Edge and Fog computing have emerged as an effective paradigm to meet the increasing demand for resources of the Internet of Things. However, existing task scheduling models exhibit reduced effectiveness due to high latency and limited resource allocation capability. To overcome these limitations, this paper proposes a novel Multi-Agent Distributed Reinforcement-based Crossover Coati Optimization Algorithm to perform effective task scheduling. The proposed model integrates the adaptive learning capability of deep reinforcement learning with the exploration and exploitation strengths of the Coati Optimization Algorithm. A crossover mechanism is incorporated with the Coati optimization process to enhance the search capability and solution diversity by combining multiple candidate solutions, thereby reducing premature convergence to local optima. The proposed model adopts a multi-agent architecture in which individual agents learn scheduling policies from local environments and optimize resource allocation, enabling adaptive and real-time task scheduling in dynamic environments. The proposed model enhances task scheduling performance and provides an efficient resource allocation strategy for large-scale edge and fog computing systems. In addition to this, it improves system efficiency by maintaining balanced workload distribution and reducing processing delay. Experimental results demonstrate that the proposed model reduces the response time to 2.3 s under dynamic conditions. These results confirm the effectiveness of the proposed approach in handling complex and dynamic scheduling scenarios.

The term “green computing” describes the efficient use of resources in computing and IT/IS infrastructure. This study suggests a unique method for dispersed fog data centres' work scheduling and resource allocation based on digital waste management. Here, the bandwidth differential preemption evolution moving average method (BDPEMA) is used to control the network's digital waste while allocating resources. Reinforcement adversarial hierarchical group multi-objective cuckoo optimisation (RAHMCO) is used to schedule network tasks. In terms of resource sharing rate, energy efficiency, reaction time, quality of service, and makespan, experimental study is conducted. The proposed approaches have been evaluated in a simulated cloud environment. The proposed method outperformed the current rules when QoS features were considered. The proposed technique attained QoS of 66%, energy efficiency of 96%, resource sharing of 88%, response time of 45%, and makespan of 61%.

Cloud computing is a dynamic technology that requires efficient resource allocation strategies, and the task scheduling is optimized in heterogeneous cloud environments. The virtualization technology is largely responsible for the popularity of the cloud. During peak load periods, due to resource scarcity, some tasks are migrated to other data centers to achieve balance. To tackle this challenge, a novel method is proposed such as the genetic water evaporation optimization (GWEO) algorithm to perform an effective resource allocation in diverse cloud infrastructures. In order to reduce task execution time, the GWEO is associated with genetic algorithms and water evaporation optimization algorithm that maximize resource allocation. The scalability and adaptability of novel GWEO were explored through experiments involving larger-scale cloud deployments, showcasing its efficacy in handling complex scheduling tasks in real-world cloud environments. The promising results indicate its potential to significantly enhance performance and scalability in diverse cloud computing environments, addressing critical challenges associated with resource optimization and task scheduling.

The rapid proliferation of the Internet of Things (IoT) has led to a significant surge in interconnected devices across diverse domains, ranging from smart homes and healthcare systems to industrial automation and smart cities. However, this exponential growth has exposed IoT devices to a plethora of cyber threats, including illegal access, data breaches, and malicious attacks, primarily due to their inherent limitations in terms of network capabilities, computational power, and memory. To combat these security challenges and ensure the safety of IoT ecosystems, the development of effective intrusion detection systems has become imperative. Such systems play a crucial role in detecting and preventing unauthorized activities within IoT networks. In this context, this article presents a pioneering approach called DeepLG SecNet, which leverages a combination of deep learning techniques, including Long Short-Term Memory (LSTM), gated Secure Network (SecNet), and Crossover Chaos Game Optimization (CCGO), to fortify IoT devices against unauthorized access and potential threats. To validate the efficacy of the proposed DeepLG SecNet method, various samples were collected from the BoT-IoT dataset and the NSL-KDD dataset. Performance evaluation was conducted using essential metrics to assess the model's detection capabilities in an IoT intrusion context. The experimental analysis yielded promising results, highlighting the effectiveness of the DeepLG SecNet method in intrusion detection for IoT environments. Specifically, DeepLG SecNet outperformed existing methods, demonstrating higher accuracy, precision, recall, and F1 score in safeguarding IoT systems from potential security breaches.

