The Internet of Things is a flexible, emerging technology and an innovative development of the environmental trend. It is a large and complex network of devices in which fog computing plays a growing role in order to handle the information flow of such large and complex networks. Influence of their activities on carbon emissions and energy costs in unlimited results. Dynamic and efficient load balancing technology can be used to improve overall performance and reduce energy consumption. Load can be transferred or shared between computer nodes through load balancing technology. Therefore, the design of energy-efficient load balancing solutions for edge and fog environments has become the main focus. In this research work, we have proposed Dynamic Energy Efficient Resource Allocation (DEER) strategy for balancing the load in fog computing environments. In the presented strategy, initially the user submits tasks for execution to the Tasks Manager. Resource Information Provider registers resources from Cloud Data Centres. The information about the tasks and resources are then submitted to the Resource Scheduler. The resource scheduler arranges the available resources in descending order as per their utilization. The resource engine after receiving the information of tasks and resources from the resource scheduler assigns tasks to the resources as per ordered list. During execution of tasks, the information about the status of the resources is also sent to the Resource Load Manager and Resource Power Manager. The Resource Power Manager manages the power consumption through the resource On/Off mechanism. After successful execution of tasks, the resource engine returns the result to the user. Simulation results reveal that the presented strategy is an efficient resource allocation scheme for balancing load in fog environments to minimize the energy consumption and computation cost by 8.67 % and 16.77 % as compared with existing DRAM scheme.

The data-centre is considered the heart of cloud computing. Recently, the growing demand for cloud computing services has caused a growing load on data centres. In terms of system behaviour and workload, patterns of cloud computing are very dynamic; and that might serve to imbalance the load among data centre resources. Eventually, some data-centre resources could come to be over-loaded/under-loaded, which leads to an increase in energy consumption in addition to decreased functioning and wastage of resources. Just considering energy-efficiency (that can be attained efficiently by consolidate the servers) may not be enough for real applications because it may cause problems such as unbalanced load for each Physical Machine (PM). Therefore, this paper surveys published load balancing algorithms that achieved by server consolidation via a meta-analysis. Load balancing with server consolidation enriches the exploitation of resource utilization and can enhance Quality of Service (QoS) metrics, since data-centres and their applications are increasing exponentially. This meta-study, reviews the literature on load balancing and server consolidation and presents a ready reference taxonomy on the most efficient algorithms that achieve load balancing and server consolidation. This work attempts to present a taxonomy with a new classification for load balancing and server consolidation, such as migration overhead, hardware threshold, network traffic, and reliability.

Cloud computing provides solutions to a large number of organizations in terms of hosting systems and services. The services provided by cloud computing are broadly used for business and scientific applications. Business applications are task oriented applications and structured into business workflows. Whereas, scientific applications are data oriented and compute intensive applications and structured into scientific workflows. Scientific workflows are managed through scientific workflows management and scheduling systems. Recently, a significant amount of research is carried out on management and scheduling of scientific workflow applications. This study presents a comprehensive review on scientific workflows management and scheduling in cloud computing. It provides an overview of existing surveys on scientific workflows management systems. It presents a taxonomy of scientific workflow applications and characteristics. It shows the working of existing scientific workflows management and scheduling techniques including resource scheduling, fault-tolerant scheduling and energy efficient scheduling. It provides discussion on various performance evaluation parameters along with definition and equation. It also provides discussion on various performance evaluation platforms used for evaluation of scientific workflows management and scheduling strategies. It finds evaluation platforms used for the evaluation of scientific workflows techniques based on various performance evaluation parameters. It also finds various design goals for presenting new scientific workflow management techniques. Finally, it explores the open research issues that require attention and high importance.

The smart grid environment, which emphasizes sustainability, dependability, and efficiency through smart components such as Intelligent Electronic Devices (IEDs), communication networks, and control systems, marks a revolutionary change in the way traditional power distribution is carried out. As smart grids grow and are integrated into energy distribution networks, these systems become more vulnerable to cybersecurity threats due to their increased connectivity, usage of IEDs, and reliance on digital communication channels. This study presents an edge computing-based, threat behavior-aware smart prioritization framework with binary and multidimensional classification and detection of cybersecurity intrusions through modified machine learning methods. The proposed framework has the potential to improve smart grid cybersecurity by offering a comprehensive defense against intrusion threats. The proposed framework enhances smart grid cybersecurity by utilizing a multi-criteria approach. It implements edge-computing technology for data storage and processing in smart grids. It applies machine-learning models for cybersecurity intrusion detection in IEDs and provides prevention by assigning priorities to the threats based on their behavior. In order to show the effectiveness of the proposed framework, we modified and implemented two machine-learning models, i.e., LGBM and One Class-SVM, as proposed models in the framework. For multidimensional classification and detection of cybersecurity intrusions in IEDs of smart grids, we used LGBM. Whereas, for binary classification and detection of cybersecurity intrusions, we used One Class-SVM. We simulated the detection and classification of cybersecurity intrusions in IEDs using a power system intrusion dataset. The results show that the LGBM model provides an accuracy of 93%, precision of 94%, recall of 93%, and F1 score of 93% in the detection and classification of cybersecurity intrusions in IEDs of smart grids. The implementation of One Class-SVM with binary classification yields an accuracy of 85%, precision of 89%, recall of 85%, and F1 score of 86%. We implemented the benchmark machine-learning models, i.e., Gradient Boosting Machine and Support Vector Machine, for performance comparison with the proposed modified machine-learning models. The performance comparison shows that the modified machine learning models implemented in the proposed framework outperformed the benchmark machine-learning models.

The Internet of Things (IoT) is a growing network of interconnected devices used in transportation, finance, public services, healthcare, smart cities, surveillance, and agriculture. IoT devices are increasingly integrated into mobile assets like trains, cars, and airplanes. Among the IoT components, wearable sensors are expected to reach three billion by 2050, becoming more common in smart environments like buildings, campuses, and healthcare facilities. A notable IoT application is the smart campus for educational purposes. Timely notifications are essential in critical scenarios. IoT devices gather and relay important information in real time to individuals with special needs via mobile applications and connected devices, aiding health-monitoring and decision-making. Ensuring IoT connectivity with end users requires long-range communication, low power consumption, and cost-effectiveness. The LPWAN is a promising technology for meeting these needs, offering a low cost, long range, and minimal power use. Despite their potential, mobile IoT and LPWANs in healthcare, especially for emergency response systems, have not received adequate research attention. Our study evaluated an LPWAN-based emergency response system for visually impaired individuals on the Hazara University campus in Mansehra, Pakistan. Experiments showed that the LPWAN technology is reliable, with 98% reliability, and suitable for implementing emergency response systems in smart campus environments.