With the increase of IoT devices, P2P-based IoT platforms have been attracting attention because of their capabilities of building and maintaining their networks autonomously in a decentralized way. In particular, Skip Graph, which has a low network rebuilding cost and allows range search, is suitable for the platform. However, when data observed at geographically close points have similar values (i.e. when data have strong spatial autocorrelation), existing types of Skip Graph degrade their search performances. In this paper, we propose a query transfer method that enables efficient search even for spatially autocorrelated data by adaptively using two-types of Skip Graph depending on the key-distance to the target key. Simulation results demonstrate that the proposed method can reduce the query transfer distance compared to the existing method even for spatially autocorrelated data.

To establish seamless and highly robust content distribution, we proposed the new concept of Inter-Stream Forward Error Correction (FEC), an efficient data recovery method leveraging several video streams. Our previous research showed that Inter-Stream FEC had significant recovery capability compared with the conventional FEC method under ideal modeling conditions and assumptions. In this paper, we design the Inter-Stream FEC architecture in detail with a view to practical application. The functional requirements for practical feasibility are investigated, such as simplicity and flexibility. Further, the investigation clarifies a challenging problem: the increase in processing delay created by the asynchronous arrival of packets. To solve this problem, we propose a pragmatic parity stream construction method. We implement and evaluate experimentally a prototype system with Inter-Stream FEC. The results demonstrate that the proposed system could achieve high recovery performance in our experimental environment.

For real-time video streaming, tree-based Application Level Multicasts (ALMs) are effective with respect to transmission delay and jitter. In particular, multiple-tree ALMs can alleviate the inefficient use of upload bandwidth among the nodes. However, most conventional multiple-tree ALMs are constructed using a Distributed Hash Table (DHT). This causes considerable delay and consumes substantial network resources because the DHT, generally, does not take distances in the IP network into account. In addition, the network constructed by a DHT has poor churn resilience because the network needs to reconstruct all the substreams of the tree network. In this paper, we propose a construction method involving overlapped cluster trees for delivering streamed data that are churn resilient. In addition, these overlapped cluster trees can decrease both the delay and the consumption of network resources because the node-connecting process takes IP network distances into account. In the proposed method, clusters are divided or merged using their numbers of members to optimize cluster size. We evaluated the performance of the proposed method via extensive computer simulations. The results show that the proposed method is more effective than conventional multiple-tree ALMs.