Background: The advancement of Industry 4.0 technologies has affected every aspect of supply chains. Recently, enterprises have tried to create more value for their businesses by tapping into these new technologies. Warehouses have been one of the most critical sections in a supply chain affected by Industry 4.0 technologies. Methods: By recognizing the role of inventory management in a supply chain and its importance, this paper aims to highlight the impact of IoT technologies on inventory management in supply chains and conducts a comprehensive study to identify the research gap of applying IoT to inventory management. The trend and potential opportunities of applying IoT to inventory management in the Industry 4.0 era are explored by analyzing the literature. Results: Our findings show that the research on this topic is growing in various industries. A broad range of journals is paying particular attention to this topic and publishing more articles in this research direction. Conclusions: Upgrading a supply chain into an integrated supply chain 4.0 is beneficial. Given the changes in fourth-generation technology compared to previous generations, the approach of conventional inventory replenishment policies seems not responsive enough to new technologies and is not able to cope with IoT systems well.

Abnormal event detection is one of the vital tasks in wireless sensor networks. However, the faults of nodes and the poor deployment environment have brought great challenges to abnormal event detection. In a typical event detection technique, spatiotemporal correlations are collected to detect an event, which is susceptible to noises and errors. To improve the quality of detection results, we propose a novel approach for abnormal event detection in wireless sensor networks. This approach considers not only spatiotemporal correlations but also the correlations among observed attributes. A dependency model of observed attributes is constructed based on Bayesian network. In this model, the dependency structure of observed attributes is obtained by structure learning, and the conditional probability table of each node is calculated by parameter learning. We propose a new concept named attribute correlation confidence to evaluate the fitting degree between the sensor reading and the abnormal event pattern. On the basis of time correlation detection and space correlation detection, the abnormal events are identified. Experimental results show that the proposed algorithm can reduce the impact of interference factors and the rate of the false alarm effectively; it can also improve the accuracy of event detection.

Privacy preserving mining of distributed data is an important direction for data mining, and privacy preserving clustering is one of the main researches. Privacy-preserving data mining techniques enable knowledge discovery without requiring disclosure of private data. The existing privacy preserving algorithms mainly concentrated on association rules and classification, only few algorithms on privacy preserving clustering, and these algorithms mainly concentrated on centralized and vertically partitioned data. So we proposed privacy preserving hierarchical k-means clustering algorithm on horizontally partitioned data, denoted as HPPHKC. The complexity on k-means clustering algorithm is only O(n), so most existing privacy preserving clustering algorithms are concentrated on k-means and based on two parties and the trusted third party, these algorithms have the drawbacks of inaccurate results because of choosing initial clustering centers randomly and applying to multi-party difficult and revealing privacy because of depending on the third party excessively. By introduction of three protocols for secure multi-party computation: distance computation, cluster center computation, and standardization and combination of the merits of hierarchical and k-means clustering, we presented a privacy-preserving hierarchical-k-means clustering algorithm on horizontally partitioned data for semi-honest parties using some secure multi-party computation protocols. The algorithm uses the security protocol mentioned above to achieve the protection of the privacy data, and uses the hierarchical clustering algorithm to obtain k cluster centers, then uses the k-means clustering algorithm to obtain the final k clusters. We introduce the clustering feature and the clustering feature tree, which are used to summarize the cluster representations. A clustering feature (CF) is a three-dimensional vector summarizing information about clusters of objects. The i^th clustering feature is CF_i = (cn_i,cc_i,cp_i), where cn_i is the number of ^th clusters, denoted as the size of ^th cluster, cc_i is the center of the cni objects, and cp_i is the pointer of the list of cni objects. The algorithm has two phases: the first phase, every object can be as a cluster, a secure computation protocol is used to compute the dissimilarity matrix and the most similar clusters will be merged. This process is repeated until we get the assigned clusters number k and get k clustering centers. In the second phase, the semi-honest third party and all data involved parties use the k-means algorithm refine the results of the first phase and get the final clustering results. Finally, we give the proof of security of the algorithm and analysis of communication costs, and we show that our scheme is secure and complete with good efficiency.

A spatial outlier is a spatial object whose non-spatial attribute values are significantly different from the values of its neighborhood. Identification of spatial outliers can lead to the discovery of unexpected, interesting, and useful spatial patterns for further analysis. Major drawbacks of existing methods are that the characteristics of spatial objects aren't considered, normal objects tend to be falsely detected as spatial outliers or true spatial outliers tend to be ignored when their neighborhood contains true spatial outliers, they have poor efficiency, and they depend on a priori given parameters. We proposed a new measure to identify spatial outliers and a novel algorithm of spatial outlier detection based on the outlying degree. The algorithm overcomes those disadvantages and can accurately detect spatial outliers. In addition, using a real-world census data set, we demonstrate that our approach is effective and efficient