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With the advancement of wireless communication, internet of things, and big data, high performance data analytic tools and algorithms are required. Data clustering, a promising analytic technique is widely used to solve the IoT and big data based problems, since it does not require labeled datasets. Recently, meta-heuristic algorithms have been efficiently used to solve various clustering problems. However, to handle big data sets produced from IoT devices, these algorithm fail to respond within desired time due to high computation cost. This paper presents a new meta-heuristic based clustering method to solve the big data problems by leveraging the strength of MapReduce. The proposed methods leverages the searching potential of military dog squad to find the optimal centroids and MapReduce architecture to handle the big data sets. The optimization efficacy the proposed method is validated against 17 benchmark functions and the results are compared with 5 other recent algorithms namely, bat, particle swarm optimization, artificial bee colony, multiverse optimization, and whale optimization algorithm. Further, a parallel version of the proposed method is introduced using MapReduce (MR-MDBO) for clustering the big datasets produced from industrial IoT. Moreover, the performance of MR-MDBO is studied on 2 benchmark UCI datasets and 3 real IoT based datasets produced from industry. The F-measure and computation time of the MR-MDBO is compared with the 6 other state-of-the-art methods. The experimental results witness that the proposed MR-MDBO based clustering outperforms the other considered algorithms in terms of clustering accuracy and computation times.

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Bioclastic carbonate deposits that formed because of a combination of nearshore marine, fluvial, and aeolian processes, occur along the Saurashtra coast and in the adjacent interior regions of western India. Whether these carbonates formed by marine or aeolian processes has been debated for many decades. The presence of these deposits inland poses questions as to whether they are climate controlled or attributable to postdepositional tectonic uplift. In particular, the debate centres on chronologic issues including (1) appropriate sampling strategies and (2) the use of 230Th/234U and 14C ages on the bulk carbonates. Using traces (<1%) of quartz grains trapped in carbonate matrices, optically stimulated luminescence (OSL) dating of quartz grains, deposited along with the carbonate grains, provides ages for the most recent deposition events. The OSL ages range from >165 to 44 ka for the shell limestones, 75–17 ka for the fluvially reworked sheet deposits, and 80–11 ka for miliolites deposited by aeolian processes. These are younger than the 230Th/234U and 14C ages and suggest that the inland carbonate deposits were reworked from older carbonate sediments that were transported during more arid phases.

In order to design micro-catchment water harvesting systems in the Indian desert, rainwater infiltration experiments were conducted on a representative loamy sand soil for a period of six years. Plots with three slopes -0.5, 5 and 10%, and five slope lengths -5.12, 7.0, 8.5, 10.75 and 14.5 m were used. With dry antecedent soil conditions, infiltration is governed by rainfall depth, whereas with wet antecedent soil conditions, raindrop impact (intensity) which forms a crust over the soil surface, is the deciding factor. Infiltration decreases significantly with increasing slope due to reduction in the time available for rainfall to infiltrate, but slope length has no significant effect. A graphical multiple curvilinear model to predict rainfall infiltration using rainfall and basin characteristics is developed and the goodness of fit is tested.