thin-film rechargeable 50 μAh CBC 5300 EnerChip solid-state devices connected in parallel. The EnerChips act as an energy buffer, storing energy with a very low self-discharge. The voltage from the photovoltaic cells is increased using a boost converter to a level sufficient to charge the EnerChips and power the rest of the system.

PANDHARIPANDE AND LI: LIGHT-HARVESTING WIRELESS SENSORS FOR INDOOR LIGHTING CONTROL

The output of the boost converter is monitored continuously by a charge control block. The charge controller disconnects the boost converter from the EnerChips if the output of the boost converter falls below the voltage needed to charge the EnerChips, thus preventing them from back powering at low light levels. A power management block is used to protect the EnerChips from discharging too deeply under low light levels or abnormally high load currents. The power management block has a control line, CHARGE, for indicating that the photovoltaic cells are charging the EnerChips. The development board is equipped with a low-power MSP430 micro-controller unit and a CC2500 2.4 GHz wireless transceiver and includes a USB to interface between the wireless radio and a PC. Under typical ambient light conditions, the minimum operating illuminance to charge the EnerChips is 200 lux, with 700 lux needed for full charge rate. The orientation and positioning of the photovoltaic cells must hence be carefully chosen in order to optimize the ambient light harvesting capability. This design is driven by two considerations, given that we desire a ceiling-mounted configuration: the shading of the photovoltaic cell and typical illuminance field characteristics. A. Orientation Considerations and Integration with Luminaire When placing the light-harvesting wireless sensing module in a ceiling-mounted configuration for integrating with the luminaire, shading of the photovoltaic cell needs to be accounted for. To evaluate this effect, we exposed the photovoltaic cell to illumination levels of 500 lux but covered the photovoltaic cell in varying degrees with a dark material. The control line CHARGE was monitored to determine a successful charge. We found that a substantial portion of the photovoltaic cell needs to be exposed if the module is to be placed close to the luminaire, while allowing for the option to harvest from daylight as well. In the ceiling-mounted configuration depicted in Fig. 3, about 3500 lux light level is recorded at the photovoltaic cell when it is placed vertically at one end of a fully on LED luminaire. Light level measurements were done using a light meter. This positioning is able to provide the EnerChips with full charge rate when the luminaire is on. Further by orienting the photovoltaic cell towards the window, it is possible to harvest daylight as well. This implementation is favorable to other design options, e.g, choosing two eZ430 kits with one facing the luminaire and another the window. In SectionVI-A, we provide experimental results to characterize illuminance values under different possible sensor orientations in an office space. B. Sensor Design Due to limited levels of light intensity at ceiling positions indoors and low conversion efficiency of the photovoltaic cell, it is necessary to have a low-power sensor design for reduced power consumption. The EnerChips are capable of supplying continuous power in the order of tens of μW to the load. Its pulse discharge current reaches 30 mA within 20 ms [19]. With these design limitations, the light sensor and motion sensor should be carefully chosen and interfaced with the

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Fig. 3. Illustration showing oriented of light-harvesting wireless sensing module at luminaire.

EnerChips such that their maximum current and power consumption at both operating mode and standby mode meet these constraints. 1) Light Sensor: We chose an analog light sensor as these are low-cost and consume less power since they are passive components, modeled by either a capacitor or resistor. A type of analog light sensor is a light dependent resistor (LDR). An LDR has low current consumption (3 μA to 60 μA), is inexpensive and can be easily mounted on a PCB board. The APDS9004 light sensor was used in our implementation. The APDS9004 is a low cost analog-output light sensor. It provides good output linearity across a wide illumination range and has an ultra-small package design, making it suitable for lighting control applications. Its supply voltage varies from 2.4 V to 5.5 V, and can be powered by the general purpose input/output (GPIO) pin of the MCU. Hence, the MCU can be programmed to power the light sensor only when it is necessary in order to lower energy consumption. A 1 k load resistor is connected to the light sensor output in order to convert photo-current to voltage. The voltage is indicative of the illuminance value, and a conversion needs to be done to translate the output voltage of the light sensor to a lux reading. 2) Motion Sensor: We chose a passive infrared (PIR) based motion sensor since it is suitable for low-power design. A PIR sensor detects movement of a person based on the induced temperature changes. In our implementation, the 1-μA type Panasonic PIR motion sensor (PaPIR) was chosen; PaPIRs sensing circuits are enclosed in a metallic can to minimize adverse effects of external electromagnetic fields and minimize sensitivity to false tripping under various operating environments. The PaPIR is a digital motion sensor and outputs two binary states to indicate presence/absence of an occupant. This has two advantages: firstly, power is further reduced without activating the analog-to-digital converter; secondly, it is possible to develop an interrupt-driven sensing protocol since the output pin of the PIR sensor can be directly connected to the GPIO of the MCU. Experiments show the power consumption of the sensor module to be 8 μW in standby mode and 700 μW in operating mode. Such a sensor module can be driven by the CBC-PV-01 photovoltaic cells under consideration as the energy-harvesting device.

