Efficient load balancing stands out as a crucial challenge in multi-cloud environments, particularly for applications that demand ultra-reliable, low-latency communications (URLLC). This paper proposes a novel approach integrating Decision Functions with Normal Distributions (DFND) for precise probabilistic modeling of task-to-cloud compatibility. Multivariate normal distributions capture interdependencies between resource features such as CPU, memory, bandwidth, and latency, ensuring accurate resource compatibility evaluation. Additionally, the Tasmanian Devil Optimization (TDO) algorithm employs dynamic exploration and exploitation strategies inspired by natural behaviors, providing rigorous optimization to improve task assignment in dynamic, multi-cloud environments. It uses flexible methods to ensure the optimization process is both efficient and scalable. Simulation results using CloudSim demonstrate significant improvements over state-of-the-art methods in terms of makespan reduction, response time minimization, resource utilization, and cost efficiency. The proposed framework effectively supports latency-sensitive, large-scale applications in dynamic, heterogeneous multi-cloud environments.

Internet of Things (IoT) technology has facilitated the deployment of autonomous sensors in remote and challenging environments, enabling substantial advancements in environmental monitoring and data collection. IoT sensors continuously gather data, transmitting it to a central Base Station (BS) via designated Cluster Heads (CHs). However, data flow encounters frequent congestion at CH nodes, negatively impacting network performance and Quality of Service (QoS). This paper introduces a novel congestion control strategy tailored for Wireless Sensor Networks (WSNs) to balance energy efficiency and data reliability. The proposed approach follows an eight-step process, integrating Generative Adversarial Networks (GANs) for enhanced clustering and Ant Colony Optimization (ACO) for optimal CH selection and routing. GANs simulate realistic node clustering, achieving better load distribution and energy conservation across the network. ACO then selects CHs based on energy levels, distance, and network centrality, using pheromone-based routing to adaptively manage data flows. A congestion factor (CF) threshold is also incorporated to dynamically reroute traffic when congestion risks arise, preserving QoS. Simulation results show that this approach significantly improves QoS metrics, including latency, throughput, and reliability. Comparative evaluations reveal that our method outperforms existing frameworks, such as Fuzzy Structure and Genetic-Fuzzy (FSFG), Deep Reinforcement Learning Cache-Aware Congestion Control (DRL-CaCC), and Adaptive Cuckoo Search Rate Optimization (ACSRO).

The increase in Internet of Things (IoT) devices has introduced significant security challenges, primarily due to their inherent constraints in computational power, memory, and energy. This study provides a comparative performance analysis of selected modern cryptographic algorithms on a resource-constrained IoT platform, the Nordic Thingy:53. We evaluated a set of ciphers including the NIST lightweight standard ASCON, eSTREAM finalists Salsa20, Rabbit, Sosemanuk, HC-256, and the extended-nonce variant XChaCha20. Using a dual test-bench methodology, we measured energy consumption and performance under two distinct scenarios: a low-data-rate Bluetooth mesh network and a high-throughput bulk data transfer. The results reveal significant performance variations among the algorithms. In high-throughput tests, ciphers like XChaCha20, Salsa20, and ASCON32 demonstrated superior speed, while HC-256 proved impractically slow for large payloads. The Bluetooth mesh experiments quantified the direct relationship between network activity and power draw, underscoring the critical impact of cryptographic choice on battery life. These findings offer an empirical basis for selecting appropriate cryptographic solutions that balance security, energy efficiency, and performance requirements for real-world IoT applications.

Federated Learning (FL) paradigm has been very popular in the implementation of 5G and beyond communication systems as it provides necessary security for the users in terms of data. However, the FL paradigm is still vulnerable to model inversion attacks, which allow malicious attackers to reconstruct data by using the trained model gradients. Such attacks can be carried out using generative adversarial networks (GANs), generative models, or by backtracking the model gradients. A zero-trust mechanism involves securing access and interactions with model gradients under the principle of “never trust, always verify.” This proactive approach ensures that sensitive information, such as model gradients, is kept private, making it difficult for adversaries to infer the private details of the users. This paper proposes a zero-trust based Block Encryption LAyer (BELA) module that provides defense against the model inversion attacks in FL settings. The BELA module mimics the Batch normalization (BN) layer in the deep neural network architecture that considers the random sequence. The sequence and the parameters are private to each client, which helps in providing defense against the model inversion attacks. We also provide extensive theoretical analysis to show that the proposed module is integratable in a variety of deep neural network architectures. Our experimental analysis on four publicly available datasets and various network architectures show that the BELA module can increase the mean square error (MSE) up to 194% when a reconstruction attempt is performed by an adversary using existing state-of-the-art methods.

In recent years, advancements in the automotive industry have accelerated the development of connected and autonomous vehicles (CAVs)