The intelligent internet of things (IoT) will become more valuable with introduction of 6G communication network, which is a sixth-sense next-generation communication network. It is certain that these two technologies will merge, opening the door for 6G wireless networks and the intelligent IoT. Ambient backscatter networks with machine learning (ML) capabilities have several applications, such as smart farming, industrial automation, and healthcare networks. Humans must provide for their own healthcare in order to thrive. A wide spectrum of disorders affecting heart as well as blood arteries are together referred to as cardiovascular disease. Novel optoelectronic materials have potential to revolutionise ongoing green shift by increasing efficiency of photovoltaic (PV) devices as well as decreasing energy consumption of devices such as LEDs as well as sensors. Both organic semiconductors, perovskites are the leading potential materials for these applications. This study suggests a unique method for detecting cardiovascular illness based on an investigation of blood artery blockages using machine learning algorithms with optoelectronic sensor analysis. Here, noise is removed from the input cardiac pictures, and the images are smoothed and normalised. The analysis of the blood artery obstruction in the right and left ventricles follows the processing of this picture. Utilising quantum dot-based Hopfield neural networks and convolutional ResNet gradient learning, the blood vessels are studied for the purpose of detecting cardiovascular illness. Different cardiac pictures are subjected to experimental investigation in terms of training accuracy, ROC, Precision, and recall. Proposed technique attained training accuracy of 98

The distributed denial of service (DDoS) assault was a kind of intrusion in the cloud computing environment that severely affects the end user by injecting illegitimate packets. To obtain performance, a hybrid improved wolf optimizer with asymmetric key Goldwasser cryptography (IWO-AKGC) algorithm was proposed based on combining the exploitation ability of security and exploration capability of machine learning. In addition to the selection of parameters, a proposed hybrid IWO-AKGC technique is used for weighting and bias coefficients in neural network models. This has led to an immediate improvement in communication security for the delivery of different types of data services via clouds, thanks to the proposed IWO-AKGC method. The recommended hybrid optimizer successfully addresses the drawbacks of conventional methods, such as local stagnation problems, delayed convergence problems, and local and global optimal trapping problems. Thus, secured data communication is obtained for cloud service provisioning. The proposed model proved to be a better model for DDoS intrusion detection.

Mobile Ad hoc Networks (MANETs) is a self-organizing networks without having a fixed infrastructure for making them susceptible to security threats. Intrusion Detection Systems (IDS) promotes security in MANETs by identifying malicious activities. Leader election is a fundamental aspect of IDS deployment, impacting resource allocation and system efficiency. This article presents a novel approach, the Crossover Boosted Grey Wolf Optimizer (CBGWO), for leader election and resource allocation in MANET-based IDS. The proposed CBGWO algorithm integrates the Grey Wolf Optimizer (GWO) with innovative crossover operators that have an ability to enhance the capabilities of exploration and exploitation in the optimization process. The leader election problem is solved through applying multi-objective optimization by considering energy consumption, reputation, and communication overhead. Objective functions are defined to maximize energy efficiency while maintaining a balanced distribution of leadership roles. Extensive simulations are conducted, varying network densities and the percentage of selfish nodes. Results demonstrate the effectiveness of the CBGWO-based model in balancing energy consumption, prolonging network lifespan, and enhancing intrusion detection by comparing different state-of-the-art models such as PCA-FELM, CTAA-MPSO, FLS-RE, LEACH, DCAIDS, WOA-GA, and VOELA. The proposed model achieved an energy consumption of 4.31 J, network lifetime of 560.482 ms, and average intrusion detection latency of 0.12 s, respectively. The proposed model outperforms than existing random and connectivity-based leader election methods that is evaluated by taking main consideration of energy efficiency and network survivability. This research contributes to the field by introducing a robust algorithm for leader election in MANET-based IDS, addressing challenges posed by network dynamics and resource constraints. The CBGWO-based approach showcases its potential to achieve effective leader election and efficient resource allocation, thereby enhancing the security and sustainability of MANETs.

