A Novel Design of an Automatic Lighting Control System for a Wireless Sensor Network with Increased Sensor Lifetime and Reduced Sensor Numbers

Wireless sensor networks (WSN) are currently being applied to energy conservation applications such as light control. We propose a design for such a system called a Lighting Automatic Control System (LACS). The LACS system contains a centralized or distributed architecture determined by application requirements and space usage. The system optimizes the calculations and communications for lighting intensity, incorporates user illumination requirements according to their activities and performs adjustments based on external lighting effects in external sensor and external sensor-less architectures. Methods are proposed for reducing the number of sensors required and increasing the lifetime of those used, for considerably reduced energy consumption. Additionally we suggest methods for improving uniformity of illuminance distribution on a workplane’s surface, which improves user satisfaction. Finally simulation results are presented to verify the effectiveness of our design

Modeling and Evaluating the Scalability of Instruction Fetching in Superscalar Processors

Scalability is important in superscalar processors design. A superscalar processor is said to be linearly scalable if with linear increase in load or demand, performance remains constant relative to linear increase in resources. In this paper, for evaluating the instruction fetching scalability, an analytical model of a superscalar processor is proposed by defining the fetch unit as the “producer ” of instructions and the execution unit as the “consumer.” The scalability of the fetch unit relative to its branch predictor – the Bi-Mode Predictor – is then evaluated using SPEC2000 suite of benchmarks. Our simulation results strongly suggest that reducing branch misprediction penalty is a better alternative solution – compared with increasing prediction accuracy – for improving instruction fetch scalability. 1. Summary and future work In this paper, we composed an analytical model for evaluating instruction fetching scalability relative to branch prediction accuracy. For this purpose, we decoupled the fetch and execution engines using the model proposed in [1] and assumed the instruction cache to be totally perfect and based the fetch engine performance only on branch prediction accuracy. We defined the performance of the fetch unit as the number of instructions it produces in each cycle based on the prediction it makes for control instructions. However, this performance is dependent on the execution unit which informs the fetch unit of the resolved outcomes of branch instructions and keeps it on the correct execution path. To assess the scalability of the fetch unit, we proposed an analytical fetching model and developed formulas for measuring the fetch performance. Our formulas dictated two factors are necessary for evaluating the fetch engine performance