The Internet of Things (IoT) is the next era of communication. Using the IoT, physical objects can be empowered to create, receive, and exchange data in a seamless manner. Various IoT applications focus on automating different tasks and are trying to empower the inanimate physical objects to act without any human intervention. The existing and upcoming IoT applications are highly promising to increase the level of comfort, efficiency, and automation for the users. To be able to implement such a world in an ever-growing fashion requires high security, privacy, authentication, and recovery from attacks. In this regard, it is imperative to make the required changes in the architecture of the IoT applications for achieving end-to-end secure IoT environments. In this paper, a detailed review of the security-related challenges and sources of threat in the IoT applications is presented. After discussing the security issues, various emerging and existing technologies focused on achieving a high degree of trust in the IoT applications are discussed. Four different technologies, blockchain, fog computing, edge computing, and machine learning, to increase the level of security in IoT are discussed.

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A Survey on IoT Security: Application Areas, Security Threats, and Solution Architectures

Survey on blockchain for Internet of Things

Internet of Things (IoT) is one of the most rapidly used technologies in the last decade in various applications. The smart things are connected in wireless or wired for communication, processing, computing, and monitoring different real-time scenarios. The things are heterogeneous and have low memory, less processing power. The implementation of the IoT system comes with security and privacy challenges because traditional based existing security protocols do not suitable for IoT devices. In this survey, the authors initially described an overview of the IoT technology and the area of its application. The primary security issue CIA (confidentially, Integrity, Availability) and layer-wise issues are identified. Then the authors systematically study the three primary technology Machine learning(ML), Artificial intelligence (AI), and Blockchain for addressing the security issue in IoT. In the end, an analysis of this survey, security issues solved by the ML, AI, and Blockchain with research challenges are mention.

Survey on IoT security: Challenges and solution using machine learning, artificial intelligence and blockchain technology

In this work, we initially study the necessary properties and security requirements of Ring Confidential Transaction (RingCT) protocol deployed in the popular anonymous cryptocurrency Monero. Firstly, we formalize the syntax of RingCT protocol and present several formal security definitions according to its application in Monero. Based on our observations on the underlying (linkable) ring signature and commitment schemes, we then put forward a new efficient RingCT protocol (RingCT 2.0), which is built upon the well-known Pedersen commitment, accumulator with one-way domain and signature of knowledge (which altogether perform the functions of a linkable ring signature). Besides, we show that it satisfies the security requirements if the underlying building blocks are secure in the random oracle model. In comparison with the original RingCT protocol, our RingCT 2.0 protocol presents a significant space saving, namely, the transaction size is independent of the number of groups of input accounts included in the generalized ring while the original RingCT suffers a linear growth with the number of groups, which would allow each block to process more transactions.

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RingCT 2.0: A Compact Accumulator-Based (Linkable Ring Signature) Protocol for Blockchain Cryptocurrency Monero.

Internet of Things (IoT) has been the most emerging technology in the last decade because the number of smart devices and its associated technologies has rapidly grown in both industrial and research prospectives. The applications are developed using IoT techniques for real-time monitoring. Due to low processing power and storage capacity, smart things are vulnerable to the attacks as existing security or cryptography techniques are not suitable. In this study, we initially review and identify the security and privacy issues that exist in the IoT system. Second, as per blockchain technology, we provide some security solutions. The detailed analysis, including enabling technology and integration of IoT technologies, is explained. Finally, a case study is implemented using the Ethererum-based blockchain system in a smart IoT system and the results are discussed.

Addressing Security and Privacy Issues of IoT Using Blockchain Technology

In the Internet of Things (IoT), a cyber physical system (CPS) has achieved great success in a wide range of distributed integration environments. In the cyber physical system (CPS), interconnected sensing devices collect data in the surrounding environment and send data to all interested nodes through the network, thereby sharing all nodes data. This process can be implemented by using a publish/subscribe (pub/sub for short) system. Providing the basic security mechanisms such as authorization and confidentiality is a challenge due to the loose coupling of subscribers and publishers in such a pub/sub system. At the meanwhile, the existing IoT ecosystem mostly relies on a centralized server and thus faces the problem of single point failure. Thus, it is interesting to realize a brokerless or decentralized pub/sub model. Inspired by this motivation, this paper mainly proposes a privacy-preserving publish/subscribe model by using the blockchain technique, which evades the centralized trustroot setting and the problem of single point failure. Another key point of our proposal is that the primitive of public key encryption with equality test (PKEwET) is employed to enable all the required authorization, communication and topics matching can be finished in a confidential manner. Finally, a lightweight prototype of our proposal is implemented by using web3j, and the security and efficiency analysis are also presented.

