In this paper, a task scheduling problem for a cloud computing environment is formulated by using the M/M/n queuing model. A priority assignment algorithm is designed to employ a new data structure named the waiting time matrix to assign priority to individual tasks upon arrival. In addition to this, the waiting queue implements a unique concept based on the principle of the Fibonacci heap for extracting the task with the highest priority. This work introduces a parallel algorithm for task scheduling in which the priority assignment to task and building of heap is executed in parallel with respect to the non-preemptive and preemptive nature of tasks. The proposed work is illustrated in a step-by-step manner with an appropriate number of tasks. The performance of the proposed model is compared in terms of overall waiting time and CPU time against some existing techniques like BATS, IDEA, and BATS+BAR to determine the efficacy of our proposed algorithms. Additionally, three distinct scenarios have been considered to demonstrate the competency of the task scheduling method in handling tasks with different priorities. Furthermore, the task scheduling algorithm is also applied in a dynamic cloud computing environment.

Unmanned aerial vehicles (UAVs) are increasingly being deployed in crucial missions for the armed forces, law enforcement, industrial control monitoring, and other sectors. However, these hostile operating circumstances, along with the UAVs’ dependence on wireless protocols, pose substantial security threats, limiting their mainstream application. With network security being such a major issue for UAV networks, the machine learning-based intrusion detection system (IDS) has been determined to be an effective strategy for protecting them. Additionally, though the existing methods offer effective strategies for detecting and categorizing abnormalities in the system, they are limited by their inability to adjust to various attack patterns. The dataset used as well as the memory and computational requirement of existing models, poses new challenges. One of the main concerns pertains to the reduced computational and memory demands of these models. So, the work carried out in this paper addresses this challenge. A new dimensional reduction technique based on correlation coefficient, information gain, and principal component analysis (PCA) is introduced to reduce the dimensionality of the UAV Attack Dataset. A novel intrusion detection system based on an artificial neural network (ANN) and genetic algorithm (GA) is then proposed. The genetic algorithm is used to generate the optimal weights of the artificial neural network. A comparison is made between the proposed model and the backpropagation network and its variant in terms of its convergence and prediction accuracy. Furthermore, the performance of the proposed model is compared with that of other classifiers. This comparison reveals that the proposed model is time efficient with an increased prediction accuracy of at least 6% more than other classifiers.

The security of Internet of Medical Things (IoMT) devices is crucial for ensuring the integrity and reliability of patients’ medical data. These devices, operating over TCP and ICMP protocols, are highly susceptible to cyberattacks. While machine learning models can detect these attacks with acceptable accuracy, their operational mechanisms remain unclear, leaving the decision-making process of the models undefined. Moreover, the accuracy and training time of machine learning models is more questionable when the datasets has large number of sparse features and class imbalances. This study introduces an interpretable feature selection technique designed to enhance intrusion detection in IoMT by reducing redundant features and improving model efficiency. Random Forest-based explainable AI model provides transparency in attack classification and better decision-making. The simulated results employing the CICIoMT2024 dataset demonstrate that the proposed method significantly improves detection performance, with the Random Forest model achieving 99% accuracy, outperforming XGBoost (98%), Decision Tree (97%), and Support Vector (98%), while ensuring explainability through SHAP-based feature analysis. Thus, the simulation outcomes reveal the key contributing factors for various cyberattacks on IoMT, facilitating enhanced security measures and real-time monitoring. The proposed approach boosts detection accuracy and interpretability, making it highly suitable for real-world IoMT security applications.

The reliability and cost are two important performance measures of an interconnection network. Both these aspects need to be attended at the layout design stage for an appropriate trade-off between them. This paper introduces a new approach for layout optimization of interconnection networks using dynamic programming. Our principal objective here is to optimize the network layout so as to maximize the network reliability of a given network within some predefined cost constraint. Since the above problem is NP-hard, a new technique based on dynamic programming is proposed to locate the optimal positions of the nodes and links in the network. The proposed method is illustrated through an example. We also present experimental results for a wide range of interconnection networks. The proposed method is found to be efficient and is applicable for large sized interconnection networks

With the exponential growth of Internet of Things (IoT) devices, edge computing has emerged as a vital paradigm for localized data processing, low-latency communication, and efficient resource utilization. However, routing in IoT-based edge networks remains challenging due to dynamic topologies, constrained energy resources, and growing security threats. To tackle these challenges, this article proposes QLB-IoT, a novel framework that integrates Q-learning-based intelligent routing with blockchain-assisted trust management to optimize energy consumption while ensuring secure and adaptive data transmission. The proposed method enables IoT devices to autonomously learn energy-efficient routing paths based on real-time network parameters such as residual energy and link distance. The Q-learning mechanism minimizes route flapping and promotes adaptive decision-making. Simultaneously, blockchain and digital signatures establish a decentralized trust layer that authenticates devices and prevents attacks like blackhole and Sybil. Compared with open shortest path first (OSPF), Q-Routing, deep reinforcement learning-based control framework for traffic engineering (DRL-TE), and intelligent edge network routing (ENIR), exhibits an extended network lifetime to $\approx 1300$ rounds ( $2.4\times $ OSPF and 8% beyond ENIR), increases throughput by 18%–30%, and reduces average latency by half, while minimizing packet loss by $\approx 40$ % and increasing the likelihood of quality of service (QoS) compliance by up to 10%. These improvements stem from a real-time exploration–exploitation mechanism that dynamically reroutes traffic away from energy-depleted nodes, and from the immutable ledger that prevents malicious route manipulation without centralized oversight. By combining adaptive, energy-aware routing with verifiable trust, QLB-IoT delivers a scalable and resilient solution for mission-critical IoT-edge deployments.