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The authors consider wireless sensor networks with multiple gateways and multiple classes of traffic carrying data generated by different sensory inputs. The objective is to devise joint routing, power control and transmission scheduling policies in order to gather data in the most efficient manner while respecting the needs of different sensing tasks (fairness). They formulate the problem as maximizing the utility of transmissions subject to explicit fairness constraints and propose an efficient decomposition algorithm drawing upon large-scale decomposition ideas in mathematical programming. They show that their algorithm terminates in a finite number of iterations and produces a policy that is asymptotically optimal at low transmission power levels.
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