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In this paper, we address the problem of minimizing the negative influence of undesirable things in a network by blocking a limited number of nodes from a topic modeling perspective. When undesirable thing such as a rumor or an infection emerges in a social network and part of users have already been infected, our goal is to minimize the size of ultimately infected users by blocking k nodes outside the infected set. We first employ the HDP-LDA and KL divergence to analysis the influence and relevance from a topic modeling perspective. Then two topic-aware heuristics based on betweenness and out-degree for finding approximate solutions to this problem are proposed. Using two real networks, we demonstrate experimentally the high performance of the proposed models and learning schemes.

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With the rapid development of the Internet of Things (IoT), a large number of heterogeneous sensing devices are accessed in their proprietary ways to IoT applications, formed ''silos'' application mode. The growth of IoT applications has been hampered by this mode due to the barriers of resource sharing of heterogeneous sensing devices. This paper proposes an IoT access platform deployed at the network edge near the sensing devices. This architecture can enable more responsive IoT applications and provide efficient privacy protection for sensitive data. We propose a general ontology-based resource description model of IoT devices to provide a consistent view of heterogeneous sensing devices for IoT applications in the cloud. Based on this model, we propose an adaptive access method to provide unified access, control, and management of IoT devices with various intelligence levels. The access platform turns the ''silos'' application mode of IoT into a horizontal application mode, supports applications to share and reuse the resources of IoT devices. We demonstrate the efficacy of our architecture with an application case study that highlights our proposed resource description model and adaptive access method and evaluate performance improvement with experiments. INDEX TERMS Sensing devices access platform, Internet of Things, edge computing, resource description model, adaptive access method.

Abstract-IoT (Internet of Things) bridges the physical world and information space. IoT services are environmentally sensitive and event-driven, so new IoT service architecture should adapt to these features. This paper analyses IoT sensing service characteristics and proposes future services architecture. It is focused on middleware architecture and interface presentation technology. In the middleware layer, traditional SOA architecture is insufficient in real-time response and parallel processes of service execution. This paper proposes a new sensing service system based on EDSOA (Event Driven SOA) architecture to support realtime, event-driven, and active service execution. At the presentation layer, this paper presents new IoT browser features, including using augmented reality technology for input and output and realizing the superposition of the physical world and abstract information. Through a use case and proof-of-concept implementation-road manhole cover monitoring system-we verified the feasibility of the proposed ideas and framework.