We propose a token-based blockchain system that streamlines abstractions into a universal token structure. In the proposed system, each token has an identity that enables the implementation of specific rollbacks and governance that make 51% attacks unprofitable. Because the token-based bookkeeping method only verifies and updates the ownership within each transaction, the proposed system supports parallel expansion and cross-chain transactions without limit. The flexible authority management mechanism of the proposed system is regulatory-friendly, as the intensity of supervision and governance can be adapted to accommodate different application scenarios.

With the convergence of fixed and mobile networks, heterogeneous networks are becoming ubiquitous. Internet giants are seeing the plight of identity authentication. To address this issue, unified access management (UAM) was conceived. This paper provides a novel unified access management scheme, named SGX-UAM, with one-time passwords (OTPs) based on Intel software guard extensions (SGX). SGX-UAM outperforms generic UAM for providing resistance to most client attacks, man-in-the-middle (MITM) attacks, phishing attacks, most replay attacks and most denial of service (DoS) attacks to which generic UAM implementaions are vulnerable. Specifically, client attacks are prevented by ensuring input security and memory security, where the former is achieved through shuffle mapping and “periodic hooking” strategy, the latter is mainly guaranteed by Intel SGX; MITM attacks are prevented by transferring ciphertext rather than plaintext; phishing attacks are avoided by authorization control; replay attacks cannot succeed because we adopts OTPs, which contain time-related dynamic factors that expire in a few seconds; as for DoS attack, we blunted its edge by blocking-invocation for identical user connection. SGX-UAM also differs from generic UAM in that it relieves the security concerns of sevice providers (SPs) and protects users’ privacy at little cost of performance. An exceptional value of SGX-UAM is that it brings a lightweight OTP solution that eliminates the need of additional hardware devices, thus reducing the costs. The experimental results show that SGX-UAM consumes almost the same time with OpenID and OAuth2.0 for one login request and performs steadily when handling sequential login requests. Furthermore, the resource usage for SGX-UAM is acceptable.

Wireless sensor networks are appealing, largely because they do not need wired infrastructure, but it is precisely this feature that renders them energy-constrained. The duty cycle scheduling is perceived as a contributor to the energy efficiency of sensing. This paper developed a novel paradigm for modeling wireless sensor networks; in this context, an adaptive sensing scheduling strategy is proposed depending on event occurrence behavior, and the scheduling problem is framed as an optimization problem. The optimization objectives include reducing energy depletion and optimizing detection accuracy. We determine the explicit form of the objective function by numerical fitting and found that the objective function aggregated by the fitting functions is a bivariate multimodal function that favors the Fibonacci tree optimization algorithm. Then, with the optimal parameters optimized by the Fibonacci tree optimization algorithm, the scheduling scheme can be easily deployed, and it behaves consistently in the coming hours. The proposed “Fibonacci Tree Optimization Strategy” (“FTOS”) outperforms lightweight deployment-aware scheduling (LDAS), balanced-energy scheduling (BS), distributed self-spreading algorithm (DSS) and probing environment and collaborating adaptive sleeping (PECAS) in achieving the aforementioned scheduling objectives. The Fibonacci tree optimization algorithm has attained a better optimistic effect than the artificial bee colony (ABC) algorithm, differential evolution (DE) algorithm, genetic algorithm (GA) algorithm, particle swarm optimization (PSO) algorithm, and comprehensive learning particle swarm optimization (CLPSO) algorithm in multiple runs.

Classical theories explaining the evolution of cooperation often rely on the assumption that the involved players are symmetrically interacted. However, in reality almost all well-documented cooperation systems show that cooperative players are in fact asymmetrically interacted and that this dynamic may greatly affect the cooperative behavior of the involved players. Here, we developed several models based on the most well known spatial game of the Hawk-Dove game, while also considering the effects of asymmetric interaction. Such asymmetric games possess four kinds of strategies: cooperation or defection of strong player and cooperation or defection of weak player. Computer simulations showed that the probability of defection of the strong player decreases with decreasing the benefit to cost ratio, and that all kinds of strategy will be substituted by cooperation on behalf of the strong player if the benefit to cost ratio is sufficiently small. Moreover, weak players find it difficult to survive and the surviving weak players are mostly defectors, similar to the Boxed Pigs game. Interestingly, the patterns of kinds of strategies are chaotic or oscillate in some conditions with the related factors.

Cloud-edge data security is a key issue in the internet of vehicles (IoV), as the potential for data breaches increases as more vehicles are connected. As vehicles become smarter and more connected, the risk of unauthorized access to the data generated by the vehicles also increases. Data encryption is a highly effective security measure that is widely used to protect the IoV from malicious actors. By encrypting data, it becomes virtually impossible for unauthorized individuals to access the information. This ensures that only the intended parties can access the data, allowing for secure communication between cloud and edge. Data encryption is a cost-effective and reliable security measure that is essential for any organization that relies on the IoV. The IoV is characterized by the large volume of data that is exchanged between devices in cloud and edge. This necessitates the use of a strong encryption method, such as stream ciphering, which is particularly well-suited to this type of environment. Stream ciphering provides the highest levels of security, making it the ideal choice for securing data transmission in the IoV. Many stream ciphering algorithms use bitwise exclusive or (XOR) to encrypt the data stream, so the core is the generation of a pseudo-random key stream. This paper proves that the probability of the number 1 appearing in the middle part of the Zeckendorf representation is constant, which can be used to generate pseudo-random key stream sequences. The pseudo-random sequence generated by the linear feedback shift register (LSFR) is periodic, and the key sequence will be duplicated. The logistic chaos (LC) sequence is too sensitive to the disturbance of initial value, and its stability is poor. In this paper, our proposed ZPKG (key generator based on Zeckendorf presentation) algorithm solves these two main problems in stream ciphering. The generated key sequence not only has strong randomness, but also is infinitely long, and it is robust to the minor disturbance of the initial value.