Emerging Unmanned Aerial Vehicles (UAVs) have applications for traffic monitoring, public safety, surveillance, agriculture, health services. Collaborative UAVs can form flying ad hoc networks, although such networks are especially vulnerable to security vulnerabilities due to open access media and limited power. Very recently, Khan et al. presented an Identity-Based Generalized Signcryption having Multi-access Edge computing to secure Flying Ad hoc Networks (FANETs). First, this paper presents the cryptanalysis of the Khan et al. scheme and shows that their scheme does not provide message confidentiality, Authenticity, and integrity. Second, it presents an improved scheme as well. The comparison of the improved scheme with the state of the art schemes based on security and cost shows, it is efficient, provably secure against security attacks, and suitable for multi-access edge computing empowered FANETs.

The electronic applications of financial institutions like banks and insurance companies use either token-based, biometric-based, or knowledge-based password scheme to keep the confidential information of their customers safe from hackers. The knowledge-based password scheme’s resistance, particularly its reformation-based password scheme against shoulder surfing attacks, is comparatively better than the other two because its password can be entered in crowded places without fear of shoulder surfers. However, the available reformation based passwords involve mental computation making their usability difficult. Furthermore, they also need an extra device like earphones during password entry causing to create a gap for information leakage. Moreover, most of the passwords store passwords’ actual content on a server database that causes penetration in the financial institutions’ database. In this article, a reformation-based password scheme involving no mental computation and using no extra device is proposed. The proposed scheme works on the password characters’ indices, which change dynamically after each login process. It gets the password characters’ indices from the end-user and obtains his password characters’ indices from the database. Next, the textual passwords are formed from the user-provided indices and those obtained from the database. The textual passwords are then compared, and if found match, then login is succeeded, otherwise failed. Our proposed password scheme’s experimental results on the password data set showed better security and usability compared to state-of-art password schemes.

Reconfigurable antenna systems are a key technology for sixth-generation (6G) networks. They enable dynamic adaptation in frequency, spatial, and polarization domains. This paper presents a unified framework for fluid antennas, pinching antennas, electromagnetically reconfigurable antennas, graphene-based terahertz (THz) antennas, and stretchable or conformal arrays. Physical principles, architectural integration, and control mechanisms are discussed in detail. Representative 6G use cases are examined, including vehicular ultra-reliable low-latency communication (URLLC), THz backhaul in cell-free massive multiple-input multiple-output (CF-mMIMO) networks, satellite–terrestrial integration, and joint communication–sensing (JCAS). Architectural interactions with reconfigurable intelligent surfaces, holographic MIMO, and integrated sensing and communication platforms are outlined, along with their roles in multi-band operation, energy harvesting, and backhaul enhancement. Key implementation challenges are identified in channel modeling, hardware fabrication, control protocols, scalability, and standardization. The paper concludes by identifying potential research directions for advancing reconfigurable antenna technologies within the broader 6G ecosystem.

Mobile Ad Hoc Networks (MANETs) consist of decentralized wireless networks with dynamic topologies and frequent link failures which make achieving efficient routing extremely difficult. Unlike prior ML-based MANET routing approaches that rely on static heuristics or offline training, our method performs real-time GNN embedding updates and decentralized execution to adapt immediately to topology changes. The paper introduces a new routing protocol for MANETs that combines Multi-Agent Deep Reinforcement Learning (MADRL) with Graph Neural Networks (GNNs) to improve routing decision-making processes. Network nodes function independently as autonomous agents which apply GNN-based state representations for capturing both local and wide network topology data. Nodes use a centralized training and decentralized execution model to collectively optimize their routing strategies through real-time network condition assessment. The protocol underwent evaluation through comprehensive NS-3 simulations within different mobility settings together with varying network densities and traffic patterns. The proposed approach shows superior performance compared to standard routing protocols like AODV and DSR in essential metrics such as packet delivery ratio (90.1%–97.8%), end-to-end delay (42ms–80ms), as well as routing overhead (12–24 packets/s). Merging MADRL with GNNs gives MANETs the ability to learn and adapt dynamically while providing a scalable and efficient solution for real-world applications such as disaster response and military communication environments.

Cloud computing is a demanding business platform for services related to the field of IT. The goal of cloud customers is to access resources at a sustainable price, while the goal of cloud suppliers is to maximize their services utilization. Previously, the customers would bid for every single resource type, which was a limitation of cloud resources allocation. To solve these issues, researchers have focused on a combinatorial auction in which the resources are offered by the providers in bundles so that the user bids for their required bundle. Still, in this allocation mechanism, some drawbacks need to be tackled, such as due to the lower average bid price the users are dropped from the auction process. To solve this problem, we proposed a “Negotiation based Combinatorial Double Auction Mechanism for Resource Allocation (N-CDARA) in cloud computing”. The proposed method negotiates with dropped users. Lower average bid price users are asked by our proposed mechanism to increase their bids, as by the quoted bids they will be dropped by the auctioneer. Most of the users that are close to winning accept the proposal and increase their bid prices. The proposed mechanism is implemented in a CloudSim simulation toolkit. Results are compared with the latest model and performance study shows that in our proposed scheme more users win and get their requested services and the utilization of offered services is increased up to 18.4% than the existing schemes.

Cloud computing liberates enterprises and organizations from expensive data centers and complex IT infrastructures by offering the on-demand availability of vast storage and computing power over the internet. Among the many service models in practice, the public cloud for its operation cost saving, flexibility, and better customer support popularity in individuals and organizations. Nonetheless, this shift in the trusted domain from the concerned users to the third-party service providers pops up many privacy and security concerns. These concerns hindrance the wide adaptation for many of its potential applications. Furthermore, classical encryption techniques render the encrypted data useless for many of its valuable operations. The combined concept of attribute-based encryption (ABE) and searchable encryption (SE), commonly known as attribute-based keyword searching (ABKS), emerges as a promising technology for these concerns. However, most of the contemporary ABE-based keyword searching schemes incorporate costly pairing and computationally heavy secret sharing mechanisms for its realization. Our proposed scheme avoids the expensive bilinear pairing operation during the searching operation and costly Lagrange interpolation for secret reconstruction. Besides, our proposed scheme enables the updation of access control policy without entirely re-encrypting the ciphertext. The security of our scheme in the selective-set model is proved under the Decisional Bilinear Diffie-Hellmen (DBDH) assumption and collision-free. Finally, the experimental results and performance evaluation demonstrate its communication and overall efficiency.