Blockchain technology is becoming increasingly attractive to the next generation, as it is uniquely suited to the information era. Blockchain technology can also be applied to the Internet of Things (IoT). The advancement of IoT technology in various domains has led to substantial progress in distributed systems. Blockchain concept requires a decentralized data management system for storing and sharing the data and transactions in the network. This paper discusses the blockchain concept and relevant factors that provide a detailed analysis of potential security attacks and presents existing solutions that can be deployed as countermeasures to such attacks. This paper also includes blockchain security enhancement solutions by summarizing key points that can be exploited to develop various blockchain systems and security tools that counter security vulnerabilities. Finally, the paper discusses open issues relating to and future research directions of blockchain-IoT systems.INDEX TERMS Blockchain, Internet of Things, threats and attacks, security.

Clustering is an effective way to prolong the lifetime of a wireless sensor network (WSN). The common approach is to elect cluster heads to take routing and controlling duty, and to periodically rotate each cluster head’s role to distribute energy consumption among nodes. However, a significant amount of energy dissipates due to control messages overhead, which results in a shorter network lifetime. This paper proposes an energy-centric cluster-based routing mechanism in WSNs. To begin with, cluster heads are elected based on the higher ranks of the nodes. The rank is defined by residual energy and average distance from the member nodes. With the role of data aggregation and data forwarding, a cluster head acts as a caretaker for cluster-head election in the next round, where the ranks’ information are piggybacked along with the local data sending during intra-cluster communication. This reduces the number of control messages for the cluster-head election as well as the cluster formation in detail. Simulation results show that our proposed protocol saves the energy consumption among nodes and achieves a significant improvement in the network lifetime.

The IPv6 routing protocol for low power and lossy networks (RPL) was designed to satisfy the requirements of a wide range of Internet of Things (IoT) applications, including industrial and environmental monitoring. In most scenarios, different from an ordinary environment, the industrial monitoring system under emergency scenarios needs to not only periodically collect the information from the sensing region, but also respond rapidly to some unusual situations. In the monitoring system, particularly when an event occurs in the sensing region, a surge of data generated by the sensors may lead to congestion at parent node as data packets converge towards the root. Congestion problem degrades the network performance that has an impact on quality of service. To resolve this problem, we propose a congestion-aware routing protocol (CoAR) which utilizes the selection of an alternative parent to alleviate the congestion in the network. The proposed mechanism uses a multi-criteria decision-making approach to select the best alternative parent node within the congestion by combining the multiple routing metrics. Moreover, the neighborhood index is used as the tie-breaking metric during the parent selection process when the routing score is equal. In order to determine the congestion, CoAR adopts the adaptive congestion detection mechanism based on the current queue occupancy and observation of present and past traffic trends. The proposed protocol has been tested and evaluated in different scenarios in comparison with ECRM and RPL. The simulation results show that CoAR is capable of dealing successfully with congestion in LLNs while preserving the required characteristics of the IoT applications.

The emergence of an array of new wireless networks has led researchers to evaluate the prospect of utilizing the physical properties of the wireless medium in order to design secure systems. In this paper, the physical layer secrecy performance of a mixed radio frequency-free space optical (RF-FSO) system with variable gain relaying scheme is investigated in the presence of an eavesdropper. We assume that the eavesdropper can wiretap the transmitted confidential data from the RF link only. It is further assumed that the main and eavesdropper RF links are modeled as generalized Gamma (GG) fading channel, and the free space optical (FSO) link experiences Málaga turbulence with pointing error impairment. Our primary concern is to protect this confidential information from being wiretapped. Besides pointing error, the atmospheric turbulence and two types of detection techniques (i.e. heterodyne detection and intensity modulation with direct detection) are also taken into consideration. Utilizing amplify-and-forward (AF) scheme, the novel mathematical closed-form expressions for average secrecy capacity, lower bound of secrecy outage probability, and strictly positive secrecy capacity are derived. As both the links (RF and FSO) undergo generalized fading channels, the derived expressions are also general. We present a unification of some existing works utilizing the proposed model to better clarify the novelty of this work. Finally, all the derived expressions are justified via Monte-Carlo simulations. INDEX TERMS Physical layer security, Generalized Gamma fading, Málaga fading, variable gain relay, average secrecy capacity, strictly positive secrecy capacity, and secrecy outage probability.

Blockchain is attracting more and more attention to its applicability in the fields of Internet of Things (IoT). In particular, it is able to store data in unalterable blocks, associated with its secure peer-topeer in a growing problem of transaction authorization in industrial and service provisioning applications. Moreover, it facilitates decentralized transaction (TX) validation and distributed ledger. The underneath algorithm of TX selection for validation may not be effective in terms of delay of various services of the applications. Because the existing random-based or fee-based selections are a delay insensitive that does not guarantee a minimum delay of a time-critical TX. This paper proposes a blockchain-based transaction validation protocol for a secure distributed IoT network. It includes a context-aware TX validation technique, where a TX is validated by a miner with the priority of a service. Besides, we adopt the Software Defined Networking enabled gateway as a middleware between IoT and the blockchain network in which the control operations and security of the network in a largescale are ensured. The proposed network model has evaluated and compared to the Core network. The results ensure the given priority in TX validation is more delay sensitive than the existing technique to provide quality of service of the network. INDEX TERMS Internet of things, blockchain technology, software defined networking, delay, security and privacy.

Predicting the time, location and magnitude of an earthquake is a challenging job as an earthquake does not show specific patterns resulting in inaccurate predictions. Techniques based on Artificial Intelligence (AI) are well known for their capability to find hidden patterns in data. In the case of earthquake prediction, these models also produce a promising outcome. This work systematically explores the contributions made to date in earthquake prediction using AI-based techniques. A total of 84 scientific research papers, which reported the use of AI-based techniques in earthquake prediction, have been selected from different academic databases. These studies include a range of AI techniques including rule-based methods, shallow machine learning and deep learning algorithms. Covering all existing AI-based techniques in earthquake prediction, this paper provides an account of the available methodologies and a comparative analysis of their performances. The performance comparison has been reported from the perspective of used datasets and evaluation metrics. Furthermore, using comparative analysis of performances the paper aims to facilitate the selection of appropriate techniques for earthquake prediction. Towards the end, it outlines some open challenges and potential research directions in the field.

Medical Imaging is the most significant technique that constitutes information needed to diagnose and make the right decisions for treatment. These images suffer from inadequate contrast and noise that occurs during image acquisition. Thus, denoising and contrast enhancement is crucial in increasing the visual quality of the images for obtaining quantitative measures. In this research, an innovative and improvised denoising technique is implemented that applies a sparse aware with convolution neural network (SA_CNN) for investigating various medical modalities. To evaluate and validate, the convolution neural network utilizes patch creation and dictionary methods for obtaining information. The proposed framework is predominant to other current approaches by employing image assessment quantitative measures like peak signal to noise ratio (PSNR), structural similarity index (SSIM), and mean squared error (MSE). The study also optimizes the computational time to achieve increased efficiency and better visual quality of the image. Furthermore, the widespread use of the Internet of Healthcare Things (IoHT) helps to provide security with vault and challenge schemes between IoT devices and servers.

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