Resource scheduling

Trust establishment (TE) among sensor nodes has become a vital requirement to improve security, reliability, and successful cooperation. Existing trust management approaches for large scale WSN are failed due to their low cooperation (i.e., dependability), higher communication and memory overheads (i.e., resource inefficient). This paper provides a new and comprehensive hybrid trust estimation approach for large scale WSN employing clustering to improve cooperation, trustworthiness, and security by detecting selfish sensor nodes with reduced resource (memory, power) consumption. The proposed scheme consists of unique features like authentication based data trust, scheduler based node trust, and attack resistant by giving the high penalty and minimum reward during node misbehavior. A task scheduling mechanism is employed for scheduling the significant task to reduce computation overhead. The proposed trust model would be capable to provide security against blackhole attack, grey hole attack, and badmouthing attack. Moreover, the proposed trust model feasibility has been tested with MATLAB. Simulation results exhibit the great performance of our proposed approach in terms of trust evaluation cost, prevention, and detection of malicious nodes with the help of analyzing consistency in trust values and communication overhead.

The term “cloud computing (CC)” has been extensively studied and utilized by major corporations since its inception. Within the realm of cloud computing, various research topics and perspectives have been explored, including resource management, cloud security, and energy efficiency. This paper explores the intersection of data quality and business process management within the context of cloud computing. Specifically, it examines how Quality of Service (QoS)-driven task scheduling algorithms in cloud environments can enhance data quality and optimize business processes. Cloud computing still faces the significant challenge of determining the most effective way to schedule tasks and manage available resources. We need effective scheduling strategies to manage these resources because of the scale and dynamic resource provisioning in modern data centers. The purpose of this work is to provide an overview of the various task scheduling methods that have been utilized in the cloud computing environment to date. An attempt has been made to categorize current methods, investigate issues, and identify important challenges present in this area. Our data reveals that 34% of researchers are focusing on makespan for QoS (Quality of Service) metrics, 17% on cost, 15% on load balancing, 10% on deadline, and 9% on energy usage. Other criteria for the Quality of Service (QoS) parameter contribute far less than the ones mentioned above. According to this study, scheduling algorithms commonly used by researchers include the genetic algorithm in bio-inspired systems and particle swarm optimization in swarm intelligence 80% of the time. According to the available literature, 70% of the studies have utilized CloudSim as their simulation tool of choice. Our findings suggest that current methodologies mainly employ genetic algorithms and particle swarm optimization, with CloudSim being a popular simulation tool. Ongoing work emphasizes refining scheduling strategies to enhance resource management in dynamic data center environments, providing crucial insights into future quality-of-service (QoS)-driven scheduling algorithms for cloud computing.