This section presents our implementation of the intelligent light control system. Fig. 5.1 shows the system architecture and the related protocol components. The control host can make deci-sions and send them to lighting devices. We test our system in an office with DALI lighting devices deployed. Below, we introduce each device, and then give our implementation results.
5.1 Light sensor
Our sensor nodes has a wireless module Jennic (JN5139) [13], a light sensor (TSL230) [14], and some input buttons (Fig. 5.2). JN5139 is a single-chip microprocessor with an IEEE 802.15.4 module [15]. Light sensors periodically report aggregated light intensity values to the sink. The sink forwards sensing data to the control host via the RS-232.
5.2 DALI module
In our current implementation, DALI lighting devices are controlled by DALI protocol. The DALI controller (Fig. 5.3(a)) issues DALI device control commands to the Osram ballasts (Fig. 5.3(b)) through the DALI bus. Then the Osram ballasts control setting for the light in-tensity control based on the control results. Our ballast
5.3 Control Host
The Control Host (Fig. 5.4(b)) implemented by JAVA handles the DALI module and sensors, is the core of our system. It is composed of three components, including the Decision Handler, Device Controller, and Sensor data handler. By applying Java thread programming techniques, tasks are handled concurrently.
Figure 5.1: System architecture.
Figure 5.2: The implemented sensor board.
Figure 5.3: DALI controller and DALI ballasts(a)(b).
Sensor data handler: Its main task is to classify the light intensity report data from the sink.
Then it relays these data to the corresponding components.
Decision Handler: This component realizes our control algorithms. It is triggered by setting the scenes. The linear and nonlinear programming are resolved by the MATLAB Builder for Java [16]. The results are sent to the DALI controller to adjust lighting devices.
Device Controller: This is the interface between the control host and the DALI controller. Com-mands are sent via RS232 to the DALI controller.
The user can carry out functions required such as all on/off, select scene , single lamp control, scan state and so on, which the DALI general permit by this control host.
5.4 Results
To determine the effectiveness of our intelligent light control system, operational experiments under the office was examined to verify whether the target illuminance could be realized and sustained. Fig. 5.4 shows the demo environment of our system. An office was used for the simulation of the lighting control algorithm developed. The Fig. 5.5 shows the plain view of the experiment office of the DALI fluorescent lamps and the illuminance sensor inside, which has a floor area of about 30m2, and the office contains one circle desk with some chairs, the projector screen at the head of the office, and the glass table and sofa for resting at the back of office. We deploy four sensors S1, S2, ..., S4 in the office that S1 is closed to the projector screen, S2 and S3 were placed on the circle desk, and S4 is placed on the table. We set the four sensor’s requirements and the power constraint of each scene. For using projector screen, we let S1 requirement that closer to the projector screen is low. Conversely, it need more high requirement when S2,S3, and S4 is far from screen. When people work at the circle desk, S2 and S3 requirements on the desk need high and S1 and S4 need not consider. If people want to eat a meal or rest at the sofa, people can set low requirement at S4 that on the glass table.
Then We measure the received illumination of each sensor and show light intensity of DALI fluorescent lamps of different scenes and power constraints.
The first case considers the projector screen is used in the office where four sensors’s re-quirement are setup for S1= [0, 150], S2= [200, 400], S3 = [200, 400], S4= [300, 500], and the power constraint is 100 watt. The optimal light settings determined by the algorithm for each
Figure 5.4: The demonstration environment of our intelligent light control system(a)(b).
Figure 5.5: Office floor plan for the simulation.
of the four luminaires are [0%, 27%, 29%, 92%] ordered by the numbers annotated in Fig. 5.5 and the consumption power is about 84 watt.
The second case considers people is working at the circle desk where four sensors’s require-ment are setup for S1 = [0, 100], S2 = [300, 500], S3 = [300, 500], S4 = [0, 100]. The optimal light settings determined by the algorithm for each of the four luminaires are [0%, 54%, 56%, 0%].
The third case considers people is resting at the glass table where four sensors’s requirement are setup for S1 = [0, 100], S2 = [0, 100], S3 = [0, 100], S4 = [150, 250]. The optimal light settings determined by the algorithm for each of the four luminaires are [0%, 0%, 0%, 40%].
The fourth case assumes the same situation with the first case presents, but the power con-straint is less than 50 watt. The optimal light settings determined by the algorithm for each of the four luminaires are [0%, 24%, 26%, 42%] and the consumption power is about 50 watt.
Chapter 6 Conclusions
In this paper, we have presented a WSN-based intelligent light control system considering scenes. For controlling DALI lighting devices, a decision algorithm is proposed and two models for power constraint are considered. We use wireless sensors to collect natural light intensities in the environment. Our system can dynamically adapt to environment light changes. Consider-ing users activities of scenes, we model the illumination requirements of sensors. Illumination decision algorithms and a device control algorithm are presented to meet sensor requirements and to satisfy power constraint. In our work, we presented a provided efficient algorithms for optimally trading off between sensor requirements and power constraint. The proposed schemes are verified by real implementation in an indoor environment. Future directions could be directed to removing the control host and evaluate the result using the distributed algorithm.
In the utility-based method of phase2, we adopt utility functions to represent users satisfaction levels. However, the utility to human, in terms of light intensity, is still an unknown factor. This may deserve further study in the medical science field.
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