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An IOT-Oriented Privacy-Preserving Publish/Subscribe Model Over Blockchains

The errors of an inertial navigation system (INS) in response to gyros' errors can be effectively reduced by the rotation technique, which is a commonly used method to improve an INS's accuracy. A gyro's error consists of a deterministic contribution and a stochastic contribution. The compensation effects of gyros' deterministic errors are clear now, but the compensation effects of gyros' stochastic errors are as yet unknown. However, the compensation effects are always needed in a rotational inertial navigation system's (RINS) error analysis and optimization study. In this paper, the compensation effects of gyros' stochastic errors, which are modelled as a Gaussian white (GW) noise plus a first-order Markov process, are analysed and the specific formulae are derived. During the research, the responses of an INS's and a RINS's position error equations to gyros' stochastic errors are first analysed. Then the compensation effects of gyros' stochastic errors brought by the rotation technique are discussed by comparing the error propagation characteristics in an INS and a RINS. In order to verify the theory, a large number of simulations are carried out. The simulation results show a good consistency with the derived formulae, which can indicate the correctness of the theory.

The Compensation Effects of Gyros' Stochastic Errors in a Rotational Inertial Navigation System

Noncommutativity error of a strapdown inertial navigation system (SINS) in an unmanned aerial vehicle's (UAV) vibration environment is analysed. The traditional analysis of noncommutativity errors is based on a coning motion model, which is inconsistent with a UAV's vibration environment. In this paper the UAV's vibration form is discussed and is modelled as a sinusoidal angular vibration and a random angular vibration. Then, SINS motion models under these two forms of vibration are built up and the formulas for the noncommutativity errors are derived separately. In addition, the effect of a multi-sample algorithm is explored, which is an effective method for compensating for noncommutativity errors in cases of coning motion. Finally, the UAV's vibration environment is simulated and it is indicated that the simulation results of the SINS's noncommutativity errors are consistent with theoretical analysis.

https://journals.sagepub.com/doi/pdf/10.5772/52758

Noncommutativity Error Analysis of Strapdown Inertial Navigation System under the Vibration in UAVs

As the development of smart grids and electricity markets around the world, short-term load forecasting (STLF) plays an increasingly important role in safe and economical operations of power systems. Facing massive multivariate and heterogeneous data from smart grid environment, traditional STLF methods no longer meet the requirements of comprehensive prediction performance in big data era. Therefore, a novel STLF model named EGA-STLF is proposed in this paper. The core idea of the proposed model is mainly based on distributed representation, bidirectional gated recurrent unit (Bi-GRU), and attention mechanism. EGA-STLF encodes non-numerical variables in input data into sparse vectors by distributed representation during preprocessing, which can more reasonably reflect the correlations between different variables across the temporal sequence. Moreover, the Bi-GRU layer in EGA-STLF processes the past and the future information simultaneously to fully extract temporal and nonlinear features from input data for the improvement of forecasting accuracy. In addition, the introduction of attention mechanism highlights the role of key features in load forecasting, which is beneficial to generate more accurate forecasting results for EGA-STLF. The validity and superiority of EGA-STLF is verified by taking the actual data from Victoria state and Queensland state in Australia for experimental research in which mean absolute percentage error (MAPE) and root mean square error (RMSE) are selected as evaluation metrics. The experimental results indicate that EGA-STLF outperforms the state-of-the-art models based on SVR and MLP in term of comprehensive forecasting accuracy. Hence, EGA-STLF is a promising method to create economic benefits in power industry.

EGA-STLF: A Hybrid Short-Term Load Forecasting Model

To find out the hidden danger in the industrial production process in time, it is necessary to monitor the health condition of the key components of the mechanical system in operation. However, traditional fault diagnosis methods usually adopt manual feature extraction, which not only costs expensively and depends on prior knowledge. Therefore, it is of great significance to study the application of automatic fault identification based on the original vibration signals. Recently, existing studies have shown that most of fault diagnoses are implemented by using deep neural network. Although these methods have achieved satisfactory performances, there are obvious limitations in real applications, that is, the complexity of deep neural network requires a lot of hardware computing resources. This hinders the development of online fault diagnosis tools. To solve this problem, this paper proposes a fault diagnosis model based on lightweight convolutional neural network MobileNet, and realizes an end-to-end intelligent fault classification and diagnosis application. We evaluated the proposed method with the rolling bearing dataset from Western Reserve University. The best average precision, recall and F1 score of ten different bearing health conditions are about 96%, 82% and 88%, respectively. In addition, we also compare the accuracy of the rolling bearing fault diagnosis classification model under the standard ReLU and the improved ReLU. Experimental results show that both obtain good performances, but the improved ReLU reaches the over 96% accuracy more rapidly.

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Papers

An IOT-Oriented Privacy-Preserving Publish/Subscribe Model Over Blockchains

The Compensation Effects of Gyros' Stochastic Errors in a Rotational Inertial Navigation System

Noncommutativity Error Analysis of Strapdown Inertial Navigation System under the Vibration in UAVs

EGA-STLF: A Hybrid Short-Term Load Forecasting Model

An End-to-End Intelligent Fault Diagnosis Application for Rolling Bearing Based on MobileNet