The secure transmission of instructions among vehicles in a platoon is one of the most essential needs for a vehicle platoon. Despite the existence of cryptographic methods to securely share instructions, instruction sharing is still subject to forgery, tampering, and denial-of-service attacks. Therefore, it is urgent to find a solution to perform data source identification to filter out irrelevant information (not instructions) while ensuring the authenticity of encrypted instructions is urgent to address. In addition, immediate revocation of credentials is also a crucial requirement for a vehicle platoon when an authorized vehicle member misbehaves. In this paper, we propose the first Secure Source Identification Scheme for Revocable Instruction Sharing (SI-RIS) to securely simultaneously achieve bilateral fine-grained access control, data source identification, immediate vehicle user revocation, and efficient encryption in vehicle platoons. Specifically, our SI-RIS solution supports fine-grained access control for both the sender and receiver over the encrypted instructions. As a result, only authorized correspondents are able to access the commands. Furthermore, upon identification of malicious members in the platoon, our SI-RIS provides an efficient direct vehicle user revocation mechanism capable of immediate revocation credentials without affecting other vehicles. We prove the security of our SI-RIS via rigorous mathematical security proof. Moreover, performance evaluation and comparisons illustrate the feasibility and practicability of SI-RIS for vehicle platoon.

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As an emerging field of communication, Vehicular Social Networks (VSNs) can reduce traffic congestion while enhancing road safety by sharing data among groups of commuters. In VSNs, Vehicular Cloud Server (VCS) based data sharing technology with encrypted primitives allows local users to outsource encrypted data for reducing the storage burden on the user side and sharing data without location restrictions. However, existing data encryption solutions that have been applied in VSNs environments still encounter weaknesses in efficiency, security, or privacy due to the following problems: (1) lack of effective access policies for flexible authorizing ciphertext to multiple data users; (2) data breaches caused by malicious data publishers; (3) necessity in hiding the private information of receivers. To date, no such solution has been available that securely enables one-to-many user authorization with privacy protection, while greatly resisting malicious data publishers. We propose a Sanitizable Access Control System with Policy-protection (SASP) for VSNs in this paper. Our SASP enables a sanitizer to test and sanitize encrypted data to defend against malicious data publishers, ensuring that the plaintext can only be recovered if an authorized user has a valid key. Furthermore, in our SASP system, the access policy is separated into attribute names and attribute values. Wherein, the attribute values contain a lot of private information, which is hidden in the ciphertext to guarantee data users' privacy. Rigorous security analysis and performance evaluations demonstrate the practicality of SASP for VSNs.

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The deep reinforcement learning-based energy management strategies (EMS) have become a promising solution for hybrid electric vehicles (HEVs). When driving cycles are changed, the neural network will be retrained, which is a time-consuming and laborious task. A more efficient way of choosing EMS is to combine deep reinforcement learning (DRL) with transfer learning, which can transfer knowledge of one domain to the other new domain, making the network of the new domain reach convergence values quickly. Different exploration methods of DRL, including adding action space noise and parameter space noise, are compared against each other in the transfer learning process in this work. Results indicate that the network added parameter space noise is more stable and faster convergent than the others. In conclusion, the best exploration method for transferable EMS is to add noise in the parameter space, while the combination of action space noise and parameter space noise generally performs poorly. Our code is available at https://github.com/BIT-XJY/RL-based-Transferable-EMS.git.@en

In this work, different lanthanides (Tm³⁺, Er³⁺; Yb³⁺, Ho³⁺, Nd³⁺) were doped into NaYF₄ via a high-temperature coprecipitation method, and followed by SiO₂ coating to improve the water dispersibility, resulting in NaYF₄:Tm³⁺, Er³⁺@NaYF₄@SiO₂ and NaYF₄:Yb³⁺, Ho³⁺@NaYF₄:Nd³⁺@SiO₂ nanoparticles (NPs). The two NPs both exhibited the temperature-dependent second near-infrared (NIR-II) downshifting luminescence over the physiological range. The luminescence ratio of Tm³⁺ emission at 1460 nm to Er³⁺ emission at 1525 nm (Tm³⁺:³H₄ → ³F₄; Er³⁺:⁴I_13/2 → ⁴I_13/2) varies with temperature increase, as well as Yb³⁺ emission at 980 nm and Ho³⁺ emission at 1150 nm (Yb³⁺:²F_5/2 → ²F_7/2; Ho³⁺:⁵I₆ → ⁵I₈). The highest relative sensitivity of NaYF₄:Tm³⁺, Er³⁺@NaYF₄@SiO₂ and NaYF₄:Yb³⁺, Ho³⁺@NaYF₄:Nd³⁺@SiO₂ aqueous suspension is 0.36% K⁻¹ (at 298 K) and 0.76% K⁻¹ (at 343 K), respectively. The biological tests prove the good biocompatibility and low toxicity of the water-soluble NPs. In vitro tissue penetration experiments verify a much better penetration ability of the synthesized NaYF₄:Tm³⁺, Er³⁺@NaYF₄@SiO₂ compared with NaYF₄:Yb³⁺, Ho³⁺@NaYF₄:Nd³⁺@SiO₂ NPs. The excellent physiological luminescent thermometry with favor wave penetration depth provides a promising platform in deep tissue temperature measurement, which is very important in vivo biosensing.

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