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Mobile data network is featured by long delay and moving terminals, which affect the user service quality performance of transmission control protocol’s (TCP) congestion control algorithm Vegas. To solve this problem, this paper first proposed to optimise the congestion control algorithm using a genetic algorithm, build ns-3 network topology structure and adopt mobile data network trace for optimisation and simulation; and the Vegas optimisation problem as a multivariate dual-objective problem was solved with Non-dominated Sorting Genetic Algorithm II (NSGA-II). The ns-3 simulation results indicate that Vegas with optimised parameters have high throughput and short delay, which significantly promotes TCP Vegas’s QoS under a mobile scene.

With an increasing number of Geographical Information System (GIS) services publicly available on the Web, the discovery of composite GIS services is promising when novel requirements are to be satisfied. GIS services in the repository like ArcGIS software are organized in a tree hierarchy, where a parent node represents a categorial GIS service with a coarser-granularity than its child GIS services, while leaf nodes correspond to atomic and exercisable GIS services. In this setting, discovering appropriate atomic GIS services is challenging. To remedy this issue, this paper proposes a composite GIS service discovery mechanism. Specifically, for the given requirement, select the parent nodes that take the given input parameters as input and remove their inactivated children. Use remaining children to build the network and repeat the previous operation until finding the services that contain the required output. Then record the semantic similarity degree, calculated by services functional description, in this network. By using the simulated annealing algorithm, a composite GIS services solution will be recommended from this semantic network. Evaluation results demonstrate that our approach could give more significant solution compared with the state-of-the-art techniques.

Currently, the deep integration of the Internet of Things (IoT) and edge computing has improved the computing capability of the IoT perception layer. Existing offloading techniques for edge computing suffer from the single problem of solidifying offloading policies. Based on this, combined with the characteristics of deep reinforcement learning, this paper investigates a computation offloading optimization scheme for the perception layer. The algorithm can adaptively adjust the computational task offloading policy of IoT terminals according to the network changes in the perception layer. Experiments show that the algorithm effectively improves the operational efficiency of the IoT perceptual layer and reduces the average task delay compared with other offloading algorithms.

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