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Fig. 4.

Wireless sensing protocol.

IV. W IRELESS S ENSING P ROTOCOL We now describe our wireless sensing protocol used to communicate between the light-harvesting wireless sensing modules and the central controller. This application layer protocol is on top of SimpliciTI™ [20], a low-power RF protocol for direct device-to-device low data rate communications in star network topologies. The protocol includes collision avoidance and receiver acknowledgement features for enhanced reliability. To illustrate how the sensing protocol works, let us consider Fig. 4. An example daylight and occupancy scenario is shown in Fig. 4(a)-(b), where “none”, “partial” and “sufficient” indicate the amount of daylight at workspace plane w.r.t. the required level of illuminance, and “on” and “off” indicate occupancy and non-occupancy respectively. Corresponding to the daylight and occupancy states, the dimming status of the luminaires is shown in Fig. 4(c), with the three states “OFF”, “ON” and “Dim” indicative of the lighting control mechanism. Once occupancy is detected by the PIR sensor, it outputs a raise edge which actives a GPIO interrupt on the MCU. The PIR interrupt enable signal is active during nonoccupancy and is duty-cycled during periods of occupancy as shown in Fig. 4(d). Within the interrupt service routine (ISR), the PIR interrupt is disabled and the MCU wakes up from low-power mode. The MCU first sets the GPIO pins which are connected to the light sensor so as to measure the current ambient illuminance, then it resets those pins in order to switch off the light sensor and save power. A timer clock inside the MCU is set to run at 1 Hz, as shown in Fig. 4(e), to synchronize light sensor sampling as well as the updating of dimming levels. The light sensor makes a measurement every 5 s as shown in Fig. 4(f). The light sensor may be made to sample more frequently, as illustrated in Fig. 4(f), when daylight illuminance is sufficiently high since the EnerChips are properly charged. This has the advantage that in case daylight levels suddenly reduce significantly, the lighting controller may adapt dimming levels quickly based on illuminance readings from light sensors. With the sensor values

as payload data, the MCU wakes up the RF module, as shown in Fig. 4(g), and sends a packet to the access point/central controller within a few ms. After that, the MCU enters the low-power mode and sets the control signal to enable charging of EnerChips by the photovoltaic cell for a specific period (4 s in our implementation), as depicted in Fig. 4(h). If the PIR sensor does not detect motion for a certain amount of time, it declares non-occupancy. The last instruction given by the MCU before it enters low-power mode is to enable the PIR interrupt. When a PIR interrupt is captured, the MCU wakes up from low-power mode again. Under sufficient light intensity levels, the energy-harvesting module can power the sensor node with ambient light energy. After the luminaires are off, the sensor node continues working using buffered energy in EnerChips. The wireless sensing protocol is adapted to perform the light sensing and communication operations depending on harvested energy. The development board has two control lines that can be connected to the MCU for efficient utilization of incoming power and extending the life of the EnerChips. BATOFF is a bit-wise input port which receives an instruction from the MCU to either close or open a switch connecting the photovoltaic cell to the EnerChips. The switch is closed if there is sufficient light intensity, determined by output port Vout ; else the switch is open, preventing the EnerChips from charging. As such when the light intensity levels are low, the harvested energy is used for sensing and radio transmission. When sensing and radio transmission is completed, BATOFF is set to 0 to enable charging of the EnerChips and the MCU enters low-power mode. CHARGE is a bit-wise output port which indicates whether ambient energy is sufficient to charge the EnerChips or not. Based on the status of this signal, MCU controls the light sensor and the RF module to respectively sense and transmit at a higher duty cycle (e.g. 1 s) as long as the EnerChips are charged. On the other hand, the MCU could decrease duty cycle (e.g. 5 s) once it detects that ambient energy is too low to charge the EnerChips and the stored energy is low. Vout is a bit-wise output port of the EnerChip which connects directly to the analog-todigital converter channel of the MCU. Before waking up the RF module, the MCU monitors the currently supplied voltage and compares it with the minimum operating voltage (3 V in current implementation). The MCU enables the EnerChips to charge for 1 s and enters low-power mode if the currently supplied voltage is lower than the minimum operating voltage; otherwise, the MCU immediately wakes up the RF module and transmits a packet. V. L IGHTING C ONTROL We consider zone-based lighting control. The indoor space is divided into logical zones, with a light-harvesting wireless sensing module sensing over a zone and a subset of luminaires associated to it. The light sensor measures light levels in its respective zone, the motion sensor detects occupancy over this zone and the wireless transceiver then sends out the sensing information to the central controller as defined by the wireless sensing protocol described earlier. Based on


