The revolution of IoT and its capabilities to serve various fields led to generating a large amount of data for processing. Tasks that require an instant response, especially with sensitive delay tasks send to the fog node due to the close distance, and the complex tasks transfer to the cloud data center for its huge computation and storage. However, sending tasks to the fog decreases the transmission delay. Still, it increases the energy consumption of the end users, while transferring tasks to the cloud reduces users’ energy consumption but increases the transmission delay due to the long distance; besides, assigning tasks to appropriate resources compatible with task requirements. These are the main challenges in cloud-fog computing that need to improve. Thus, this study proposed a Multi-Objectives Grey Wolf Optimizer (MGWO) algorithm to reduce the QoS objectives delay and energy consumption and held in the fog broker, which plays an essential role in distributing tasks. The simulation result verifies the effectiveness of the MGWO algorithm compared to the state-of-the-art algorithms in reducing delay and Energy consumption.

The provision of resources and services for scientific workflow applications using a multi-cloud architecture and a pay-per-use rule has recently gained popularity within the cloud computing research domain. This is because workflow applications are computation intensive. Most of the existing studies on workflow scheduling in the cloud mainly focus on finding an ideal makespan or cost. Nevertheless, there are other important quality of service metrics that are of critical concern in workflow scheduling such as reliability and resource utilization. In this respect, this paper proposes a new multi-objective scheduling algorithm with Fuzzy resource utilization (FR-MOS) for scheduling scientific workflow based on particle swarm optimization (PSO) method. The algorithm minimizes cost and makespan while considering reliability constraint. The coding scheme jointly considers task execution location and data transportation order. Simulation experiments reveal that FR-MOS outperforms the basic MOS over the PSO algorithm.

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.

Cloud computing is an innovative technology that deploys networks of servers, located in wide remote areas, for performing operations on a large amount of data. In cloud computing, a workflow model is used to represent different scientific and web applications. One of the main issues in this context is scheduling large workflows of tasks with scientific standards on the heterogeneous cloud environment. Other issues are particular to public cloud computing. These include the need for the user to be satisfied with the quality of service (QoS) parameters, such as scalability and reliability, as well as maximize the end-users resource utilization rate. This paper surveys scheduling algorithms based on particle swarm optimization (PSO). This is aimed at assisting users to decide on the most suitable QoS consideration for large workflows in infrastructure as a service (IaaS) cloud applications and mapping tasks to resources. Besides, the scheduling schemes are categorized according to the variant of the PSO algorithm implemented. Their objectives, characteristics, limitations and testing tools have also been highlighted. Finally, further directions for future research are identified.

In recent years, fire detection technologies have helped safeguard lives and property from hazards. Early fire warning methods, such as smoke or gas sensors, are ineffectual. Many fires have caused deaths and property damage. IoT is a fast-growing technology. It contains equipment, buildings, electrical systems, vehicles, and everyday things with computing and sensing capabilities. These objects can be managed and monitored remotely as they are connected to the Internet. In the Internet of Things concept, low-power devices like sensors and controllers are linked together using the concept of Low Power Wide Area Network (LPWAN). Long Range Wide Area Network (LoRaWAN) is an LPWAN product used on the Internet of Things (IoT). It is well suited for networks of things connected to the Internet, where terminals send a minute amount of sensor data over large distances, providing the end terminals with battery lifetimes of years. In this article, we design and implement a LoRaWAN-based system for smart building fire detection and prevention, not reliant upon Wireless Fidelity (Wi-Fi) connection. A LoRa node with a combination of sensors can detect smoke, gas, Liquefied Petroleum Gas (LPG), propane, methane, hydrogen, alcohol, temperature, and humidity. We developed the system in a real-world environment utilizing Wi-Fi Lora 32 boards. The performance is evaluated considering the response time and overall network delay. The tests are carried out in different lengths (0–600 m) and heights above the ground (0–2 m) in an open environment and indoor (1st Floor–3rd floor) environment. We observed that the proposed system outperformed in sensing and data transfer from sensing nodes to the controller boards.

The Internet of Things (IoT) generates massive data from smart devices that demand responses from cloud servers. However, sending tasks to the cloud reduces the power consumed by the users’ devices, but increases the transmission delay of the tasks. In contrast, sending tasks to the fog server reduces the transmission delay due to the shorter distance between the user and the server. However, this occurs at the user end’s expense of higher energy consumption. Thus, this study proposes a mathematical framework for workload allocation to model the power consumption and delay functions for both fog and clouds. After that, a Modified Least Laxity First (MLLF) algorithm was proposed to reduce the maximum delay threshold. Furthermore, a new multi-objective approach, namely the Non-dominated Particle Swarm Optimization (NPSO), is proposed to reduce energy consumption and delay compared to the state-of-the-art algorithms. The simulation results show that NPSO outperforms the state-of-the-art algorithm in reducing energy consumption, while NGSA-II proves its effectiveness in reducing transmission delay compared to the other algorithms in the experimental simulation. In addition, the MLLF algorithm reduces the maximum delay threshold by approximately 11% compared with other related algorithms. Moreover, the results prove that metaheuristics are more appropriate for distributed computing.