Optimizing the service policy of a wireless access point on the move with renewable energy

Ceran, Elif Tuğçe; Erkilic, Tugce; Uysal-Biyikoğlu, Elif; Girici, Tolga; Leblebi̇ci̇oğlu, Kemal

doi:10.1109/allerton.2014.7028559

Cited by 1 publication

(2 citation statements)

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“…Various other works, such as [16], and [17], also consider threshold based solutions for resource allocation problems in energy harvesting systems for stochastic models. In recent work [18], [19], [20] resource allocation for solar powered stationary and mobile service providers have been addressed through various different optimization techniques.…”

Section: A Related Workmentioning

confidence: 99%

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Optimal energy allocation policies for a high altitude flying wireless access point

Ceran

Erkilic

Uysal-Biyikoğlu

et al. 2016

Trans. Emerging Tel. Tech.

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Inspired by recent industrial efforts towards high altitude flying wireless access points powered by renewable energy, an online resource allocation problem for a mobile access point travelling at high altitude is formulated. The access point allocates its resources (available energy) to maximise the total utility (reward) provided to a sequentially observed set of users demanding service. The problem is formulated as a 0/1 dynamic knapsack problem with incremental capacity over a finite time horizon, and the solution of which is quite open in the literature. We address the problem through deterministic and stochastic formulations followed by a model where the statistics of the underlying processes are not known and learned through rule-based and neural network approaches. For the deterministic problem, several online approximations including optimisation via genetic algorithm and rule-based approach are proposed based on an instantaneous threshold that can adapt to short-time-scale dynamics. For the stochastic model, after showing the optimality of a threshold-based solution on a dynamic programming formulation, an approximate threshold-based policy is obtained. The performances of proposed policies are compared with that of the optimal solution obtained through dynamic programming

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Section: A Related Workmentioning

confidence: 99%

“…After stating a bound on the expected energy consumption from slot n till the end of time horizon in (18), a computationally cost effective suboptimal policy called "Expected Threshold" is proposed in (21) as follows:…”

Section: Suboptimal Solution: Expected Threshold Policymentioning

confidence: 99%