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The approximate computing paradigm provides methods to optimize algorithms while considering both application quality of service and computational complexity.
In this paper, we address the problem of maximizing lifetime for a wireless sensor network while meeting a minimum level of application quality of service.
The paper also extends a previous service composition algorithm that provided a feasible solution compliant with the application quality of service criteria (QoS).
Then, considering power consumption, application quality of service (QoS) profiles, and corresponding cost functions, a multi-objective optimization approach using an event-based finite state model and dynamic constraint programming method has been used to determine the appropriate transmission power, process power, cloud offloading and optimum QoS profiles.
Key benefits of this approach include ease of implementation, compactness of instrumentation, simplicity of control, lack of moving parts, versatility of application, quality of data, compatibility with existing optical mapping setups, and low cost.
Compare the application quality of different brands.
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In this paper, we address the channel allocation problem in CR-VANET with the objective of system-wide throughput maximization while maintaining the application quality-of-service (QoS) requirements in terms of channel throughput and packet delivery delay for data transmission.
Furthermore, since we concentrate on MEMS loudspeaker applications, quality of sound plays a major role, where an undistorted and linear system defines the challenge.
In many applications, quality of process is characterized by a single random variable called quality characteristic but some cases occur that process is characterized by more than one quality characteristic.
Kim also reported the application of quality-of-life scale SF-36 in the intracranial aneurysm group [ 12].
In a dynamic network, it is difficult to use multimedia and other advanced applications without quality-of-service (QoS) constraint.
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