With the rapid development of the internet of things (IoT), traditional industries are setting off a massive wave of digitization. In the era of the Internet of Everything, millions of devices and links in IoT pose more significant challenges to data management. Most existing solutions employ centralized systems to control IoT devices, which brings about the privacy and security issues in IoT data management. Recently, blockchain has attracted much attention in the field of IoT due to its decentralization, traceability, and non-tamperability. However, it is non-trivial to apply the current blockchain techniques to IoT due to the lack of scalability and high resource costs. Different blockchain platforms have their particular advantages in the scenario of IoT data management. In this paper, we propose a cross-chain framework to integrate multiple blockchains for efficient and secure IoT data management. Our solution builds an interactive decentralized access model which employs a consortium blockchain as the control station. Other blockchain platforms customized for specific IoT scenarios run as the backbone of all IoT devices. It is equivalent to opening the off-chain channels on the consortium blockchain. Our model merges transactions in these channels for confirmation based on the notary mechanism. Finally, we implement a prototype of the proposed model based on hyperledge Fabric and IOTA Tangle. We evaluate the performance of our method through extensive experiments. The results demonstrate the effectiveness and efficiency of our framework.

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As an important part of intrusion detection, feature selection plays a significant role in improving the performance of intrusion detection. Krill herd (KH) algorithm is an efficient swarm intelligence algorithm with excellent performance in data mining. To solve the problem of low efficiency and high false positive rate in intrusion detection caused by increasing high-dimensional data, an improved krill swarm algorithm based on linear nearest neighbor lasso step (LNNLS-KH) is proposed for feature selection of network intrusion detection. The number of selected features and classification accuracy are introduced into fitness evaluation function of LNNLS-KH algorithm, and the physical diffusion motion of the krill individuals is transformed by a nonlinear method. Meanwhile, the linear nearest neighbor lasso step optimization is performed on the updated krill herd position in order to derive the global optimal solution. Experiments show that the LNNLS-KH algorithm retains 7 features in NSL-KDD dataset and 10.2 features in CICIDS2017 dataset on average, which effectively eliminates redundant features while ensuring high detection accuracy. Compared with the CMPSO, ACO, KH, and IKH algorithms, it reduces features by 44%, 42.86%, 34.88%, and 24.32% in NSL-KDD dataset, and 57.85%, 52.34%, 27.14%, and 25% in CICIDS2017 dataset, respectively. The classification accuracy increased by 10.03% and 5.39%, and the detection rate increased by 8.63% and 5.45%. Time of intrusion detection decreased by 12.41% and 4.03% on average. Furthermore, LNNLS-KH algorithm quickly jumps out of the local optimal solution and shows good performance in the optimal fitness iteration curve, convergence speed, and false positive rate of detection.

A transmission hyperspectral microscopic imager (THMI) that utilizes machine learning algorithms for hyperspectral detection of microalgae is presented. The THMI system has excellent performance with spatial and spectral resolutions of 4 µm and 3 nm, respectively. We performed hyperspectral imaging (HSI) of three species of microalgae to verify their absorption characteristics. Transmission spectra were analyzed using principal component analysis (PCA) and peak ratio algorithms for dimensionality reduction and feature extraction, and a support vector machine (SVM) model was used for classification. The average accuracy, sensitivity and specificity to distinguish one species from the other two species were found to be 94.4%, 94.4% and 97.2%, respectively. A species identification experiment for a group of mixed microalgae in solution demonstrates the usability of the classification method. Using a random forest (RF) model, the growth stage in a phaeocystis growth cycle cultivated under laboratory conditions was predicted with an accuracy of 98.1%, indicating the feasibility to evaluate the growth state of microalgae through their transmission spectra. Experimental results show that the THMI system has the capability for classification, identification and growth stage estimation of microalgae, with strong potential for in-situ marine environmental monitoring and early warning detection applications.

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