Temporal Index Scheme of Hyperledger Fabric System in IoT

As a large number of mobile terminals are connected to the IoT, the security problem of IoT is a challenge to the IoT technology. Blockchain technology has the characteristics of decentralization, data encryption, smart contract, and so on, especially suitable in the complex heterogeneous network. However, sequential access based on block files in the blockchain hinders efficient query processing. The problem is due to current blockchain solutions do not support temporal data processing. In this paper, we propose two index building methods (TISD and TIF) to address this issue in Hyperledger Fabric System. TISD (temporal index based on state databases) segments the historical data by time interval in the time dimension and indexes events at the same time interval. TIF (temporal index based on files) builds the index of files by the block transaction data, which is arranged in chronological order and is stored at a certain time interval. In the experimental part, we compare the query time on two datasets and analyse the query performance. Experiments demonstrated that our two methods are relatively stable in overall time performance on different datasets in the Hyperledger Fabric System.

Temporal experience modifies future thoughts: Manipulation of Libet’s W influences difficulty assessment during a decision-making task

Past studies have employed the subjective experience of decision time (Libet’s W) as an index of consciousness, marking the moment at which the agent first becomes aware of a decision. In the current study, we examined whether the temporal experience of W affects subsequent experience related to the action. Specifically, we tested whether W influenced the perception of difficulty in a decision-making task, hypothesizing that temporal awareness of W might influence the sense of difficulty. Consistent with our predictions, when W was perceived as early or late, participants subsequently rated the decision difficulty to be easy or difficult, respectively (Exp.1). Further investigation showed that perceived difficulty, however, did not influence W (Exp.2). Together, our findings suggest a unidirectional relationship such that W plays a role in the metacognition of difficulty evaluation. The results imply that subjective temporal experience of decision time modifies the consequential sense of difficulty.HighlightsPerceived timing of decision (W) can bias the metacognition of difficulty evaluation in a decision-making task.Defined as a temporal index of consciousness, time W’s influence on difficulty evaluation reflects the possibility that the role of consciousness is to modify subsequent thoughts and behaviors.Explicit attention is necessary for the timing of decision (W) to be consciously experienced and effectively influential on subsequent thoughts.

An Adaptive Construction Method of Hierarchical Spatio-Temporal Index for Vector Data under Peer-to-Peer Networks

Spatio-temporal indexing is a key technique in spatio-temporal data storage and management. Indexing methods based on spatial filling curves are popular in research on the spatio-temporal indexing of vector data in the Not Relational (NoSQL) database. However, the existing methods mostly focus on spatial indexing, which makes it difficult to balance the efficiencies of time and space queries. In addition, for non-point elements (line and polygon elements), it remains difficult to determine the optimal index level. To address these issues, this paper proposes an adaptive construction method of hierarchical spatio-temporal index for vector data. Firstly, a joint spatio-temporal information coding based on the combination of the partition and sort key strategies is presented. Secondly, the multilevel expression structure of spatio-temporal elements consisting of point and non-point elements in the joint coding is given. Finally, an adaptive multi-level index tree is proposed to realize the spatio-temporal index (Multi-level Sphere 3, MLS3) based on the spatio-temporal characteristics of geographical entities. Comparison with the XZ3 index algorithm proposed by GeoMesa proved that the MLS3 indexing method not only reasonably expresses the spatio-temporal features of non-point elements and determines their optimal index level, but also avoids storage hotspots while achieving spatio-temporal retrieval with high efficiency.

Reverse Nearest Neighbors query of moving objects based on HRNN-tree

Reverse nearest neighbors query as an indispensable part of spatial retrieve plays an important role in spatial analysis and spatial processing. In this paper, we target the problem of reverse nearest neighbors query for moving objects. We devised a spatio-temporal index suitable for this problem firstly and then an algorithm for reverse nearest neighbors search is proposed based on this index. The results of experimental evaluation demonstrate that this algorithm outperforms others for reverse nearest neighbors queries of moving objects. This work can better improve and enhance the power of quantitative analysis and process for spatio-temporal database.