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The Internet of Things (IoT) enabled Cyber–Physical System (CPS) is a promising technology applying in smart home, industrial manufacturing, intelligent transportation, etc. The IoT enabled CPS consists of two main components, i.e., IoT devices and cybers, which interact with each other. The IoT devices collect sensory data from physical environments and transmit them to the cybers, and the cybers make decisions to respond to the collected data and issue commands to control the IoT devices. It is generally known that energy is an important but limited resource in IoT devices. Data compression is an efficient way to reduce the energy consumption of data collection in sustainable IoT enabled CPSs, especially the Principal Component Analysis (PCA) based data compression. The trade-off between data compression ratio and data reconstruction error is one of the biggest challenges for PCA based data compression. In this paper, we investigate PCA based data compression to maximize the compression ratio with bounded reconstruction error for data collection in IoT enabled CPSs. Firstly, a similarity based clustering algorithm is proposed to cluster IoT devices in an IoT enabled CPS. Then, a PCA based data compression algorithm is proposed to compress the collected data to the greatest extent in each cluster with a bounded reconstruction error. Extensive simulations are conducted to verify the efficiency and effectiveness of the proposed algorithms. [ABSTRACT FROM AUTHOR]

• Structural similarity metrics and learning techniques are introduced. • A method for classifying algorithms by network structure is proposed. • General applications and research directions of network embedding are promoted. Social networks have a plethora of applications, and analysis of these applications has been gaining much interest from the research community. The high dimensionality of social network data poses a significant obstacle in its analysis, leading to the curse of dimensionality. The mushrooming of representation learning in various research fields facilitates network representation learning (also called network embedding), which will help us address the above-mentioned issue. Structural Representation Learning aims to learn low-dimensional vector representations of high-dimensional network data, allowing maximal preservation of network structural information. This representation can then serve as a backbone for various network-based applications. First, we investigate the techniques used in network representation learning and similarity indices. We then categorize the representative algorithms into three types based on the network structural level used in their learning process. We also introduce algorithms for representation learning of edges, subgraphs, and the whole network. Finally, we introduce the evaluation metrics and the applications of network representation learning and promising future research directions. [ABSTRACT FROM AUTHOR]

Abstract: In verifiable multi-secret sharing schemes (VMSSs), many secrets can be shared but only one share is kept by each user and this share is verifiable by others. In this paper, we propose two secure, efficient, and verifiable multi-secret sharing schemes, namely Scheme-I and Scheme-II. Scheme-I is based on the Lagrange interpolating polynomial and the LFSR-based public key cryptosystem. The Lagrange interpolating polynomial is used to split and reconstruct the secrets and the LFSR-based public key cryptosystem is employed to verify the validity of the data. Scheme-II is designed according to the LFSR sequence and the LFSR-based public key cryptosystem. We compare our schemes with the state-of-the-art in terms of attack resistance, computation complexity, and so on, and conclude that our schemes have better performance and incur less computation overhead. Our schemes can effectively detect a variety of forgery or cheating actions to ensure that the recovery of the secrets is secure and creditable, and the length of the private key is only one third of that of others for the same security level. [Copyright &y& Elsevier]

In this paper, we propose a robust communication-efficient decentralized learning algorithm, named RCEDL , to address data heterogeneity , communication heterogeneity and communication efficiency simultaneously in real-world scenarios. To the best of our knowledge, this is the first work to address the above challenges in a united framework. In detail, we design a compressed cross-gradient aggregation mechanism with delay to resolve the Non-IID issues, a blocking-resilient mechanism which allows receiving delayed parameters and gradients, and a communication-efficient mechanism including parameters compression and adaptive neighbors selection methods to reduce the communication cost as much as possible. In addition, we also provide convergence analysis of RCEDL and prove its convergence rate O (1 N K ) same with the state-of-the-art decentralized learning algorithms. Finally, we conduct extensive experiments to evaluate RCEDL algorithm on two widely used datasets CIFAR-10 and MNIST under different experimental settings. Compared with the state-of-the-art baseline methods, the proposed RCEDL is much more robust with higher accuracy and at least 3.4 × communication cost reduction under the heterogeneous environment. [ABSTRACT FROM AUTHOR]