9838 and 0.9937, which indicated 98.38% and 99.37% of variability in the response could be explained by the model. Therefore, the present R2-values reflected a very good fit (>0.9) between the experimental and predicted values [17].In addition, the R2Adj (0.9353 and 0.9997) were satisfactory, which confirms the aptness of the model. Moreover, the adequate precision (12.57 and 170.96) shows remarkable signal (4). This ensured model (quadratic) was suitable to navigate the design space and provide a satisfactory match of the polynomial model to the experimental data.3.2. The Quadratic Expression ModelIt is normal to describe experimental data by forming a mathematical relationship between the factors (independent variables) and responses (dependent variables).
The final model to describe the relationship of the energy band gap and surface roughness with control factors is shown in Equations (2) and (3), respectively, as follows:Y1=0.44266?0.14248X
In recent years, wireless sensor networks (WSNs) have seen tremendous applications in different aspects of our lives such as habitat, structure health and remote health monitoring, precision agriculture, home automation, smart electric grids, and intelligent transportations systems. Typically, a large number of tiny computing devices (nodes) constitute a WSN where nodes are considered as constrained in resources, i.e., with limited on-board memory, short-range radio transceivers, and battery power. Depending on the application environment, nodes are interfaced with various sensors for monitoring some phenomenon of interest (temperature, humidity, pressure, etc.
) and forward sensory data to special devices (sinks) in a cooperative manner (typically multi-hop). The sink device (base-station) upon receiving the sensory data analyses the reported activity and may further route the data to a remote user/database via some regular infrastructure such as the Internet [1]. A typical WSN architecture is illustrated in Figure 1.Figure 1.Wireless sensors network.Nodes in a sensor network are battery operated and in most situations, battery replacement or recharging is not viable. To achieve prolonged network lifetime, sensor nodes must tailor their activities in an energy-efficient way so that the scarce energy reserves are used very efficiently. Upon deployment, sensor nodes sense, process and communicate an observed phenomenon.
Among these tasks, Dacomitinib communication is considered as the main consumer of sensor energy reserves, thereby imposing strict energy-aware constraints on all communication activities by the sensor nodes [2]. Since routing protocols and media access control (MAC) protocols are directly related to the communication module, hence protocols at these two layers must make an intelligent utilization of the scarce energy resources.