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TABLE I I LLUMINANCE VALUES (LUX) FOR D IFFERENT O RIENTATION OF THE P HOTOVOLTAIC C ELL

Fig. 5.

Block diagram of closed-loop feedback illumination controller.

occupancy information in each zone and over the entire space, corresponding set-points at the light sensors are determined. A calibrated set-point is determined in a dark room setting. This involves dimming the lighting system at various levels and measuring the illuminance values at the workspace plane and at the light sensors. Thus, the light sensor measurement that corresponds to an average illuminance value of L (o) and L (u) are the respective set-points to be achieved by the controller under local zone occupancy and local zone nonoccupany/global occupancy. Fig. 5 shows a block diagram of the controller to achieve the set-point at a particular light sensor in a zone. The setpoint value, z (s), is known based on the occupancy state s. At iteration n, an error signal en = z (s) − x n is generated as a difference between the set-point and the current light sensor measurement, in lux. The error signal is input to a PID controller, whose P, I and D control branches respectively weigh the error signal, an accumulation of past errors and a differential of the error. The generated output is given by yn = K P en +

J −1 KI en− j + K D (en − en−1 ), J

(1)

j =0

where K P , K I and K D are the weights of the P, I and D control branches respectively, and averaging in the I-branch is over J samples. We chose these weights using the classic Ziegler-Nichols setting [18]. The output signal is in lux and translated into a dimming level using function f 1−1 , where function f 1 is a mapping of dimming levels of luminaires in a zone to illuminance value measured at the corresponding light sensor. This resulting dimming level dn is applied at the associated luminaires. The resulting artificial light output gets superposed with daylight and the total illumination is captured by light sensor measurements over their sensing regions. The light sensor reading is a voltage output and is translated into a lux value. This translation is achieved by the function fc . The mapping f c is determined experimentally, but also may be obtained from the light sensor data sheet if available. The control algorithm stops once the error en is within a prescribed bound. The expected dimming response of the lighting control system is as follows. When there is global occupancy from a non-occupancy state, the luminaires dim up to the level that results in an average illuminance value of L (u) over the workspace plane, i.e. the light sensor set-point is z (u) . The control is applied once zone occupancy changes to maintain the corresponding set-point adapted to daylight changes. For example, when a zone becomes occupied, a new dimming level is determined such that at least an average value of

