Radio optimization schemes are further divided into three categories such as schemes based on power control in transmission, cooperative communication schemes, and also modulation optimization schemes.
In WSN, one of the main units in which heavy energy consumption occurs is the radio module. Therefore, some power control mechanism is needed for transmission. In the adaptive transmission power control framework, a separate model representing the relationship between power needed for particular transmission and corresponding quality of the link is built by the separate node for their neighbors.
For designing optimal WSN, optimization of energy is a crucial factor that needs to be carefully addressed. Also, in WSN, the energy consumption part in the circuit module as compared with actual energy consumption that occurs in the transmission part may not be negligible, and further, both these parts need to be recorded separately. Therefore, the general optimization techniques for energy may not be sufficient in the case of WSN, which are responsible for reducing the energy in the transmission process. It should be noted that in communication link with point-to-point connection and for applications having shorter distance, one traditional fact that higher transmission duration results in minimum energy consumption may be false if energy consumption in circuit module is significantly considered. Also, the transmission time should be maximum in order to minimize the energy requirement in the transmission process. Further, there is a need of optimizing the transmission time for overall minimizing the total energy consumption in WSN.
In WSN, one of the ways to save energy is by efficiently handling the transmitted data. The aim of data aggregation schemes is to collect the sensitive data from the sensor nodes in the network and further sent it to the base station with lower data latency. Environment monitoring requires fresh data, and therefore, data latency is playing a crucial role in various monitoring applications. It is a very tedious step or even impossible to access all original data after performing the aggregation function. The success of gaining original data depends on the type of aggregation function. There are two approaches, namely, lossy and lossless. In the lossless approach, there exists a high possibility of gaining the original data without error. For selecting a particular approach, various factors need to be evaluated such as application area, rate of data transmission, and network characteristics. Further, the data samples should be minimum for energy conservation schemes. The impact of reduced data samples also results in the decrement of the total number of communications and thereby maintaining energy efficiency. Further, spatiotemporal correlations can be utilized for reducing the data samples and such type of scheme is known as adaptive sampling. Here, a joint approach covering the features of both temporal and spatial correlations can be used to minimize the total amount of data that is needed to be acquired.
Furthermore, in WSN, the number of transmissions can be further reduced by using the characteristics of multiple data packets coding scheme. This mechanism results in a reduced number of transmissions because multiple data packet coding occurs within a single transmission.
Data Driven Approaches
The sleep/wake-up policies can be further classified as schemes based on topology control and also schemes based on duty cycling schemes. A brief discussion about topology control schemes is provided as follows.
Topology control framework is aimed at maintaining efficient connectivity or coverage in WSN. In this framework, each sensor node dynamically adjusts the power for optimal transmission. Also, each sensor node selects the appropriate neighboring sensor nodes set for direct communication. All these steps in the topology control framework contribute towards the conservation of energy at each sensor node and consequently enhance the network lifetime.
The optimal deployment of WSN requires careful handling of two crucial factors, namely, network coverage and connectivity. Further, area monitoring applications need proper coverage of networks with efficient connectivity; therefore, the success of area monitoring applications primarily depends on these two prominent factors. Also, there may exist a probability of coverage area redundancy issue and this redundancy should be minimum for optimal resource utilization by the resource constraints sensor nodes in the WSN.
The rapid development in the IC fabrication technology and current innovations in semiconductor technology results in a tremendous transformation in the field of high-performance computing such as starting from the development of single-core architecture to multicore architecture with homogeneous characteristics and then multicore architecture with heterogeneous and dynamic reconfigurable characteristics. This rapid development further imposes a challenge in terms of increment in power density as well as heat dissipation and consequently affected the system reliability and availability. Currently, research is going on achieving high performance with low power consumption. Nagalakshmi and Gomathi have presented various effective techniques for overall reducing the power dissipation in multicore processing architecture. Further, there is an urgent need to handle power management issues for designing efficient microprocessors in current scenarios of high-performance computing. The ultimate goal is to maximize the performance of the processor with low power consumption. The role of power management techniques is to maintain the balance between higher performance and power consumption with aggressive thermal effects. And that's actually schemes Based on Optimization of Power for the Hardware Devices
