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The escalation of Internet of Things (IoT) devices has led to increased data generation at the network edge that has burdened the cloud infrastructure in terms of handling and processing of data. This has led to the rapid adoption of fog computing because of its ability to bring computation and storage closer to the edge and support for real-time applications and services by reducing latency. One of the foremost challenges in the fog computing arena is minimizing turnaround time. This research paper proposes a Modified Levy Flight Firefly Algorithm (MLFFA) to optimize task scheduling for fog computing environments. Specifically, the objective is to minimize the turnaround time of tasks. Moreover, genetic operators like crossover and mutation are also employed to achieve an optimal balance between exploration and exploitation. Experimental observations undertaken show that the proposed method improves the average turnaround time by 55%, 22%, and 13%, average waiting time by 59%, 45%, and 37%, average energy consumption by 19%, 7%, and 4%, and average failure rate by 50%, 28%, and 7% compared to the existing studies, namely Load Balancing and Optimization Strategy (LBOS), Technique for Resource Allocation and Management (TRAM), and Fuzzy Golden Eagle Load Balancing (FGELB), respectively. [ABSTRACT FROM AUTHOR]

The groundwater is a significant source of readily available freshwater. However, these groundwater aquifers are susceptible to contamination by the presence of many potential pollution sources. These potential sources may be active at any location for any duration with an unknown magnitude of source fluxes. The identification of these potential sources is necessary for remediation and management. In this work, the groundwater contamination sources are identified for two probable situations, i.e., Error-free concentration measurements and erroneous concentration measurements. The proposed methodology exploits the capability of the multistage approach of recent hybrid soft computing models (ensemble of Wavelet Neural Network [WNN]; Autoregressive Integrated Moving Average [ARIMA]) to identify potential pollution sources effectively. The models are trained by the simulated concentrations for randomly generated source fluxes by Groundwater Modelling System (GMS). The multistage approach has been adopted here, which includes three-stage models. To exploit the effectiveness of WNN and ARIMA, they are used at different stages in the multistage models, thereby developing various hybrid ensembles. Total of four hybrid models are developed utilising the multi-staging approach of WNN and ARIMA models, Namely Hybrid-II, III, IVA, and IVB. Predicted results from these models were compared on the same data sets. They are compared using various performance indices, such as RMSE, NE, correlation coefficient, etc. Error values of Hybrid II ensemble (employing WNN at first stage and ARIMA at second stage) are lowest as compared to only WNN and other Hybrid models. Hence, the Hybrid-II model can be utilised to accurately predict potential contamination sources in any sample area in groundwater. [ABSTRACT FROM AUTHOR]

Hydraulic structures such as barrages divert water from the river to main irrigation or power generation canals. The depth of sheet piles, the length and thickness of floor may be treated as basic parameters for design of hydraulic structures. However, there is no fixed procedure to design these basic parameters. Present work investigates the optimal design of barrages located on an alluvial permeable foundation. The optimum cost is nonlinear function of basic design parameters. A nonlinear optimization formulation (NLOF) is solved using hybrid differential evolution multiple particle swarm optimization (HDEMPSO). The mathematical model that represents the subsurface flow is embedded in the NLOF. The applicability of the approach has been illustrated with a typical example of barrage profile. The results obtained in this study show drastic cost savings when the proposed NLOF is solved using HDEMPSO. HDEMPSO results are better compared to those obtained by genetic algorithm and conventional techniques. Key words: optimal design, subsurface flow, hydraulic structures, barrages, embedded simulation optimization, seepage head, genetic algorithm, differential evolution, hybrid particle swarm optimization. Les ouvrages hydrauliques tels que les barrages detournent l'eau des rivieres pour alimenter les canaux hydroelectriques ou d'irrigation principaux. La profondeur des palplanches, la longueur et l'epaisseur du seuil du barrage peuvent etre traitees comme des parametres de base de la conception d'ouvrages hydrauliques. Cependant, il n'existe pas de procedure clairement definie pour permettant d'etablir de tels parametres. La presente etude porte sur la conception optimale de barrages dont la fondation repose sur des alluvions permeables. Le cout optimal est represente par une fonction lineaire des parametres de base de conception. Une formulation d'optimisation non lineaire (FONL) est resolue a l'aide de la methode hybride basee sur l'optimisation multiobjectif par essaim particulaire et sur revolution differentielle (MHOMEPED). Le modele mathematique qui represente l'ecoulement subsurfacique est integre a la FONL. L'applicabilite de cette approche est demontree au moyen d'un exemple typique de profil de barrage. Les resultats obtenus dans la presente etude montrent que des economies considerables sont realisees lorsque la FONL proposee est resolue a l'aide de la MHOMEPED. Les resultats obtenus par cette derniere sont plus facilement comparables a ceux trouves grace aux algorithmes genetiques et aux methodes conventionnelles. [Traduit par la Redaction] Mots-cles: conception optimale, ecoulement subsurfacique, ouvrages hydrauliques, barrages, integration de l'optimisation et de la simulation, hauteur de chute, algorithmes genetiques, evolution differentielle, methode hybride d'optimisation par essaim particulaire., Introduction Rivers of the Indian subcontinent observe high variability of flow over the year. Design and construction of diversion head works has been a matter of concern for hydraulic researchers [...]

Task scheduling is a critical issue in distributed computing environments like cloud and fog. The objective is to provide an optimal distribution of tasks among the resources. Several research initiatives to use meta-heuristic techniques for finding near-optimal solutions to task scheduling problems are under way. This study presents a comprehensive taxonomic review and analysis of recent metaheuristic scheduling techniques using exhaustive evaluation criteria in the cloud and fog environment. A taxonomy of metaheuristic scheduling algorithms is presented. Besides, we have considered an extensive list of scheduling objectives along with their associated metrics. Rigorous evaluation of existing literature is performed, and limitations highlighted. We have also focused on hybrid algorithms as they tend to improve scheduling performance. We believe that this work will encourage researchers to conduct further research for removing the limitations in existing studies. [ABSTRACT FROM AUTHOR]

The temporal and spatial characterization of unknown groundwater pollution sources remains an important problem in effective aquifer remediation and assessment of associated health risks. The characterization of contaminated source involves identifying spatially and temporally varying source locations, injection rates, and release periods. The proposed methodology exploits the universal function approximation capability of a feed forward multilayer artificial neural network (ANN) to identify the unknown pollution sources. The ANN is trained to identify source characteristics based on simulated contaminant concentration measurement data at specified observation locations in the aquifer. These concentrations are simulated for a large set of randomly generated pollution source fluxes. The back-propagation algorithm is used for training the ANN, with each corresponding set of source fluxes and resulting concentration measurement constituting a pattern for training the ANN. Performance of this methodology is evaluated for various data availability, measurement error, and source location scenarios. The developed ANNs are capable of identifying unknown groundwater pollution sources at multiple locations using erroneous measurement data. CE Database subject headings: Ground-water pollution; Pollution sources; Neural networks; Data analysis.