Generalized Frequency Division Multiplexing (GFDM) is considered a strong candidate to replace Orthogonal Frequency Division Multiplexing (OFDM) in 5G MIMO networks because of its enhanced spectral utilization and design flexibility. Despite these advantages, GFDM faces the drawback of producing a relatively high Peak-to-Average Power Ratio (PAPR), which limits the efficiency of power amplifiers. To address this issue, the Partial Transmit Sequence (PTS) method is often employed for PAPR reduction. Nevertheless, the effectiveness of PTS is hindered by the intensive computational effort required for searching multiple phase factors. To overcome this challenge, we propose a method that integrates the Enhanced Squirrel Search Algorithm (ESSA) with an adaptive parameter control mechanism and a Grey Wolf Optimizer (GWO), enabling a dynamic balance between exploration and exploitation during phase factor selection. This improvement reduces the computational overhead, accelerates the convergence, and enhances the robustness of the phase sequence optimization. Simulation results show that the Hybrid PTS-ESSA-GWO-RPSM model achieves superior PAPR reduction compared to conventional ESSA-based approaches, while also providing better BER and SNR performance under varying channel conditions. The proposed method therefore offers an efficient trade-off between complexity and PAPR reduction, making it suitable for practical deployment in MIMO-GFDM-based 5G systems. The proposed scheme is evaluated against related methods by analyzing key performance indicators, including Complementary Cumulative Distribution Function (CCDF), Bit Error Rate (BER), Peak-to-Average Power Ratio (PAPR), and Signal-to-Noise Ratio (SNR).

Abstract The escalating threat of easily transmitted diseases poses a huge challenge to government institutions and health systems worldwide. Advancements in information and communication technology offer a promising approach to effectively controlling infectious diseases. This article introduces a comprehensive framework for predicting and preventing zoonotic virus infections by leveraging the capabilities of artificial intelligence and the Internet of Things. The proposed framework employs IoT‐enabled smart devices for data acquisition and applies a fog‐enabled model for user authentication at the fog layer. Further, the user classification is performed using the proposed ensemble model, with cloud computing enabling efficient information analysis and sharing. The novel aspect of the proposed system involves utilizing the temporal graph matrix method to illustrate dependencies among users infected with the zoonotic flu and provide a nuanced understanding of user interactions. The implemented system demonstrates a classification accuracy of around 91% for around 5000 instances and reliability of around 93%. The presented framework not only aids uninfected citizens in avoiding regional exposure but also empowers government agencies to address the problem more effectively. Moreover, temporal mining results also reveal the efficacy of the proposed system in dealing with zoonotic cases.

Summary Internet of Things (IoT) based sensor networks have been established as a pillar in intelligent communication systems for efficiently handling roadside congestion and accidents. These IoT networks sense, collect, and process data on a real‐time basis. However, IoT based sensor network clustering has various energy constraints such as inefficient routing due to long‐haul transmission, hot spot problem, network overhead, and unstable network whenever deployed along with the roadside that affect their architecture. In such networks, clustering techniques play a crucial role in extending the lifespan and optimizing the routes by integrating sensor devices through clusters. Therefore, a meta‐heuristic algorithm for clustering in IoT sensor networks for an intelligent transportation system is proposed. In this work, the seagull optimization algorithm is applied for clustering by considering residual and average energy, node spacing, and distance fitness parameters. Moreover, this work also considers the dynamic communication range of the cluster heads for increasing the stability period and lifetime of the proposed networks. The experiment results demonstrate that the proposed Seagull optimization algorithm for clustering in IoT networks (SOAC‐IoTNs) and Seagull optimization algorithm for clustering in IoT networks with dynamic communication range (SOAC‐IoTNs‐DR) achieve a significant increase in the stability period and network lifetime, with percentage increments of 55.68% and 71.47%, and 10.03% and 88.66% respectively, compared to the existing optimized genetic algorithm for cluster head selection with single static sink (OptiGACHS‐StSS).

Abstract Microservices is a structural approach, where multiple small set of services are composed and processed independently with lightweight communication mechanism. To accomplish the end‐user demand in minimum delay and cost without violating the service level agreement (SLA) constraints and overhead is a challenging issue in cloud computing. In addition, existing framework tries to deploy the microservice over the best computing resource for latency‐sensitive applications, but long boot‐time, and low resource utilization still remains a challenging task. To find the solution for aforementioned issues, we propose a Quality of Service (QoS) aware resource allocation model based on a Fine‐tuned Sunflower Whale Optimization Algorithm (FSWOA) that find the best resources for microservice deployment and fulfill the objectives of users as well as service provider. The proposed technique deploys the container‐based services over the physical machine based upon the capacity, to execute the micro services by utilizing the CPU and memory maximally. The proposed work aims is to distribute the workload in efficient manner and avoid the wastage of resources that leads to optimize the QoS parameters. The experimental results conducted in simulation environment demonstrates that proposed approach perform superior over baseline approaches and reduces the time, memory consumption, CPU consumption, and service cost up to 4.26%, 11.29%, 17.07% and 24.22% compared to SFWAO, GA, PSO and ACO.

Yellow fever is a vigorous, phlebotomic, vector-borne disease that poses a significant public health threat in regions with high mosquito density and inadequate vaccination coverage. The disease's toxic phase is lethal, making prompt identification and control measures crucial. The emergence of the latest technologies and data analytics techniques, such as edge-cloud computing, data analytics, and machine learning/deep learning, has played a pivotal role in revolutionizing remote healthcare services. Henceforth, applying the abovementioned technologies leads to improvements in the response time, service quality, and location awareness of healthcare systems. Relative to this context, we propose an intelligent fuzzy-centric fog–cloud-assisted healthcare framework to identify and control yellow fever epidemics. Initially, at the fog layer, singular value decomposition is used for data dimensionality reduction analysis and the Fuzzy-C mean clustering (FCM) algorithm is leveraged to get rigorous results. Moreover, for better results and to focus on time-series patterns, the deep interval type 2 fuzzy Bi-LSTM model is proposed at the cloud layer to generate a yellow fever severity index and visualize each yellow fever region based on self-organized maps. In addition, we propose an alert generation mechanism to facilitate real-time decision-making. Finally, results show that the proposed system yields significant efficacy, compared with other state-of-the-art methodologies.