L (o) over the workspace plane is attained, i.e. the light sensor set-point is z (o). If a zone subsequently becomes unoccupied under global presence, a new dimming level is determined such that at least an average value of L (u) over the workspace plane is attained, i.e. the light sensor set-point is z (u) . VI. E XPERIMENTAL R ESULTS We evaluated the light-harvesting wireless sensing module for an office lighting control application. Experiments were performed in an office space, about 8 m in length, 6.5 m in width and 3 m in height, with the workspace plane about 2.2 m from the ceiling. Daylight entered through a large window situated at one end along the length of the room. The lighting system consisted of eight LED luminaires arranged in two rows of four luminaires, with a luminaire spacing of about 2 m. Each luminaire contained two Philips Master LEDtubes type GA 1200 mm 22W 840 G13 with a luminous flux output of 1500 lumen. Two light-harvesting wireless sensing modules were ceiling-mounted at two of the luminaires (see Fig. 1) for a depiction of position) and the access point/central controller was at a PC. The office space was as such partitioned into two zones. A. Illuminance Characterization As discussed earlier in Section III-A, proper orientation of the light-harvesting wireless sensing module is critical so that the EnerChip may be able to charge properly and sufficiently to support the lighting control application. We characterized the illuminance field at different possible sensor positions close to the luminaire, and recorded the measured illuminance values with only the lighting system at maximum dimming and then with only daylight, under overcast sky conditions. From Table I, it is evident that in the configuration where the photovoltaic cell is parallel to and facing the luminaire, the energy-harvesting can utilize only artificial light but not daylight. In the last configuration, the photovoltaic cell cannot effectively harvest light from the lighting system, and can harvest from daylight only when there is ample daylight contribution at the ceiling, which is not the case under overcast sky conditions as seen from Table I.

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Fig. 6.

Calibration function fc .

Fig. 7.

Calibration function f1 .

Fig. 8.

Calibration function f2 .

While the first three configurations are suitable to harvesting from artificial light, it is only when the sensor is facing the window that there is sufficient illuminance for lightharvesting. Note that illuminance levels due to daylight would be even higher in clear sky, sunny conditions. Hence the design orientation used was with the photovoltaic cell facing the window and perpendicular to the luminaire (first configuration in Table I) and the sensors’ field of view towards the workspace plane (orientation of the sensing module illustrated earlier in Fig. 3). B. Calibration Functions We now describe how the calibration functions f c , that relates the output voltage of the light sensor to a lux reading; f 1 , that relates the dimming level of the luminaires to a lux values at corresponding light sensors; and f2 , that relates the dimming level to illuminance at the workspace plane, are obtained. All measurements for this procedure were done in dark-room conditions. 1) Function f c : The light sensor was first placed at the workspace plane below the luminaires, with a light meter close to it, and their respective readings were recorded as the luminaires were dimmed from 0 to 1 in steps of 0.1. The corresponding output voltage and lux values were found to follow a linear behavior, and a linear fitting curve was obtained using these measurements, resulting in the function f c (v) = 0.56v + 22.4, where v is the voltage, as shown in Fig. 6. 2) Function f 1 : The light sensor was placed in the ceilingmounted configuration depicted in Fig. 3, at the LED luminaire and facing downwards towards the workspace plane. The lighting control system adjusted the dimming level of the luminaires from 0 to 1 in steps of 0.1, and the corresponding values in lux at the light sensor were recorded. The measurements were found to follow a linear curve as shown in Fig. 7. A linear fit was hence obtained resulting in function f 1 (d) = 86d + 16. The offset was canceled out in the lighting control algorithm. 3) Function f 2 : A light meter was placed at the workspace plane, and the lighting system was dimmed from 0 to 1 with steps of 0.1. The illuminance values from the light meter

were recorded. The relationship between dimming level and illuminance values was found to be linear, as expected. A linear fit was obtained to the measurements as shown in Fig. 8, resulting in f 2 (d) = 552.5d + 7.04, where the small offset was accounted for in illuminance value calculations. Note that functions f 1 and f 2 depend on the specific room environment, such as objects in the room, reflectance of objects, distance of workspace plane from ceiling, etc. Hence, this calibration must be conducted during system setup, subsequent to large renovation changes in the room environment. The calibration functions are known and stored at the central controller. C. System Performance With PID Control We now evaluate the performance of the occupancy and daylight adaptive lighting control system. First, consider the PID controller (1) with Ziegler-Nichols weights [18], K P = 0.48, K I = 0.1371 and K D = 0.42, and J = 5. The update rate of the implemented PID controller is 1 Hz. The control stopping criterion was that if mean(en ) < 5 lux, stop dimming, where the mean value was computed over 5 samples. The PID controller is implemented as a centralized computer-controlled system. The light-harvesting wireless


Fig. 9.

Fig. 10.

Dimming level over control iterations.

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Fig. 11. Dimming levels of luminaries in the two zones over control iterations with dynamic occupancy and daylight.

