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The extensive application of the Internet of Things (IoT) and artificial intelligence technology has greatly promoted the construction and development of smart cities. Smart home as the foundation of smart cities can optimize home lifestyles. However, users access the smart home system through public channels, and the transmitted information is vulnerable to attack by attackers, and the privacy and data security of the home user will be difficult to be guaranteed. Therefore, how to protect users' data and privacy security becomes critical. In this paper, we design a provably secure authentication scheme for the smart home environment, which ensures that only legitimate users can use smart devices. We use the informal model to verify the security of the scheme and formally analyze the security and correctness of the scheme through the Real or Random model. Finally, through the comparison of security and performance analysis, it is proven that our scheme has higher security under similar performance. [ABSTRACT FROM AUTHOR]

The smart home is a crucial embodiment of the internet of things (IoT), which can facilitate users to access smart home services anytime and anywhere. Due to the limited resources of cloud computing, it cannot meet users' real-time needs. Therefore, edge computing emerges as the times require, providing users with better real-time access and storage. The application of edge computing in the smart home environment can enable users to enjoy smart home services. However, users and smart devices communicate through public channels, and malicious attackers may intercept information transmitted through public channels, resulting in user privacy disclosure. Therefore, it is a critical issue to protect the secure communication between users and smart devices in the smart home environment. Furthermore, authentication protocols in smart home environments also have some security challenges. In this paper, we propose an anonymous authentication protocol that applies edge computing to the smart home environment to protect communication security between entities. To protect the security of smart devices, we embed physical unclonable functions (PUF) into each smart device. Real-or-random model, informal security analysis, and ProVerif are adopted to verify the security of our protocol. Finally, we compare our protocol with existing protocols regarding security and performance. The comparison results demonstrate that our protocol has higher security and slightly better performance. [ABSTRACT FROM AUTHOR]

With the maturity and popularization of the Internet of Things, we saw the emergence of the Internet of Vehicles. This collects and processes real-time traffic information, alleviates traffic congestion, and realizes intelligent transportation. However, sensitive information, such as real-time driving data of vehicles, are transmitted on public channels, which are easily to steal and manipulate for attackers. In addition, vehicle communications are vulnerable to malicious attacks. Therefore, it is essential to design secure and efficient protocols. Many studies have adopted asymmetric cryptosystems and fog computing to in this environment, but most of them do not reflect the advantages of fog nodes, which share the computational burden of cloud servers. Therefore, it is challenging to design a protocol that effectively uses fog nodes. In this paper, we design an authentication protocol based on a symmetric encryption algorithm and fog computing in the Internet of Vehicles. In this protocol, we first propose a four-layer architecture that significantly reduces the computational burden of cloud servers. To resist several well-known attacks, we also apply Intel software guard extensions to our protocol. This is because it can resist privileged insider attacks. We prove the security of the proposed protocol through the Real-Or-Random model and informal analysis. We also compare the performance of the proposed protocol with recent protocols. The results show better security and a lower computational cost. [ABSTRACT FROM AUTHOR]

Hierarchical key assignment scheme is an efficient cryptographic method for hierarchical access control, in which the encryption keys of lower classes can be derived by the higher classes. Such a property is an effective way to ensure the access control security of Internet of Things data markets. However, many researchers on this field cannot avoid potential single point of failure in key distribution, and some key assignment schemes are insecure against collusive attack or sibling attack or collaborative attack. In this paper, we propose a hierarchical key assignment scheme based on multilinear map to solve the multigroup access control in Internet of Things data markets. Compared with previous hierarchical key assignment schemes, our scheme can avoid potential single point of failure in key distribution. Also the central authority of our scheme (corresponding to the data owner in IoT data markets) does not need to assign the corresponding encryption keys to each user directly, and users in each class can obtain the encryption key via only a one-round key agreement protocol. We then show that our scheme satisfies the security of key indistinguishability under decisional multilinear Diffie-Hellman assumption. Finally, comparisons show the efficiency of our scheme and indicates that our proposed scheme can not only resist the potential attacks, but also guarantee the forward and backward security. [ABSTRACT FROM AUTHOR]

As the Internet of Things (IoT) becomes more and more intelligent, a new computing paradigm, predictive intelligence is incorporated into many IoT applications. The devices of predictive intelligence in IoT applications must consider the power and delay consumption. As the Power-Driven X-Routing (PDXR) problem model under the advanced semiconductor design, the length-restricted condition in the multi-dynamic voltage model is introduced to save power consumption and the X-architecture is introduced to better reduce the wirelength to optimize the chip delay. To this end, an effective particle swarm optimization-based power-driven length-restricted X-routing algorithm is proposed for predictive intelligence in IoT applications. Firstly, a pre-calculated lookup table is designed to provide fast information query for the subsequent algorithm flow. Secondly, an improved particle swarm optimization algorithm is presented for the discrete PDXR problem. Thirdly, in the adjustment phase, the choice of intermediate nodes is expanded, and is no longer limited to the corner points of obstacles. Fourthly, a removal strategy of redundant points is proposed to optimize the routing path. Finally, the wirelength is further reduced by a local topology optimization strategy. The experimental results show that the proposed algorithm can achieve the best wirelength cost at a very fast speed under the constraint of restricted wirelength, so as to better satisfy the demand of the power and delay performance of semiconductor design for predictive intelligence in IoT applications. [Display omitted] • An improved PSO algorithm is presented for discrete routing problem. • A preprocessing technology is designed to construct initial routing tree. • An adjustment strategy is proposed to correct edges that violate constraints. • An optimization system is developed to further optimize wirelength. [ABSTRACT FROM AUTHOR]

This paper carries out the mechanism analysis of non-inertial particle swarm optimization. It is a modified algorithm based on a new kinetic equation which is applied to the prediction of non-stationary time series for the internet of things in the edge computing. In order to avoid premature convergence and accelerate convergence rate, different from standard particle swarm optimization, the modified algorithm uses a new kinetic equation to lead particles motion direction, besides generalized opposition-based learning (GOBL) and adaptive elite mutation (AEM) strategies. The work presents the second order standardized recurrence equation for the new kinetic equation whose corresponding characteristic equations can be analyzed via the difference functions to obtain the boundaries of coefficients and its convergence region. Besides, GOBL and AEM strategies are also analyzed to boost global and local convergence of the algorithm as an interpretation. It is illustrated that performance analysis of the algorithm with two well-known test functions. The good performance is further validated from application in edge computing. [ABSTRACT FROM AUTHOR]