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Variations in life-history patterns are thought to represent a given species' maximization of lifetime inclusive fitness within a particular environment [1], [2].
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The maximization of network lifetime.
For wireless sensor networks, one of the most important design goals is the maximization of network lifetime.
In this research, we focus on the maximization of network lifetime which has become a critical issue in sensor networks.
As shown in the equation, ε i is the energy consumption of the ith node and ( {E}_O^i ) represents the initial energy of node i. (2) The maximization of network lifetime .
At this point, the maximization of the network lifetime is transformed into the problem of target area coverage.
In this paper, the problem is solved considering three objectives, simultaneously (i.e. maximization of the Project Lifetime Economic Return, minimization of CO2 emission, and minimization of the fuel price risk due to the use of non-renewable energy sources).
In fact, as future works, we intend to study other optimization metrics such as the maximization of the network lifetime or the minimization of the average (or the maximum) distance travelled by nodes belonging to a path.
In essence, the maximization of the given network lifetime is transformed into seeking the largest set of sensor nodes in the entire target area.
In [17], maximization of large-scale WSN lifetime is pursued by means of a fully distributed algorithm according to which each sensor autonomously performs classical or compressive sampling in order to reduce the number of transmitted packets.
In the specific context of lifetime health, the findings underscore the insufficiency of pure QALY maximization and explicate how people make trade-offs in a way that can help operationalize lifetime prioritarianism and non-standard CEA.
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