Light sensor measurement over control iterations. Fig. 12. Light sensor measurement in the two zones over control iterations with dynamic occupancy and daylight.

sensing hardware module is interfaced to Matlab, where the control algorithms runs. As such, energy requirements of the PID controller are not taken into consideration as they do not have implications on the harvesting mechanism. Figs. 9 and 10 show respectively the dimming levels and light sensor measurements in one of the zones corresponding to different workspace illuminance values, under no daylight condition. From Figs. 9 and 10, it is clear that the controller reaches steady state within a small number of iterations, and no noticeable oscillations are observed thereafter. Further, the algorithm converges close to the set-point illuminance value that corresponds to the illuminance value at the workspace plane. As an example, the set-point for 500 lux at the workspace plane is around 95 lux, and the control algorithm converges to 93 lux as seen in Fig. 10. The small variations in light sensor measurements in steady-state seen in Fig. 10 may be attributed to movements in the environment and sensor reading fluctuation. In Figs. 11 and 12, we show the performance of the lighting system for a dynamic occupancy and daylight scenario.

The dimming levels of the luminaires and the light sensor measurements in the two zones are shown respectively in these plots. We chose L (o) = 500 lux and L (u) = 300 lux, following recommended norms for office workspace lighting [21]. The corresponding set-points were z (o) = 95 lux and z (u) = 61 lux at the light sensors. From Fig. 11, we can see that as the amount of daylight increases, the dimming level in occupied zone 1 goes lower while the light sensor maintains a steadystate value close to the target set-point, as seen in Fig. 12.

D. Lifetime of the Sensing Module We now calculate the lifetime of the designed wireless sensing module. For this, consider the condition that there is insufficient illuminance to charge the EnerChips and there is non-occupancy. As such the sensing module, which is in standby mode with the PIR interrupt active, operates on buffered energy in the EnerChips. The capacity of the

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EnerChips may be calculated as [19] E = 100 μAh × 3.2 V = 320 μWh. By design, in standby mode, the wireless sensing module has a power consumption of Pst andby = 8 μW. Thus, at 50% energy charge of the EnerChip capacity, the lifetime is given by Tli f e =

50% × E 50% × 320 μWh = = 20 hr s. Pst andby 8 μW

(2)

This estimated lifetime was validated using our sensor prototype by experiments, under dark-room and non-occupancy conditions. VII. C ONCLUSION We presented a wireless sensing module operating entirely on energy harvested from daylight and an artificial lighting system. A suitable orientation of the ceiling-mounted module was determined to be one vertical to the luminaire and facing direction of daylight ingress. A low-power design for the light sensor and motion sensor, as well as for the wireless sensing protocol, were proposed. The light-harvesting wireless sensing module was functionally built on the eZ430-RF2500-SEH board to demonstrate the sensing and lighting control application. Light and motion sensing information were then used in a PID controller with Zeigler-Nichols weights. Experimental results showed that this design achieved stable illumination control. Results further showed that the sensing module can operate for at least 20 hours on a 50% EnerChip capacity. As more flexible and low-cost light-harvesting devices emerge, embedded integration of batteryless sensors that function perpetually on harvested energy into luminaires will become an important challenge. In addition, controlling lighting systems reliably based on sensing information from energy harvesting wireless sensors will be another technical challenge. R EFERENCES [1] J. M. Gilbert and F. Balouchi, “Comparison of energy harvesting systems for wireless sensor networks,” Int. J. Autom. Comput., vol. 5, no. 4, pp. 334–347, 2008. [2] F. J. Bellido-Outeirino, J. M. Flores-Arias, F. Domingo-Perez, A. Gil-deCastro, and A. Moreno-Munoz, “Building lighting automation through the integration of DALI with wireless sensor networks,” IEEE Trans. Consumer Electron., vol. 58, no. 1, pp. 47–52, Feb. 2012. [3] D. Caicedo and A. Pandharipande, “Distributed illumination control with local sensing and actuation in networked lighting systems,” IEEE Sensors J., vol. 13, no. 3, pp. 1092–1104, Mar. 2013. [4] M.-S. Pan, L.-W. Yeh, Y.-A. Chen, Y.-H. Lin, and Y.-C. Tseng, “A WSN-based intelligent light control system considering user activities and profiles,” IEEE Sensors J., vol. 8, no. 10, pp. 1710–1721, Oct. 2008.

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Ashish Pandharipande photograph and biography are not available at the time of publication.

Shuai Li photograph and biography are not available at the time of publication.