As an important branch of path planning, coverage path planning (CPP) is widely used for unmanned aerial vehicles (UAVs) to cover target regions with lower energy consumption. Most current works focus on convex regions, whereas others need pre-decomposition to deal with non-convex or mixed regions. Therefore, it is necessary to pursue a concise and efficient method for the latter. This paper proposes a two-stage method named Shrink-Segment by Dynamic Programming (SSDP), which aims to cover mixed regions with limited energy. First, instead of decomposing and then planning, SSDP formulates an optimal path by shrinking the rings for mixed regions. Second, a dynamic programming (DP)-based approach is used to segment the overall path for UAVs in order to meet energy limits. Experimental results show that the proposed method achieves less path overlap and lower energy consumption compared to state-of-the-art methods.

We study the collaborative data offloading problem for earth observation satellite networks (EOSNs) that allows light-loaded satellites to assist heavily-loaded ones to offload earth observation data. First, we propose a task flow graph (TFG) to depict the collaborative data offloading procedure. Based on the proposed TFG, we formulate the studied problem as integer linear programming (ILP) to maximize the weighted sum of successfully offloaded tasks. Then, we leverage the underlying structure of ILP to propose a fast yet efficient algorithm. Finally, our simulation results demonstrate that collaborative data offloading can significantly improve the performance of the EOSNs.

Bluetooth Low Energy Mesh (BLE Mesh) is a promising protocol to interconnect massive transceivers in a low-power, high-flexibility and wide-coverage way. It applies managed flooding to realize efficient many-to-many communication, especially in the small packet scenario. However, the traditional approaches usually generate too much redundant packets, resulting in severe collision and even broadcast storm. Related works deal with this problem mainly from the aspects of transmission mode, probabilistic broadcast and node selection. These algorithms decrease the redundancy a lot but often come at a considerable delay. In this paper, we propose a rapid flooding approach based on adaptive delay and low-power sleep(FADLS) for BLE Mesh, which adjusts the maximum random delay and relay sleep time adaptively, based on a lightweight neighbor information acquisition mechanism. The simulation results show that the FADLS algorithm reduces the broadcast collision and transmission delay effectively. In addition, the adaptability enables the algorithm more suitable for changing scales of BLE Mesh networks.

The application of the traditional planar acoustics method is limited due to the low accuracy when computing the echo characteristics of underwater targets. Based on the concept of the shooting and bouncing ray which considers multiple reflections on the basic of the geometrics optics principle, this paper presents a more efficient GPU-accelerated multiresolution grid algorithm in the shooting and bouncing ray method (SBR) to quickly predict the target strength value of complex underwater targets. The procedure of the virtual aperture plane generation, ray tracing, scattered sound field integral and subdividing the divergent ray tubes are all implemented on the GPU. Particularly, stackless KD-tree traversal is adopted to effectively improve the ray-tracing efficiency. Experiments on the rigid sphere, cylinder and corner reflector model verify the accuracy of GPU-based multiresolution SBR. Besides, the GPU-based SBR is more than 750 times faster than the CPU version because of its tremendous computing capability. Further, the proposed accelerated GPU-based multiresolution SBR improves runtime performance at least 2.4 times that of the single resolution GPU-based SBR.

The IEEE 802.11 standard provides multi-rate support for different versions. As there is no specification on the dynamic strategy to adjust the rate, different rate adaptation algorithms are applied according to different manufacturers. Therefore, it is often hard to interpret the performance discrepancy of various devices. Moreover, the ever-changing channels always challenge the rate adaptation, especially in the scenario with scarce spectrum and low SNR. As a result, it is important to sense the radio environment cognitively and reduce the unnecessary oscillation of the transmission rate. In this paper, we propose an environment-aware robust (EAR) algorithm. This algorithm employs an occasional small packet, designs a rate scheme adaptive to the environment, and enhances the robustness. We verify the throughput of EAR using network simulator NS-3 in terms of station number, motion speed and node distance. We also compare the proposed algorithm with three benchmark methods: AARF, RBAR and CHARM. Simulation results demonstrate that EAR outperforms other algorithms in several wireless environments, greatly improving the system robustness and throughput.

With the explosive growth of cloud computing applications, the east-west traffic among servers has come to occupy the dominant proportion of the traffic in data center networks (DCNs). Cloud computing tasks need to be executed in a distributed manner on multiple servers, which exchange large amounts of intermediate data between the adjacent stages of each multi-stage task. Therefore, the congestion in DCNs can reduce the processing performance when conducting multi-stage tasks. To address this, the relationship between the blocking performance and the traffic load can be adopted as a theoretical basis for network planning and traffic engineering. In this paper, the traffic load correlation between edge links and aggregation links is considered, and an iterative blocking performance analysis method is proposed for two-layer tree-type DCNs. The simulation results show the good accuracy of the proposed method with respect to the theoretical results especially in the blocking rate range below 4% and with over-subscription ratio 1.5.