Cloud computing is a rapidly growing technology that has been implemented in various fields in recent years, such as business, research, industry, and computing. Cloud computing provides different services over the internet, thus eliminating the need for personalized hardware and other resources. Cloud computing environments face some challenges in terms of resource utilization, energy efficiency, heterogeneous resources, etc. Tasks scheduling and virtual machines (VMs) are used as consolidation techniques in order to tackle these issues. Tasks scheduling has been extensively studied in the literature. The problem has been studied with different parameters and objectives. In this article, we address the problem of energy consumption and efficient resource utilization in virtualized cloud data centers. The proposed algorithm is based on task classification and thresholds for efficient scheduling and better resource utilization. In the first phase, workflow tasks are pre-processed to avoid bottlenecks by placing tasks with more dependencies and long execution times in separate queues. In the next step, tasks are classified based on the intensities of the required resources. Finally, Particle Swarm Optimization (PSO) is used to select the best schedules. Experiments were performed to validate the proposed technique. Comparative results obtained on benchmark datasets are presented. The results show the effectiveness of the proposed algorithm over that of the other algorithms to which it was compared in terms of energy consumption, makespan, and load balancing.

In wireless sensor networks for the Internet of Things (WSN‐IoT), the topology deviates very frequently because of the node mobility. The topology maintenance overhead is high in flat‐based WSN‐IoTs. WSN clustering is suggested to not only reduce the message overhead in WSN‐IoT but also control the congestion and easy topology repairs. The partition of wireless mobile nodes (WMNs) into clusters is a multiobjective optimization problem in large‐size WSN. Different evolutionary algorithms (EAs) are applied to divide the WSN‐IoT into clusters but suffer from early convergence. In this paper, we propose WSN clustering based on the memetic algorithm (MemA) to decrease the probability of early convergence by utilizing local exploration techniques. Optimum clusters in WSN‐IoT can be obtained using MemA to dynamically balance the load among clusters. The objective of this research is to find a cluster head set (CH‐set) as early as possible once needed. The WMNs with high weight value are selected in lieu of new inhabitants in the subsequent generation. A crossover mechanism is applied to produce new‐fangled chromosomes as soon as the two maternities have been nominated. The local search procedure is initiated to enhance the worth of individuals. The suggested method is matched with state‐of‐the‐art methods like MobAC (Singh and Lohani, 2019), EPSO‐C (Pathak, 2020), and PBC‐CP (Vimalarani, et al. 2016). The proposed technique outperforms the state of the art clustering methods regarding control messages overhead, cluster count, reaffiliation rate, and cluster lifetime.

Students are flooded with enormous Internet-based information on their smartphones in their daily routine. Getting precise and relevant information on smartphones that assist students in their learning process is tedious and time consuming activity. This research explores the effective of Smart Mobile Adaptive App for Erudition and Teaching (SMART) in facilitating students in adaptive learning. SMART app is based on adaptive m-learning model consisting of six stages. SMART app latent variables which includes usefulness, ease of use, adaptiveness, spare time management and learning collaboration were measured and evaluated to reveal how much they contribute in assisting students in learning process. Moreover, different learning objects in form of different types of learning contents were evaluated when providing them on smartphones for adaptive learning characteristics. Final results revealed that m-learning can be a prodigious complementary instrument to outmoded classroom learning.

Real-time flows using time division multiple access (TDMA) scheduling in cluster-based wireless sensor networks try to schedule more flows per time frame to minimize the schedule length to meet the deadline. The problem with the previously used cluster-based scheduling algorithm is that intra-cluster scheduling does not consider that the clusters may have internal or outgoing flows. Thus, intra-cluster scheduling algorithms do not utilize their empty time-slots and thus increase schedule length. In this paper, we propose a new intra-cluster scheduling algorithm by considering that clusters may have having internal or outgoing flows. Thus, intra-cluster scheduling algorithms do not differentiate the intra-cluster time slots and utilize their empty time slots. The objective is to schedule more flows per time frame, to reduce schedule length and improve the acceptance rate of flows. Simulation results show that the acceptance rate of the proposed scheme has a higher performance than the previous scheme.