The rapid advancement of mobile edge computing (MEC) has revolutionized the distributed computing landscape. With the help of MEC, the traditional centralized cloud computing architecture can be extended to the edge of networks, enabling real-time processing of resources and time-sensitive applications. Nevertheless, the problem of efficiently assigning the services to the computing resources is a challenging and prevalent issue due to the dynamic and distributed nature of the edge network's architecture. Thus, we require intelligent real-time decision-making and effective optimization algorithms to allocate resources, such as network bandwidth, memory, and CPU. This paper proposes an MEC architecture to allocate the resources in the network to optimize the quality of services (QoS). In this regard, the resource allocation problem is formulated as a bi-objective optimization problem, including minimizing cost and energy with quality and deadline constraints. A hybrid cascading-based meta-heuristic called GA-PSO is embedded with the proposed MEC architecture to achieve these objectives. Finally, it is compared with three existing approaches to establish its efficacy. The experimental results report statistically better cost and energy in all the considered instances, making it practical and validating its effectiveness.

Summary The cloud services demand has increased exponentially in the last decade due to its plethora of services. It becomes a significant platform to compute large and diverse applications over the internet. On the contrary, on‐demand resource allocation to a variety of applications becomes a serious issue due to dynamic workload conditions and uncertainty in the cloud environment. Several existing state of art techniques often fails to allocate the optimal resources to forthcoming demands, leading to an imbalance workload over cloud platform, degrading the performance. This article introduces a secure and self‐adaptive resource allocation framework that addressed the mentioned issues and allocates the most suitable resources to users' applications while ensuring the deadline constraints. Further, the proposed framework is integrated with a metaheuristic algorithm named enhanced spider monkey optimization algorithm that is based on the intelligent foraging behavior of spider monkeys. The proposed algorithm finds an optimal resource for the user's application using the fission‐fusion approach and improves multiple influential parameters like time, cost, degree of load balancing, energy consumption, task rejection ratio and so on. The experimental CloudSim based results verified that the proposed framework performs superior to state of art approaches like PSO, GSA, ABC, and IMMLB.

The Internet of Things (IoT) has revolutionized the industrial field with numerous facilities and advancements. The industrial IoT system demands delay-aware workload execution with the aid of a fog computing platform, and precise resource allocation is required in fog nodes (FNs) to execute the fluctuating industrial IoT workloads with minimal cost and delay. In view of the issue mentioned above, we introduce an autonomic workload prediction and resource allocation framework that efficiently allocates resources among FNs. In the proposed framework, the workloads are predicted in the analysis phase with the guidance of the deep autoencoder (DAE) model, and the FNs are scaled based on the demand of Industrial IoT workloads. The crow search algorithm (CSA) is integrated with the framework for optimal FN selection to improve cost and delay objectives. The proposed scheme is evaluated and compared with the existing optimization models in terms of execution cost, request rejection ratio, throughput, and response time. The simulation results establish that the proposed scheme outperformed other optimization models. The method provided a suitable solution for the optimal FN placement problems in efficiently executing dynamic industrial IoT workloads.

Abstract With the advent of the Internet of Things (IoT) paradigm, the cloud model is unable to offer satisfactory services for latency‐sensitive and real‐time applications due to high latency and scalability issues. Hence, an emerging computing paradigm named as fog/edge computing was evolved, to offer services close to the data source and optimize the quality of services (QoS) parameters such as latency, scalability, reliability, energy, privacy, and security of data. This article presents the evolution in the computing paradigm from the client‐server model to edge computing along with their objectives and limitations. A state‐of‐the‐art review of Cloud Computing and Cloud of Things (CoT) is presented that addressed the techniques, constraints, limitations, and research challenges. Further, we have discussed the role and mechanism of fog/edge computing and Fog of Things (FoT), along with necessitating amalgamation with CoT. We reviewed the several architecture, features, applications, and existing research challenges of fog/edge computing. The comprehensive survey of these computing paradigms offers the depth knowledge about the various aspects, trends, motivation, vision, and integrated architectures. In the end, experimental tools and future research directions are discussed with the hope that this study will work as a stepping‐stone in the field of emerging computing paradigms.

With Cloud computing becoming mainstream for the execution of various applications, the multi-objective scheduling algorithms for providing the most suitable services to users have gained much attention. As provisioning Cloud services that satisfy end-users quality of service (QoS) requirements is complex and challenging, scheduling algorithms for cloud computing tend to focus on optimizing the execution cost or the execution time within user-defined deadline constraints. This paper addresses the problem of efficiently allocating Cloud services among competing jobs to achieve multiple end-users QoS. We design and develop a framework called Autonomic Resource Provisioning and Scheduling (ARPS) framework. ARPS framework has the decision-making capability to schedule the jobs at the best resources within the deadline and optimizes both the execution time and the cost simultaneously. The ARPS framework is also integrated with the spider monkey optimization (SMO) algorithm based scheduling mechanism. Our proposed mechanism is intended to solve a multi-objective optimization problem, including minimizing processing time, cost, and energy consumption. We study the effectiveness of the proposed scheduling mechanism through extensive simulation analysis using Cloudsim To assess the relative performance of our method, we compare it against four existing mechanisms. Experimental results show that the proposed mechanism outperforms its counterparts.