To address the issues of uneven satellite network load and unstable link connections, a globally adaptive satellite network routing strategy based on traffic prediction (G-AODV) is proposed by enhancing the existing Ad hoc On-Demand Distance Vector Routing (AODV) protocol. To prevent heavily loaded nodes from becoming intermediate nodes, G-AODV introduces a traffic prediction mechanism in the route discovery phase. The routing request packet adopts the corresponding forwarding control policy based on the comparison between the predicted traffic load value at the next moment and the dynamic threshold. At the same time, a path replacement strategy is adopted to replace paths before node congestion occurs to achieve load balancing. Considering the unstable characteristics of the satellite chain that easily breaks, the route maintenance phase selects a path repair method by judging the node stability to avoid secondary breaks in the route. The simulation results show that in scenarios with different packet delivery rates, compared with the three comparative routing strategies, the packet delivery rate of G-AODV is increased by up to 20%, the packet loss rate is reduced by 22%, and the end-to-end delay is significantly reduced. In scenarios with different communication connection pairs, G-AODV’s packet delivery rate increased by up to 20%, the packet loss rate decreased by 18%, and the end-to-end delay was still reduced.

The problem of multiple DAGs sharing satellite constellation resources has gradually attracted widespread attention. Due to the limited computing resources and energy consumption of satellite networks, it is necessary to formulate a reasonable multi-DAG task scheduling scheme to ensure the fairness of each workflow under the premise of considering latency and energy consumption. Therefore, in this paper, we propose a multi-DAG satellite network task scheduling algorithm based on cache-composite priority under the Software-Defined Networking satellite network architecture. The basic idea of this algorithm lies in the DAG selection phase, where not only are task priorities computed but also the concept of fair scheduling is introduced, so as to prevent the excessively delayed scheduling of low-priority DAG tasks. In addition, the concept of public subtasks is introduced to reduce the system overhead caused by repetitive tasks. The experimental results show that the hybrid scheduling strategy proposed in this paper can meet the demand of DAG scheduling and improve the degree of task completion while effectively reducing the task latency and energy consumption.

Mercury ion is a highly toxic anthropogenic pollutants and has serious well-known effects on human. There is an ever-growing demand for convenient detection of mercury driven contaminants in environment, including coastal seawater. However, most of the reported methods are instrument-based and are not easy for portable detection. Our protocol described an efficient Digital Microfluidics method for detecting mercury in coastal seawater samples. It combined the miniaturization/automation potential of digital microfluidics and the sensitivity of fluorescence probe. To overcome a potential risk of driven failure, induced by diversity ion ingredients in seawater, a feedback control loop was included into control system. The method showed satisfied stability and selectivity in Hg sensing under high salinity condition, with the sensitivity of Hg2+ at the parts-per-billion level and total testing time of less than 20 s. With the advantages of being fast, amenable to automation and low cost, this protocol is promising for the formation of simple and rapid sensor device, especially for a routine monitoring and emergency detection of Hg/or other metals in coastal waters.