Hardware are properly evaluated and tested by reputed companies before deployment to achieve energy efficiency effectively.
The problem of sensor deployment to achieve k-coverage of a field, where every point is covered by at least k sensors, is very critical in the design of energy-efficient wireless sensor networks (WSNs).
Traditional vibration measurement sensors such as accelerometers have high-frequency sampling capacity; however, they are discrete point-wise sensors only providing sparse, low spatial sensing resolution measurements, while dense deployment to achieve high spatial resolution is expensive and results in the mass-loading effect and modification of structure's surface.
The architecture discussed in this chapter primarily focuses on premise/in-house deployment option to achieve high availability.
Considering the best scenarios found, we show deployment parameters to achieve maximum system throughput, such as density of femto-users and primary exclusion zones of macro-users.
The objective of cost deployment is to achieve the target cost while keeping a balance with quality (Jiang et al. 2007).
The good performance and operability of WMNs largely depends on placement of mesh routers nodes in the geographical deployment area to achieve network connectivity, stability and user coverage.
Fiscal incentives and public financing can support of any aspects of the research, development, demonstration and deployment lifecycle to achieve technology push and market pull for renewable energy technologies.
Considering the best scenarios, the deployment parameters to achieve maximum system throughput, such as the number of admitted secondary users coexisting with primary users and primary exclusion zones, are presented.
The results demonstrate that our strategy requires less roadside units than the baseline for non-massive deployments in order to achieve similar levels of performance, incurring in less costs when setting up the content-delivery infrastructure.
