We propose a stable backbone tree construction algorithm using multi-hop clusters for wireless sensor networks (WSNs). The hierarchical cluster structure has advantages in data fusion and aggregation. Energy consumption can be decreased by managing nodes with cluster heads. Backbone nodes, which are responsible for performing and managing multi-hop communication, can reduce the communication overhead such as control traffic and minimize the number of active nodes. Previous backbone construction algorithms, such as Hierarchical Cluster-based Data Dissemination (HCDD) and Multicluster, Mobile, Multimedia radio network (MMM), consume energy quickly. They are designed without regard to appropriate factors such as residual energy and degree (the number of connections or edges to other nodes) of a node for WSNs. Thus, the network is quickly disconnected or has to reconstruct a backbone. We propose a distributed algorithm to create a stable backbone by selecting the nodes with higher energy or degree as the cluster heads. This increases the overall network lifetime. Moreover, the proposed method balances energy consumption by distributing the traffic load among nodes around the cluster head. In the simulation, the proposed scheme outperforms previous clustering schemes in terms of the average and the standard deviation of residual energy or degree of backbone nodes, the average residual energy of backbone nodes after disseminating the sensed data, and the network lifetime.

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AR (Augmented Reality) on mobile device is beyond providing simple additional information. Moreover, it has extended its range to variety domains. The latest AR service for device recognition uses POI (Point of Interest), GPS (Global Positioning System) or digital compass. These technologies could not be applied well in home and office environments, because it is difficult to recognize small and uniform shaped devices like industrial products. Nowadays, other methods of device recognition have been widely studied. One is tracking device features. This makes feature model and then compares feature model with trained model. The other is that using bar code or RFID. Such methods are hard to get into user-space because dynamic device status, device sensing or recognition could not be made into trained model. Therefore, in inner space like home or office, AR has not been used widely compared to outer space. In this paper, we propose new concept of technology about device recognition using network profile information though a mobile camera interface. In addition, we provide preview method about device's service, contents, and real-time status information so that users can use faster, more intuitive user-interface in mobile devices. We implement the system of media server and renderer to share digital contents. This provides intuitive interaction for contents sharing just using camera preview.