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Unmanned Surface Vehicles (USVs) generate a large amount of data that needs to be processed in real time when they work, but they are usually limited by computational and battery resources, so they need to offload their tasks to the edge for processing. However, when numerous USVs offload their tasks to the edge nodes, some offloaded tasks may be thrown due to queuing timeouts. Existing task offloading methods generally consider the latency or the overall system energy consumption caused by the collaborative processing at the edge and end layers, and do not consider the wasted energy when the tasks are thrown. Therefore, to address the above situation, this paper establishes a task offloading model to minimize long-term task latency and energy consumption by jointly considering the requirments of latency and energy-sensitive tasks and the overall load dynamics in the cloud, edge, and end layers. A deep reinforcement learning (DRL)-based Task Offloading with Cloud Edge Jointly Load Balance Optimization algorithm (TOLBO) is proposed to select the best edge server or cloud server for offloading. Simulation results show that the algorithm can improve the utilization of energy consumption of the cloud edge nodes compared with other algorithms. At the same time, it can significantly reduce the task throw rate, average latency, and energy consumption of end devices.

Data centers have become a prevalent infrastructure to host a large number of services, such as social networking and Web search, which generally have rigorous delay requirements. Even a slight increase in delay can seriously affect the performance of applications. Therefore, there have been several efforts focusing on minimizing the flow completion time (FCT) to meet the delay requirements, such as ICTCP, which adjusts the receive window according to the available bandwidth and DCTCP that uses ECN to provide the feedback to end hosts in mixing workloads. However, both approaches are only for a specific scene and cannot effectively solve the problem that packets losses lead to timeouts due to TCP incast. Moreover, they all use the fair-share mechanism, which causes the increasing of the FCT for small flows, when background flows pour into the network. For example, background flows cause the increasing(≈ 5ms) of the FCT for the ICTCP in an incast experiment. To solve these problems, we propose a priority-driven congestion control algorithm, PTCP, which can effectively avoid the incast problem and improve the FCT in mixing workloads. The PTCP leverages the priority to adjust the receive window and control the interval time of ACK. To evaluate the PTCP, we conduct extensive experiments in NS-2. The simulation results show that the PTCP outperforms greatly previous schemes both in the incast scenario and mixing workloads.

This paper aims to develop a method that can accurately estimate the crowdedness level for crowdsensing buses. Multiple related features are reflected by passengers' moving trajectories at the bus stops. Many state-of-the-art posture recognition approaches have high accuracy, which can ensure that the passengers motion monitoring results are reliable. Through these observations, we propose an improved behavior-learning-based crowdedness estimation mechanism, named BCE, to obtain the crowdedness level of a bus. The motion sequence and gait information of a passenger is obtained via sensors in smartphones and is described by feature vectors. Then the feature vectors are classified as bus crowdedness levels based on gcForest classifier for single-person bus's crowdedness level estimation and on Recurrent Neural Network (RNN) for multipeople bus's crowdedness level estimation. Additionally, the moving trajectories and the corresponding crowdedness of the passengers who do not involve in our system can be recognized passively through the motion information of adjacent involved passengers on the bus. The experiments prove that our mechanism achieves an accuracy of 92% overall.