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Collaborative Computing Services at Ground, Air, and Space: An Optimization Approach

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Multi-access edge computing (MEC)-enabled integrated space-air-ground networks have drawn much attention recently, as they can provide communication and computing services to wireless devices in areas that lack terrestrial base stations (TBSs). They could make it possible for battery-powered Internet of Things (IoT) devices to offload their computation tasks to MEC-enabled unmanned aerial vehicles (UAVs) assisted aerial networks and low earth orbit (LEO) satellites and thus reduce their energy consumption and allow them to complete the execution of tasks on time. However, due to the limited computation capacity of the MEC servers at UAVs and satellites, an efficient offloading decision and computation resource allocation scheme is essential. Therefore, this paper investigates the problem of minimizing the latency experienced by the wireless devices in the MEC-enabled integrated space-air-ground network by optimizing the offloading decision while assuring the energy constraints of both devices and UAVs. The problem is proved to be a non-convex problem, and the block successive upper-bound minimization (BSUM) method is proposed as a solution. Finally, extensive simulation results are presented to exhibit the effectiveness of the BSUM algorithm in solving the proposed problem.

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... j be the total amount of data transferred from UAV k to satellite s. Then, the transmission delay incurred when device j's offloaded data is transferred from UAV k to satellite s can be expressed as [36] l k→s,trans ...
... where R k→s is the achievable backhaul link capacity between the UAV and the satellite that can be calculated based on (15). Additionally, the amount of transmission energy used by UAV k when transferring the total offloaded data of its associated devices to the satellite s is given by [36] E k→s,trans = P k→s β k→s R k→s , ∀k ∈ K, ∀s ∈ S. (20) ...
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... In [18], the alternating optimization (AO) method is utilized to solve the problem of minimizing the energy consumption of LEO satellites by decomposing it into two sub-problems. Further research [19] employs the block successive upperbound minimization (BSUM) method for optimal offloading decisions in mobile edge computing (MEC) enabled SAGIN. The goal is to minimize user-experienced latency while considering the energy consumption constraints of UAV and LEO. ...
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... Due to the limited computational resources of UAVs and satellites, an effective offloading decision and computational resource allocation scheme is crucial. The authors in Reference [88] study the problem of resource offloading and computational resource allocation in SAGINs. Joint optimization of wireless device latency, UAV energy consumption, and computational task offloading is performed, and a minimization method based on block-continuous upper bounds is proposed. ...
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... The authors proposed an algorithm to decompose the problem and solve the sub-problems iteratively. In [56], the authors considered that the UAVs could communicate with each other, so that users' computation tasks could be offloaded among the UAVs for better execution. The user association and offloading decision were jointly optimized to minimize the sum of users' task latency. ...
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Geometry of Convex Inequalities. Computation of Analytic Center. Linear Programming Algorithms. Worst-Case Analysis. Average-Case Analysis. Asymptotic Analysis. Convex Optimization. Nonconvex Optimization. Implementation Issues. Bibliography. Index.