In the satellite and terrestrial integrated network, limited spectrum resources and flat beam coverage property cause serious inter-cell interference for satellite communication network users. Beam cooperation based dynamic channel assignment can decrease the inter-cell interference, especially for the edge users. However, link quality significantly degrades when traffic increases. To solve this problem, this paper proposes the beam cooperation based dynamic channel assignment scheme with interference threshold, in which interference threshold is applied to limit non-effective beam cooperations. In the proposed scheme, the channel is assigned only when it meets the link quality requirement. Simulation results show that this scheme can improve the link quality significantly in the case of heavy traffic.
An increasing number of cloud providers now offer mobile edge computing (MEC) services for their customers to support task offloading. This is undertaken to reduce latency associated with forwarding data from IoT devices owned by customers to cloud platforms. However, two challenges remain in existing MEC scenarios: (i) the coverage of MEC services is limited; (ii) there is limited ability to develop an audit trail about which MEC service providers have processed a user's data. A new architecture for automatically offloading user tasks in MEC scenarios is proposed that addresses the two challenges above. The architecture makes use of drones to dynamically cache data generated from IoT devices and forward this data to MEC servers that participate in a private blockchain network. Our simulated experiments demonstrate the flexibility of the task offloading process through the proposed architecture, which can provide greater visibility of MEC service providers involved in processing users' data.
An automatic target detection method used in long term infrared (IR) image sequence from a moving platform is proposed. Firstly, based on non-linear histogram equalization, target candidates are coarse-to-fine segmented by using two self-adapt thresholds generated in the intensity space. Then the real target is captured via two different selection approaches. At the beginning of image sequence, the genuine target with litter texture is discriminated from other candidates by using contrast-based confidence measure. On the other hand, when the target becomes larger, we apply online EM method to iteratively estimate and update the distributions of target's size and position based on the prior detection results, and then recognize the genuine one which satisfies both the constraints of size and position. Experimental results demonstrate that the presented method is accurate, robust and efficient.
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