# Design, Implementation and Power Analysis of
## 1 Introduction
The three criteria were lighting levels between ceiling and working plane, lighting capability and energy savings. In a daylight control system, various parameters [15] were calculated and the energy savings were proved to vary in different places and under different conditions. Occupancy detection control is modeled by checking the motion or presence to trigger the light bulbs. This can also be implemented with vacancy sensors. Various occupation detection methodologies were presented in a review of occupancy sensing methodologies [16; 17]. In conditions where occupants stay active for all time, dimming is found to save more energy [18]. Individual tuning [19; 20] is adjustment of the light intensity level to the user's comfort which is categorized as personal tuning. This control technique was selected by single users. In centralized tuning or institutional tuning [21; 22] a cluster of light bulbs is controlled by location or building- specific conditions. In image-based control the luminance was calculated by the image captured by a low cost camera [23], CMOS low resolution camera [24] or CCD camera [25]. In video analytics-based control [26], lights in places like underground garages were controlled by video analytics. Artificial neural networks, deep learning techniques and machine learning are the most recent methods deployed to control the light [27; 28]. When one or more of the above discussed techniques [29; 30; 31] are combined, they form a hybrid control technique. [PERSON] [6] proposed a smart LED lighting system with the use of a wireless light dimmer, a multimeter, IR modules, a touch pad as a human computer interface and a self-adaptive weighted data fusion algorithm. A smart home energy management was built with a low power microprocessor using light, humidity and temperature sensors along with a disjoint multipath routing protocol, Zigbee and IEEE 802.15.4 [31]. A LED lighting system for green buildings was presented with CC2530 for Zigbee management, MSP430 for smoothing control of LED panel, and a dimmable LPF-40D-42 MeanWell commercial driver [32]. Lighting control for multipurpose outdoor environments [33] addressed efficiency at the component level and optimized the performance of LED drivers at the system level, by defining the control strategy and associated hardware infrastructure. [PERSON] et al. developed a lighting control system for historical buildings using daylight harvesting, absence control, Thermoken SR-MDS solar sensors, Eltako switches, Eltako actuators and ST-Microelectronics smart plug [34]. An automation system for controlling streetlights [28], was designed with an Arduino microcontroller, a light dependent resistor (LDR) and infrared-sensors. In this work, the number of vehicles crossing the street lights was also counted. [PERSON] et al. analyzed the energy saving potential of various lighting control strategies [35]. A wireless mesh networked lighting system [25], was considered with daylight and occupancy adaptive control through multiple sensor-equipped luminaires and a central controller. Building-in-Briefcase (BiB) [17] is a portable sensor network platform for continuous monitoring of building occupancy and the environment. A lighting automatic control system [36] made adjustments in lighting intensity, incorporating user illumination requirements based on their activities and achieved changes based on external lighting. An architecture [21], centralizing the control of lighting was merged with numerous artificial intelligence methods. After a study of the relevant literature, the task is to provide one common solution for both indoor and outdoor lighting solutions. Also, the proposed model is suitable for developing countries that have both incandescent and light emitting diode bulbs in use. The Pervasive Adaptive Resourceful Smart Lighting and Alerting Device (PARSLAD) has an alarm system in the operation of the alert mode, so, monitoring and control can be achieved with one device.