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This paper proposes a blockchain-based identity authentication (BA) scheme for IoT devices to solve the authentication security problem of IoT devices. The BA scheme uses hashing and digital signature algorithms to achieve integrity and nonrepudiation of authentication messages. Blockchain technology is used to achieve decentralised and distributed storage and management of authentication data. Besides, the BA scheme uses the idea of trust domains and trust credentials to establish a master-slave connection between IoT devices. The BA scheme is then compared with the existing four schemes and analysed from six perspectives to show that the BA scheme has better security. Also, the results show that the BA scheme has reasonable computational and storage overhead. Finally, the advantages of the BA scheme over traditional centralised and existing blockchain-based authentication schemes are compared and analysed. The results show that it can perfectly solve the problem of overreliance on trusted third parties in traditional authentication schemes. [ABSTRACT FROM AUTHOR]

Sensor web systems, cyber-physical systems, and the so-called Internet of Things are concepts that share a set of common characteristics. The nature of such systems is highly dynamic and very heterogeneous and issues such as interoperability, energy consumption, or resource management must be properly managed to ensure the operation of the applications within the required quality of service level. In this context, base technologies such as component based software engineering or Service Oriented Architecture can play a central role. Model driven development and middleware technologies also aid in the design, development, and operation of such systems. This paper presents a middleware solution that provides runtime support for the complete lifecycle management of a system consisting of several concurrent applications running over a set of distributed infrastructure nodes. The middleware builds up on top of a general purpose component model and is driven by a quality of service aware self-configuration algorithm that provides stateful reconfiguration capabilities in face of both internal (application triggered) and external (application unaware) reconfiguration events. The platform has been deployed over an automated warehouse supervision system that serves as a case study. [ABSTRACT FROM AUTHOR]

The advent of the Internet of things (IoT) is changing the way how we interact with the physical world. However, the current Internet suffers from exponential increase in bandwidth demand. In order to resolve the bandwidth issue, we can consider aerial networks by unmanned aerial vehicles (UAV) or the so-called drones for establishing a three-dimensional mobile network in an ad hoc manner. By deploying a network from the sky, we can use the otherwise idle wireless medium and high mobility free from ground obstacles. Aerial networks are especially effective for supporting the temporary surge of population as well as disaster areas because building an additional network infrastructure requires extensive time. In this paper, we propose an efficient handover mechanism for aerial networks in the three-dimensional space, which significantly differs from the conventional two-dimensional schemes. The proposed scheme adjusts the height of a drone and the distance between the drones. To this end, we use the seamless handover success probability and the false handover initiation probability in order to evaluate the optimal coverage decision algorithm. To the best of our knowledge, the proposed scheme is the first attempt for resolving handover of net-drones in the three-dimensional space. [ABSTRACT FROM AUTHOR]

The massive data generated by the Internet of Things (IoT) are considered of high business value, and data mining algorithms can be applied to IoT to extract hidden information from data. In this paper, we give a systematic way to review data mining in knowledge view, technique view, and application view, including classification, clustering, association analysis, time series analysis and outlier analysis. And the latest application cases are also surveyed. As more and more devices connected to IoT, large volume of data should be analyzed, the latest algorithms should be modified to apply to big data. We reviewed these algorithms and discussed challenges and open research issues. At last a suggested big data mining system is proposed. [ABSTRACT FROM AUTHOR]