In the contemporary landscape, the escalating deployment of drones across diverse industries has ushered in a consequential concern, including ensuring the security of drone operations. This concern extends to a spectrum of challenges, encompassing collisions with stationary and mobile obstacles and encounters with other drones. Moreover, the inherent limitations of drones, namely constraints on energy consumption, data storage capacity, and processing power, present formidable obstacles in developing collision avoidance algorithms. This review paper explores the challenges of ensuring safe drone operations, focusing on collision avoidance. We explore collision avoidance methods for UAVs from various perspectives, categorizing them into four main groups: obstacle detection and avoidance, collision avoidance algorithms, drone swarm, and path optimization. Additionally, our analysis delves into machine learning techniques, discusses metrics and simulation tools to validate collision avoidance systems, and delineates local and global algorithmic perspectives. Our evaluation reveals significant challenges in current drone collision prevention algorithms. Despite advancements, critical UAV network and communication challenges are often overlooked, prompting a reliance on simulation-based research due to cost and safety concerns. Challenges encompass precise detection of small and moving obstacles, minimizing path deviations at minimal cost, high machine learning and automation expenses, prohibitive costs of real testbeds, limited environmental comprehension, and security apprehensions. By addressing these key areas, future research can advance the field of drone collision avoidance and pave the way for safer and more efficient UAV operations.

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 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.

The proliferation of Internet of Things (IoT) devices and other IT forms in almost every area of human existence has resulted in an enormous influx of data that must be managed and stored. One viable solution to this issue is to store and handle massive amounts of data in cloud environments. Real-time data analysis has always been critical. However, it becomes even more crucial as technology and the IoT develop, and new applications emerge, such as autonomous cars, smart cities, and IoT devices for healthcare, agriculture, and other industries. Given the massive volume of data, moving to a remote cloud is time-consuming and produces severe network congestion, rendering cloud administration and rapid data processing difficult. Fog computing provides close-to-device processing at the network’s periphery, and fog computing can analyze data in near real-time. However, the increased amount of IoT gadgets and data they produce is a formidable challenge for fog nodes. Task offloading may enhance fog computing by offloading the excess data to other nodes for processing due to the restricted resources in the fog. Management of tasks and resources must be optimized in fog devices. This review article overviews related works on task offloading in IoT-Fog-Cloud Environment. In addition, we discuss about fog networks and Software-defined network (SDN) applications and challenges in fog offloading.

Population-based meta-heuristic is a high-level method intended to provide sufficient solution for problems with incomplete information among a massive volume of solutions. However, it does not guarantee to attain global optimum in a reasonable time. To improve the time and accuracy of the coverage in the population-based meta-heuristic, this paper presents a novel algorithm called the raccoon optimization algorithm (ROA). The ROA is inspired by the rummaging behaviors of real raccoons for food. Raccoons are successful animals because of their extraordinarily sensitive and dexterous paws and their ability to find solutions for foods and remember them for up to three years. These capabilities make raccoons expert problem solvers and allow them to purposefully seek optimum solutions. These behaviors exploited in the ROA to search the solution spaces of nonlinear continuous problems to find the global optimum with higher accuracy and lower time coverage. To evaluate the ROA’s ability in addressing complicated problems, it has been tested on several benchmark functions. The ROA is then compared with nine other well-known optimization algorithms. These experiments show that the ROA achieves higher accuracy with lower coverage time.