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Internet of Things (IoT) has attracted tremendous research attention in the recent past fromindustry and academia. IoT is quite helpful in uplifting living standards by transforming conventional technology into smart systems. Greenhouse production is considered as an ultimate solution for rising global food demands with the growing population. Greenhouse provides a year-round production facility for fresh vegetables with around 50% increased production rate in comparison to open-air cultivation. However, energy consumption and labor cost in greenhouses account for more than 50% of the cost of greenhouse production. In this paper, we have proposed a novel optimization scheme that aims to achieve a trade-off between energy consumption and desired climate setting in greenhouse i.e. temperature, $${\mathrm{CO}}_2$$ level, and humidity. For performance evaluation of the proposed system, we have developed an ad-hoc emulator of the greenhouse environment. For the proposed model validation and experimental analysis, we have used 15 days of external environmental data collected in Jeju, South Korea. Proposed optimization scheme results are compared with a baseline scheme. Comparative analysis of experimental results shows that our proposed model maintains desired indoor environment for maximizing crop production with 26.56% reduced energy consumption than the baseline scheme. Furthermore proposed model achieve a 27.76% cost reduction when compared to the baseline scheme. Better optimization results of the proposed scheme give us the confidence to further investigate its effectiveness in a real environment for achieving improved energy efficiency.

The extremely vibrant, scattered, and non–transparent nature of cloud computing formulate trust management a significant challenge. According to scholars the trust and security are the two issues that are in the topmost obstacles for adopting cloud computing. Also, SLA (Service Level Agreement) alone is not necessary to build trust between cloud because of vague and unpredictable clauses. Getting feedback from the consumers is the best way to know the trustworthiness of the cloud services, which will help them improve in the future. Several researchers have stated the necessity of building a robust management system and suggested many ideas to manage trust based on consumers' feedback. This paper has reviewed various reputation-based trust management systems, including trust management in cloud computing, peer-to-peer system, and Adhoc system.

In recent few years, the widespread applications of indoor navigation have compelled the research community to propose novel solutions for detecting objects position in the Indoor environment. Various approaches have been proposed and implemented concerning the indoor positioning systems. This study propose an fuzzy inference based Kalman filter to improve the position estimation in indoor navigation. The presented system is based on FIS based Kalman filter aiming at predicting the actual sensor readings from the available noisy sensor measurements. The proposed approach has two main components, i.e., multi sensor fusion algorithm for positioning estimation and FIS based Kalman filter algorithm. The position estimation module is used to determine the object location in an indoor environment in an accurate way. Similarly, the FIS based Kalman filter is used to control and tune the Kalman filter by considering the previous output as a feedback. The Kalman filter predicts the actual sensor readings from the available noisy readings. To evaluate the proposed approach, the next-generation inertial measurement unit is used to acquire a three-axis gyroscope and accelerometer sensory data. Lastly, the proposed approach’s performance has been investigated considering the MAD, RMSE, and MSE metrics. The obtained results illustrate that the FIS based Kalman filter improve the prediction accuracy against the traditional Kalman filter approach.

Mountains are attraction spots for tourists, and tourism contributes to the country’s gross domestic product. Mountains have many benefits such as biodiversity, tourism, and the supplication of food, to name a few. However, there are challenges to protect mountain lives from hazards such as fire caused by tourist activities in mountains. The in-time fire detection and notification to the authorities have always been the central point in literature studies, and different studies have been carried out to optimize the notification time. In this paper, we model the fire detection and notification as a real-time internet of things application and uses task orchestration and task scheduling mechanism to provide scalability along with optimal latency. The proposed fire detection and prediction mechanism detect mountain fire at the earliest stage and provide predictive analysis to prevent damage to mountain life and tourists. The architecture is based on microservice-based IoT task orchestration mechanism and device virtualization, which is not only lightweight but also handles a single problem in parallel chunks, thus optimizes the latency. The in-time information about the fire is used for predictive analysis and notified to safety authorities which helps them to make a more informed decisions to minimize the damage caused by mountain fire. The performance of the proposed mechanism is evaluated in terms of different measures such as RMSE, MAPE, MSE, and MAPE. The proposed work approaches the fire detection and notification as a collection of tasks, and thus those tasks are selected for deployment which are guaranteed to be executed and have minimum latency. This idea of pre-planing the latency and task execution is the first attempt to the best of the authors’ knowledge.

Recently groundwater scarcity has accelerated drilling operations worldwide as drilled boreholes are substantial for replenishing the needs of safe drinking water and achieving long-term sustainable development goals. However, the quest for achieving optimal drilling efficiency is ever continued. This paper aims to provide valuable insights into borehole drilling data utilizing the potential of advanced analytics by employing several enhanced cluster analysis techniques to propel drilling efficiency optimization and knowledge discovery. The study proposed an L2-weighted K-mean clustering algorithm in which the mean is computed from transformed weighted feature space. To verify the effectiveness of our proposed L2-weighted K-mean algorithm, we performed a comparative analysis of the proposed work with traditional clustering algorithms to estimate the digging time and depth for different soil materials and land layers. The proposed clustering scheme is evaluated widely used evaluation metrics such as Dunn Index, Davies–Bouldin index (DBI), Silhouette coefficient (SC), and Calinski–Harabaz Index (CHI). The study results highlight the significance of the proposed clustering algorithm as it achieved better clustering results than conventional clustering approaches. Moreover, for facilitation of subsequent learning, achievement of reliable classification, and generalization, we performed feature extraction based on the time interval of the drilling process according to soil material and land layer. We formulated the solution by grouping the extracted features into six different blocks to achieve our desired objective. Each block corresponds to various characteristics of soil materials and land layers. Extracted features are examined and visualized in point cloud space to analyze the water level patterns, depth, and days required to complete the drilling operations.

During the last decade, substantial resources have been invested to exploit massive amounts of boreholes data collected through groundwater extraction. Furthermore, boreholes depth can be considered one of the crucial factors in digging borehole efficiency. Therefore, a new solution is needed to process and analyze boreholes data to monitor digging operations and identify the boreholes shortcomings. This research study presents a boreholes data analysis architecture based on data and predictive analysis models to improve borehole efficiency, underground safety verification, and risk evaluation. The proposed architecture aims to process and analyze borehole data based on different hydrogeological characteristics using data and predictive analytics to enhance underground safety verification and planning of borehole resources. The proposed architecture is developed based on two modules; descriptive data analysis and predictive analysis modules. The descriptive analysis aims to utilize data and clustering analysis techniques to process and extract hidden hydrogeological characteristics from borehole history data. The predictive analysis aims to develop a bi-directional long short-term memory (BD-LSTM) to predict the boreholes depth to minimize the cost and time of the digging operations. Furthermore, different performance measures are utilized to evaluate the performance of the proposed clustering and regression models. Moreover, our proposed BD-LSTM model is evaluated and compared with conventional machine learning (ML) regression models. The $R^{2}$ score of the proposed BD-LSTM is 0.989, which indicates that the proposed model accurately and precisely predicts boreholes depth compared to the conventional regression models. The experimental and comparative analysis results reveal the significance and effectiveness of the proposed borehole data analysis architecture. The experimental results will improve underground safety management and the efficiency of boreholes for future wells.