inversion of gravity data has been widely used to reconstruct the density distributions of ore bodies, basins, crust, lithosphere, and upper mantle. For global model of 3-D density structures of planetary interior, such as the Earth, the Moon, or Mars, it is necessary to use an inversion algorithm that operates in the spherical coordinates. We develop a 3-D inversion algorithm formulated with specially designed model objective function and radial weighting function in the spherical coordinates. We present regional and global synthetic examples to illustrate the capability of the algorithm. The inverted results show density distribution features consistent with the true models. We also apply the algorithm to a set of lunar Bouguer gravity anomaly derived from the Gravity Recovery and Interior Laboratory (GRAIL) gravity field and obtain a lunar 3-D density distribution. High-density anomalies are clearly identified underlying lunar basins, a wide region of the lateral density heterogeneities that exist beneath the South Pole-Aitken basin are found, and low-density anomalies are distributed beneath the Feldspathic Highlands Terrane on the lunar far-side. The consistency of these results with those obtained independently from other existing methods verifies the newly developed algorithm.

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SUMMARYVehicle Ad-Hoc Networks (VANET) enable all components in intelligent transportation systems to be connected so as to improve transport safety, relieve traffic congestion, reduce air pollution, and enhance driving comfort. The vision of all vehicles connected poses a significant challenge to the collection, storage, and analysis of big traffic-related data. Vehicular cloud computing, which incorporates cloud computing into vehicular networks, emerges as a promising solution. Different from conventional cloud computing platform, the vehicle mobility poses new challenges to the allocation and management of cloud resources in roadside cloudlet. In this paper, we study a virtual machine (VM) migration problem in roadside cloudletbased vehicular network and unfold that (1) whether a VM shall be migrated or not along with the vehicle moving and (2) where a VM shall be migrated, in order to minimize the overall network cost for both VM migration and normal data traffic. We first treat the problem as a static off-line VM placement problem and formulate it into a mixed-integer quadratic programming problem. A heuristic algorithm with polynomial time is then proposed to tackle the complexity of solving mixed-integer quadratic programming. Extensive simulation results show that it produces near-optimal performance and outperforms other related algorithms significantly.

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