Authentication is crucial in Internet of Things (IoT) networks as it ensures the integrity, accuracy, and tamper-resistance of information. This research article aims to design a lightweight and robust secure transmission system for IoT-enabled devices, enabling secure interaction with public cloud computing. The proposed system leverages blockchain technology integrated with elliptic curve cryptography (ECC), resulting in a resilient and efficient scheme tailored for resource and energy-constrained devices. We have adopted the SECP256K1 elliptic curve to design a Lightweight and Robust Anonymous ECC and Blockchain (LRAEB) scheme that offers superior performance. The use of blockchain guarantees the integrity and confidentiality of data stored in public cloud servers, addressing security vulnerabilities such as data leakage and unauthorized access. To validate the security of this novel technique, it will undergo verification using the Random Oracle Model (ROM) and Python. Our theoretical analysis confirms that the LRAEB scheme achieves better efficiency compared to existing schemes. In conclusion, we anticipate that this novel technique will significantly enhance information flow while mitigating the security flaws associated with public cloud computing and IoT devices.

The Internet of Vehicles (IoVs) integrates vehicles to the enormous realm of cyberspace which introduces some intelligence and convenience in the transportation sector. However, real-time traffic related information is exchanged over the open public internet among the vehicles and with other infrastructures. This exposes these networks to a myriad of security threats that can lead to accidents and congestions. Although many solutions have been developed over the recent past, most of them are inefficient while others are still susceptible to attacks. In this paper, we leverage on the k-valued modified bilinear inverse Diffie-Hellman problem and one-way hashing function to develop an efficient authentication protocol for IoVs. To demonstrate the robustness of its semantic security, we deploy the Real or Random (ROR) model. In addition, we execute extensive informal security anaysis to show that our scheme resists typical IoVs attacks such as forgery, privileged insider, and replay. Moreover, its performance evaluation shows that it incurs the lowest computation and communication overheads among its peers. Specifically, the proposed protocol reduces the transmission overheads by 8.5%, while increasing the supported security functionalities by 88.9%. It is therefor suitable for deployment in the IoV environment to mitigate the numerous security threats at relatively lower computation and energy costs.

The Internet of Medical Things (IoMT) holds the promise of cutting costs and improving both efficiency and quality of healthcare systems. In IoMT, billions of sensitive patient physiological data are exchanged among the patients and healthcare facilities. Since open channels are used during these exchanges, attackers can compromise this data, posing serious consequences which can be fatal. Although a myriad of techniques have been developed to mitigate these threats, they are either inefficient or lack some pertinent security features. In this paper, we leverage on lightweight cryptographic primitives to develop an authentication protocol that is not only efficient but also robust against typical IoMT threats. The performance evaluation carried out shows that our approach reduced the computation complexity by 58.26% while thwarting threats such as session key disclosure, privileged insider, offline guessing, replay, side-channeling and session hijacking. In addition, the formal security analysis using Burrows–Abadi–Needham (BAN) logic demonstrates the robustness of the mutual authentication procedures.

Robotic autonomous ultrasound imaging is a challenging task as robots require strong analytical capabilities to make sound decisions in complex spatial relationships. In this paper, we integrate visual and tactile information into the ultrasound robotic system drawing inspiration from the process of human doctors conducting ultrasound scans, and explore the impact of different modalities of information on our task. The proposed multimodal deep reinforcement learning (DRL) framework can integrate real-time visual feedback and tactile perception, and directly output 6D pose decisions to control the ultrasound probe, thereby achieving fully autonomous ultrasound imaging of soft, movable, and unmarked targets. We demonstrate the feasibility of our method on a simulation platform and propose an effective model transfer learning method. Subsequently, we conducted further evaluations of the approach in a real-world environment. The results indicate that our approach effectively enhances the performance of autonomous ultrasound scanning and manual adjustments further optimize the outcomes.Note to Practitioners—This work is motivated by the increasing demand for intelligent human-machine interaction in medical applications. By improving the automation of traditional medical scanning procedures such as ultrasound scanning, the efficiency of medical scanning can be greatly improved. In this work, we propose a multi-modal autonomous ultrasound scanning system based on DRL, which can be applied to improve the efficiency of human-machine interaction in medical environments to execute daily health screening or used in emergency situations.

This study presents a systematic literature review on blockchain-based authentication in smart environments that include smart city, smart home, smart grid, smart healthcare, smart farming and smart transportation. The review incorporated 39 articles presenting blockchain solutions for security and privacy issues through authentication mechanisms in these smart environments. Guided by three research questions to determine the main issues in smart environment, the availability of blockchain-based authentication solutions and identified research gaps and future research endeavors, this review used PRISMA method to provide insights on the use of blockchain-based authentication schemes. The research gap is that blockchain solutions are mostly at the proposal, and sometimes conceptual stage is in the reviewed articles. In addition, solutions presented in smart environments require exploration into blockchain. The findings show similar situational issues across different smart environments and the flexibility and adaptability of blockchain to provide solutions to the identified issues pertaining to security and privacy. More clearly, the authentication problem posed across different smart environments can be adapted to blockchain technology provided that it is combined with other technologies to increase efficiency, despite the existence of large-scale authentication mechanisms. Now, blockchain still has the unique, distributed feature of converting the current database into blockchain databases. This review guided future research directions which could further contribute to the sustainable management of smart environments.

Internet of Vehicles (IoVs) has greatly improved safety and quality of services in Intelligent Transportation System (ITS). However, the deployed Dedicated Short-Range Communication (DSRC) protocol broadcasts messages after every 100-300ms. This presents some challenges in message validation within this short duration. As such, most of the current authentication schemes which incur heavy computation and communication overheads are not suitable in this environment. In this paper, an efficient authentication scheme is presented based on lightweight cryptographic primitives such as collision-resistant one-way hashing functions and exclusive OR (XOR) operations. In our protocol, two-factor authentication is attained using Physically Unclonable Function (PUF) generated identities and random nonces, as well as passwords. Extensive formal security verification using Real or Random (RoR) model shows that it is provably secure. In addition, elaborate semantic security analysis shows that it offers anonymity, untraceability and key secrecy as well as resilience against numerous IoV attack vectors. In terms of performance, comparative evaluations demonstrate that it reduces computation and energy consumptions by 42.31%. Moreover, it increases the supported security features by 26.67%.

Document retrieval plays an essential role in many real-world applications especially when the data storage is outsourced. Due to the great advantages offered by cloud computing, clients tend to outsource their personal data to remote servers maintained by external service providers. This raises serious privacy concerns about outsourced data because such providers are usually considered untrusted entities. The majority of previous schemes of document similarity search share the same limitation: they focus mainly on static collections. Dynamic searchable schemes (DSE) allow adding or removing documents at the expense of more leakage than static schemes. To thwart certain attacks, DSE schemes should support forward privacy property, which ensures that newly added documents cannot be related to previously issued search queries. We design and implement dynamic secure similarity search schemes with forward privacy for textual documents utilizing simhash method for hamming similarity. Our scheme provides an efficient search time and a sufficient level of privacy. To show the practicality of our proposed scheme, we performed excremental results with large document collections.

Document Similarity Detection (DSD) is significant in our real life applications. However, the existing methods ignore the privacy of what is contained in the documents uploaded on remote servers, thus reducing the applicability of these methods. The proposed scheme allows documents to be compared without revealing to those remote servers. For each document, the fingerprint set is calculated. The inverted index is constructed on the basis of the whole fingerprint set. The inverted index is widely used for efficient retrieval. This index is under protection by Paillier cryptosystem before it gets uploaded to the server.