In modern era, the internet of things (IoT) has transfigured the medical industry, empowering the unified collection and exchange of patient data. However, this increased connectivity has raised significant concerns regarding the safety and confidentiality of electronic healthcare records (EHRs). This systematic review explores the security and privacy issues surrounding IoT-based EHRs and presents an in-depth analysis of how blockchain technology can be employed as an effective countermeasure to address these challenges. The chapter provides an overview of IoT in healthcare, highlights the vulnerabilities of EHR systems, and discusses the potential threats to patient data. It then delves into the principles of blockchain technology and its applicability to enhance EHR security and privacy. Various blockchain-based solutions and implementations are reviewed, and their advantages and limitations are discussed. This systematic review investigates the incorporation of IoT with EHRs in healthcare and contributes towards better understanding of the synergy between IoT, EHRs, and blockchain in the healthcare industry and assists as a valuable supplement for academics, practitioners, and policymakers. Additionally, the survey presents case studies and real-world examples of successful blockchain integration into healthcare systems. Finally, this chapter concludes by outlining the future prospects and challenges of utilizing blockchain technology for securing IoT-based EHRs and emphasizes the importance of a holistic approach to safeguarding patient data.

In a time marked by an ever-increasing number of sensitive data and mounting worries about breaches of privacy, the area of cryptography has emerged as the frontrunner in the fight to keep personal information secure. “Next-Gen Cryptography: The Role of Machine Learning in Privacy Preservation for Sensitive Data” investigates the revolutionary junction of two fields that are on the cutting edge: cryptography and machine learning. This chapter elucidates how the combination of these disciplines promises to reshape the landscape of data security, particularly with regard to the protection of sensitive information.

The cloud computing has developed a vital service in information technology (IT). It assures resource pooling as well as offers services on-demand around the network. The efficient task scheduling and the balanced task distribution become the major challenging problem in the cloud computing system because of the active heterogeneous nature of resources as well as the tasks. The resources are unstable in nature whenever a large number of resources are requested for completing the tasks. The main part of this issue is to design the effective intelligent searching arrangement for scheduling the tasks in suitable virtual machines and how VM schedules the task in the efficient way. In this article, an effective method using MAP reduces structure and HBSFD for the effective task scheduling in the provided cloud. First, from the client's task, task features are extracted. Later, these extracted features are chosen by the feature choice using the adjusted rand index and the standard deviation ratio (FSASR) method. Then, the larger tasks are divided to smaller subtasks using the map-reduce structure. Finally, the tasks are effectively scheduled with the help of the hybrid bird swarm-based flow directional algorithm (HBSFD). The experimental evaluations are conducted on the platform of cloudsim, and the experimental outcomes demonstrates that the proposed HBSFD technique performs better than other state-of-art approaches with respect to measures such as average turnaround time and processing time.

An enriched computational platform has been unfolded with the introduction of cloud technology offering ensemble services to users that includes storage, database and processing power.More recently, the cloud technology has been upgraded to a federated environment that offers even more features where various service providers could interconnect for providing an integrated service in a transparent way to cloud users.Applications that demand enormous computing resources like for instance bioinformatics workflow applications could very well make use of the abundant cloud resources for effective execution.Fine tuning the task scheduling activity in cloud could further boost the overall cloud performance.In this paper, we had designed an optimal and ideal task scheduling algorithm that primarily focuses on reducing the cost and makespan QoS parameters, eventually leading to enhanced cloud performance.The proposed algorithm, which is a meta-heuristic enhanced hybrid version named, grey wolf optimizer cuckoo (GWOC) is formally designed from the existing grey wolf optimizer and cuckoo search algorithms.results obtained clearly justify the goal accomplishment of the proposed GWOC algorithm and its swiftness in achieving convergence, thereby clearly outperforming existing contemporaries like gravitational search algorithm (GSA), whale optimization algorithm (WOA) algorithm and grey wolf optimizer (GWO) algorithm.The proposed GWOC technique had produced an improvement of 2.11%, 3.5% and 5.17% for makespan and had reduced the cost to the tune of 7.71%, 11.3%, and 15.4% when compared with gravitational search algorithm (GSA), whale optimization algorithm (WOA) algorithm and grey wolf optimizer (GWO) algorithm respectively when used with 100 VMs.Detailed results have been presented in section 5.

Globally, the research community has been extremely concerned about the recent sharp rise in the proportion of elderly individuals. Studies are being done actively to make use of the advantages of ICT that allow them to live autonomously and encourage overall wellness. Smart homes are frequently used to help this population. Nevertheless, there is a dearth of pertinent empirical studies that attempt to quantify and analyze these individuals' intentions to use that kind of service. In this research, we suggest an autonomous smart home application that is energy-efficient and combines big data and cloud computation to supervise seniors. A smart multimedia-based supervisor that enables an elderly individual to observe various energy-efficient mechanisms, regulate smart home devices via gestures, receive alerts concerning device status updates, and distribute multimedia information with a group of interest is developed. An autonomous switch on/off management device employing speech or gestures has been created to preserve energy. Speech-based keywords are recognized and employed as control signals. The promising prospect of using this architecture on a massive scale is demonstrated by empirical outcomes.

