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Wireless Sensor Networks (WSNs) have left an indelible mark on the lives of all by aiding in various sectors such as agriculture, education, manufacturing, monitoring of the environment, etc. Nevertheless, because of the wireless existence, the sensor node batteries cannot be replaced when deployed in a remote or unattended area. Several researches are therefore documented to extend the node's survival time. While cluster-based routing has contributed significantly to address this issue, there is still room for improvement in the choice of the cluster head (CH) by integrating critical parameters. Furthermore, primarily the focus had been on either the selection of CH or the data transmission among the nodes. The meta-heuristic methods are the promising approach to acquire the optimal network performance. In this paper, the 'CH selection' and 'sink mobility-based data transmission', both are optimized through a hybrid approach that consider the genetic algorithm (GA) and particle swarm optimization (PSO) algorithm respectively for each task. The robust behavior of GA helps in the optimized the CH selection, whereas, PSO helps in finding the optimized route for sink mobility. It is observed through the simulation analysis and results statistics that the proposed GAPSO-H (GA and PSO based hybrid) method outperform the state-of-art algorithms at various levels of performance metrics.

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Workflow scheduling is one of the challenging issues in emerging trends of the distributed environment that focuses on satisfying various quality of service (QoS) constraints. The cloud receives the applications as a form of a workflow, consisting of a set of interdependent tasks, to solve the large-scale scientific or enterprise problems. Workflow scheduling in the cloud environment has been studied extensively over the years, and this article provides a comprehensive review of the approaches. This article analyses the characteristics of various workflow scheduling techniques and classifies them based on their objectives and execution model. In addition, the recent technological developments and paradigms such as serverless computing and Fog computing are creating new requirements/opportunities for workflow scheduling in a distributed environment. The serverless infrastructures are mainly designed for processing background tasks such as Internet-of-Things (IoT), web applications, or event-driven applications. To address the ever-increasing demands of resources and to overcome the drawbacks of the cloud-centric IoT, the Fog computing paradigm has been developed. This article also discusses workflow scheduling in the context of these emerging trends of cloud computing.

In a WSN, sensor node plays a significant role. Working of sensor node depends upon its battery's life. Replacements of batteries are found infeasible once they are deployed in a remote or unattended area. Plethora of research had been conducted to address this challenge, but they suffer one or the other way. In this paper, a particle swarm optimization (PSO) algorithm integrated with an energy efficient clustering and sink mobility ((PSO-ECSM) is proposed to deal with both cluster head selection problem and sink mobility problem. Extensive computer simulations are conducted to determine the performance of the PSO-ECSM. Five factors such as residual energy, distance, node degree, average energy and energy consumption rate (ECR) are considered for CH selection. An optimum value of these factors is determined through PSO-ECSM algorithm. Further, PSO-ECSM addresses the concern of relaying the data traffic in a multi-hop network by introducing sink mobility. PSO-ECSM's performances are tested against the state-of-the-art algorithms considering five performance metrics (stability period, network, longevity, number of dead nodes against rounds, throughput and network's remaining energy). Statistical tests are conducted to determine the significance of the performance. Simulation results show that the PSO-ECSM improves stability period, half node dead, network lifetime and throughput vis-à-vis ICRPSO by 24.8%, 31.7%, 9.8 %, and 12.2%, respectively.

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