The incorporation of cloud to Internet of Health Things (IoHT) referred to as Cloud-assisted IoHT (CIoHT) assures efficient storage of the sensitive health data with better flexibility and scalability. However, owing to the openness nature of the CIoHT infrastructure, it is often susceptible to several security threats. On the contrary, security is an essential aspect of the CIoHT infrastructure. Thus, to address these security concerns, it is a common practice to establish a mutual authentication scheme among the communicating cloud entities that ensures to satisfy all the essential security requirements. In line with this, establishing trust among these entities is also a significant prerequisite. However, after a rigorous literature survey, it is found that all the existing authentication schemes are either vulnerable to various security threats or bear higher computation and/or communication overheads. Above all, these schemes have either taken the security or the trust aspect into consideration, but not both. Thus, we have proposed a Zero Trust Security (ZTS) based mutual authentication scheme for the CIoHT infrastructure using a Puncturable Pseudorandom Function (PPRF) in this paper. The detailed security analysis using informal and formal security analysis, and formal security verification using AVISPA tool ensures that the proposed scheme is highly robust against various known attacks needed in a CIoHT infrastructure. Moreover, the comprehensive comparative analysis exhibit that in comparison to the related literature our scheme provides higher computation and communication efficiency. Thus, unlike the existing schemes, the proposed scheme satisfies the vital security-trust-efficiency traid; ensuring its feasibility for implementation in real-world CIoHT infrastructure.

Vehicular Ad hoc NETwork (VANET) refers to an arbitrarily distributed network that is an integral subset of an Intelligent Transport System (ITS) and plays a vital role in providing convenient transportation, improving traffic safety, etc.VANETrealizes interactive communication through wireless medium among Vehicle to Vehicle (V2V) and Vehicle to Infrastructure (V2I). But, owing to the open/public communication nature of the wireless medium, it is often prone to different security threats. On the other hand, security is a significant aspect of theVANETframework. Thus, to address the security concerns, it is a common practice to establish an authentication and key agreement protocol among the communicating entities that ensures to provide the essential security requirements. However, after extensive literature survey, it is identified that all the existing authentication protocols are prone to different security threats. Moreover, the implementation of these protocols in real-world scenarios becomes impractical as it bears higher computation and/or communication overheads. Therefore, in this paper, we have proposed a trust-extended mutual authentication protocol for theVANETframework using a Puncturable Pseudorandom Function (PPRF). The informal and formal security verification of our protocol proves that it provides comparably higher security than the existing schemes. Further, our protocol is simulated using a well-known AVISPA simulation tool and the results show that our protocol is SAFE against replay and man-in-the-middle attacks. Additionally, in comparison to the existing literature our protocol provides higher computation and communication efficiency. Therefore, our protocol is reliable and secure for real-world implementation in theVANETframework.

The proliferation of 5G/B5G communication has led to increased integration between digital twin (DT) technology and connected autonomous vehicular systems (CAVS). The complex and resource-intensive vehicular applications pose significant connectivity and performance challenges for CAVS. To improve connectivity, optimize spectrum allocation, and mitigate network congestion, non-orthogonal multiple access (NOMA) is implemented. Furthermore, offloading and service caching are employed by storing and offloading relevant services at the edge of vehicular networks. However, due to the limited caching storage of vehicular edge servers, the decision to cache popular and emergent services to minimize delay and energy consumption becomes challenging. The decisions regarding computation offloading and service caching are also strongly coupled. In this work, a popularity-conscious service caching and offloading problem (PSCAOP) in a DT and NOMA-aided CAVS (DTCAVS) is studied. PSCAOP is mathematically constructed and observed to be NP-complete. Then a quantum-inspired particle swarm optimization (QPSO) algorithm is proposed for DTCAVS (DTCAVS-QPSO), aiming to minimize delay and energy consumption. DTCAVS-QPSO prioritizes the popular and emergent service caching. The quantum particle (QP) is encoded to provide a comprehensive solution to the PSCAOP. A one-time mapping algorithm is used to decode the QPs. The fitness function is formulated considering delay, energy consumption, and type of service. All the phases of DTCAVS-QPSO are observed to be bounded in polynomial time. The significance of the proposed DTCAVS-QPSO is demonstrated through extensive simulations and hypothesis-based statistical analysis. Experimental outcomes underscore the superiority of the DTCAVS-QPSO over other standard works, indicating an average delay and an energy consumption reduction between 6% and 49%.

During the last decade, there has been profound progress in the field of visual saliency. However, there still exist various major challenges that hinder the detection performance for scenes with complex composition, presence of additive noise, objects of diverse scale and rotations etc. Generally, images with additive noise have low spatial resolution and blurred edges, which affects the learning capability of the network and causes inaccurate detection. In order to address these issues, in this study, the authors propose a fully convolutional neural network which jointly denoise the input maps by learning edges and contrast details, followed by learning of residing salient details via colour spatial maps in an end‐to‐end fashion. Their framework employs convolutional layers that use gradient and contrast details of images to denoise the areas with high edge density. After denoising, the denoised images are subjected to salient object detection (SOD) using convolutional layers. The effectiveness of the proposed network is evaluated on benchmark datasets. The experimental results demonstrate the significant performance improvement of the proposed method over state‐of‐the‐art detection techniques.

This paper proposes a hybrid approach of personalized Web Information Retrieval that utilizes (1) ontology for retrieval of user's context (2) user profile that is temporarily updated according to users' browsing behavior and (3) collaborative filtering for considering recommendation of similar users. Empirical analysis reveals that Precision, Recall and F-Score of most of the queries for many users are improved with using the proposed method.