The Cloud environment had been the go-to for many users recently. Once request from users get submitted, cloud resources are put into action to fulfill the request. Scheduling is the primary task in cloud that needs to be up-to the mark for completing the requests swiftly. Multiple dynamic requests are submitted simultaneously by cloud users that necessitates precise and prompt scheduling in cloud. Scheduling in cloud may be hampered by various constraints, take for example the various QoS parameters that needs to be upheld. Though many researchers had proposed solutions for scheduling in cloud, improvisations can still be made by combining several QoS parameters that help attain optimized scheduling in cloud to boost the overall cloud performance. In this paper, we had proposed a Binary Grey Wolf Optimization (BGWO) algorithm to optimize the scheduling activity in cloud computing environment. The BGWO is a multi-heuristic algorithm where tasks are scheduled based on a fitness function, explicitly designed for achieving optimization goal. The fitness function that had been designed comprises of three prime parameters namely, the degree of imbalance (DoI), energy consumption and makespan. The performance efficiency of the proposed BGWO had been ascertained by comparing it with Oppositional based Grey Wolf Optimization algorithm (OGWO) and Mean Grey Wolf Optimization algorithm (Mean GWO) with respect to imbalance, energy and makespan parameters. The proposed algorithm had produced a cumulative improvement of 10.13% and 17.4% for makespan, 30.18% and 41.96% for DoI, 8.94% and 14.95% for energy consumption parameters. Detailed comparative results obtained had been described in the Results part of this research article.

Task scheduling is an important issue in cloud computing when it comes to achieving multiple goals and satisfying different user needs. The increasing demand and users urge the necessity to minimize the task completion time and enhance the load balancing capacity. To achieve this goal, this article proposes a Hybrid firebug and Tunicate Optimization (HFTO) algorithm. Based on the previous scheduling information, the HFTO classifier classifies the task and creates different variants of Virtual Machine (VM). This step helps to minimize the time taken for VM creation. The proposed HFTO task scheduling framework aims at optimizing different Quality of Service (QoS) parameters such as fault tolerance, response time, efficiency, and makespan. The optimization algorithm helps to expand the search space of the solutions and frames an optimal task scheduling strategy for the virtual machines. The HFTO optimization method has several advantages, including enhanced search capability and faster convergence. The HFTO algorithm improves the fault tolerance capability by allocating the tasks to appropriate resources based on the resource load peak. The lightweight tasks can be allocated to the resources with high CPU utilization and the computation-intensive tasks can be allocated to the resources with low CPU utilization. The response time and execution time are improved by task pre-emption. Hence the time complexity and computational complexity can be improved by the HFTO algorithm even with limited resource capability. The experiments are conducted using the CloudSim experimental platform and the results are compared to the state-of-art techniques. The performance of the proposed methodology is evaluated in terms of different performance metrics namely makespan, load balancing, and average execution time. The results show that, when compared to existing techniques, the proposed methodology provides higher load balancing efficiency and improved cloud task scheduling performance.

Cloud computing has been booming technology in recent years. It is used to share resources over the internet. Though cloud has many advantages and is used worldwide. It also has some disadvantages and issues. The major problem in cloud computing is scheduling and resource allocation. Allocating resources and tasks is one of the highly critical challenges. There are no proper methods or techniques to improve task scheduling and resource allocation. Previous methods used Virtual machine (VM) instances for scheduling. The major drawback of using Virtual machine instances is that it takes a lot of startup time and consumes all the resources to perform the task. In this paper, we have proposed a solution with fuzzy C-means clustering hybrid algorithms of using Black widow optimization for task scheduling and fish swarm optimization for efficient resource allocation to reduce cost, energy, resource utilization.

