Primal decomposition and online algorithms for flow optimization in wireless DTNs

Konidaris, George; Toumpis, Stavros; Gitzenis, Savvas

doi:10.1109/glocom.2013.6831052

Cited by 7 publications

(1 citation statement)

References 11 publications

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“…It can be seen that problem (P1) is a mixed integer linear programming (MILP) problem, which is generally NP-hard in nature [31]. Moreover, constraints C1 and C7 introduce complex coupling relationships among multiple time slots [32]. This exacerbates the computational complexity to solve the problem.…”

Section: A Optimization Problemmentioning

confidence: 99%

Multi-Resource Coordinate Scheduling for Earth Observation in Space Information Networks

Wang

Sheng

Zhuang

et al. 2018

IEEE J. Select. Areas Commun.

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Space information network (SIN) is an innovative networking architecture to achieve near-realtime mass data observation, processing and transmission over the globe. In the SIN environment, it is essential to coordinate multi-dimensional heterogeneous resources (i.e., observation resource, computation resource and transmission resource) to improve network performance. However, the time varying property of both the observation resource and transmission resource is not fully exploited in existing studies. Dynamic resource management according to instantaneous channel conditions has a potential to enhance network performance. To this end, in this paper, we study the multi-resource dynamic management problem, considering stochastic observation and transmission channel conditions in SINs.Specifically, we develop an aggregate optimization framework for observation scheduling, compression ratio selection and transmission scheduling, and formulate a flow optimization problem based on extended time expanded graph (ETEG) to maximize the sum network utility. Then, we equivalently transform the flow optimization problem on ETEG as a queue stability-related stochastic optimization problem. An online algorithm is proposed to solve the problem in a slot-by-slot manner by exploiting the Lyapunov optimization technique. Performance analysis shows that the proposed algorithm achieves close-to-optimal network utility while guaranteeing bounded queue occupancy. Extensive simulation results further validate the efficiency of the proposed algorithm and evaluate the impacts of various network parameters on the algorithm performance.

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Section: A Optimization Problemmentioning

confidence: 99%