Cloud computing has garnered unprecedented growth in recent years in the field of Information Technology. It has emerged as a high-performance computing option owing to its infrastructure that comprises of heterogeneous collection of autonomous computers and adaptable network architecture. The tasks that are scheduled in an optimized manner for their execution could be classified under NP-hard problems. Though meta-heuristic scheduling algorithms emerge as scheduling options, they need to be much more consistent while dealing with the dynamic set up of the cloud environment. In this paper, we had proposed a multi-objective meta-heuristic scheduling algorithm namely Quasi Oppositional Genetic Spotted Hyena Optimization (QOGSHO) algorithm that globally optimizes the makespan, resource consumption and SLA violation QoS parameters, thereby improving the performance. The algorithm proposed is an amalgamated product of meta-heuristic algorithms like Quasi Oppositional Based Learning (QOBL), Spotted Hyena Optimization (SHO), and Genetic Algorithm (GA). The performance efficiency of the proposed QOGSHO algorithm had been compared with various scheduling algorithms using uniform datasets by varying the data instance sizes in a simulated cloud environment. The obtained results clearly justify the task scheduling efficiency of the proposed algorithm with respect to the QoS parameters namely makespan, resource utilization and SLA violation.

Cloud computing is the computing technology that offers dynamically scalable and flexible computing resources. Task scheduling in the cloud system is the major problem that needs to be tackled for enhancing the system performance and cloud customer satisfaction level. The task scheduling scheme directly affects the execution time as well as the execution cost of the system. To overcome the above-stated issue, the novel hybrid Whale optimization algorithm-based MBA algorithm is proposed for solving the multi-objective task scheduling problems in cloud computing environments. In the hybrid WOA based MBA algorithm, the multi-objective behavior decreases the makespan by maximizing the resource utilization. The output of the Random double adaptive whale optimization algorithm (RDWOA) is enhanced by utilizing the mutation operator of the Bees algorithm. The performance evaluation is conducted and compared with other algorithms using the platform of Cloudsim tool kit for various measures such as completion, time, and computational cost. The results are analyzed for the performance measures such as makespan, execution time, resource utilization and computational cost and the analysis proves that the proposed algorithm performs better than other algorithms such as IWC, MALO, BA-ABC and MGGS. The proposed HWOA based MBA algorithm converged faster than any other approach for large search spaces and makes it appropriate for large scheduling problems. The experimental results reveal that the HWOA based MBA algorithm effectively minimizes the task completion time and also execution time.

Mobile malware is malicious software designed specifically for targeting various mobile gadgets like tablets, smartphones, and so forth, in which any type of malicious code affecting the mobile devices without the knowledge of the user. The increasing number of users encourages the hacker for generating various malware applications. Therefore, in this paper, we utilized three vital phases namely the pre‐processing process, Feature extraction process as well as classification process in which the malicious data are detected. In the pre‐processing phase, an Androguard tool is used for decompiling and disassembling the android applications. The API call features are extracted in the feature extraction phase and in the classification phase, long short term memory based electro search optimization (LSTM‐ESO) is employed to detect the unknown mobile applications as benign or malicious. The malicious mobile detecting accuracy deals in requesting permission and exhibiting malicious code applications. In order to enhance the identification of various malware applications, this paper utilized frequency analysis and permissions of API calls. Finally, the experimental analysis is performed by evaluating the performance measures like accuracy, precision, recall, and F‐measure. From the evaluation outcome, it is observed that the classification accuracy obtained is 97.69%.

A cloud computing system typically comprises of a huge number of interconnected servers that are organized in a datacentre. Such servers dynamically cater to the on-demand requests put forward by the clients seeking solutions to their applications through an interface. The scheduling activity concerned with scientific applications is designated under the NP hard problem category since they make use of heterogeneous resources of dynamic capabilities. Recently cloud computing researchers had developed numerous meta-heuristic approaches for providing solutions to the challenges arising in the task scheduling activities. Scheduling of tasks poses a major concern in cloud computing environment. This decreases the efficiency of the system considerably, if not handled properly. Hence, an improvised task scheduling algorithm that enhances the performance of the cloud is needed. There are two factors that affect the cloud environment: service quality and energy usage. To increase the performance in above suggested factors (memory, makespan and energy efficiency), an efficient hybridized algorithm, obtained by integrating the Cuckoo Search Algorithm (CSA) and Whale Optimization Algorithm (WOA), called the CWOA had been proposed in this work. The performance of our proposed CWOA algorithm had been compared with Ant Colony Optimization, CSA and WOA and it was found to produce an improvement of 5.62%, 4.36% and 2.27% with respect to makespan, 16.36%, 19.19% and 13.13% with respect to memory utilization and 19.08%, 19.34% and 16.75% with respect to energy consumption parameters, respectively. Comprehensive results have been tabulated in the result section of this article.