Internet of things (IoT) is a smart technology that connects anything anywhere at any time. Such ubiquitous nature of IoT is responsible for draining out energy from its resources. Therefore, the energy efficiency of IoT resources has emerged as a major research issue. In this paper, an energy-efficient architecture for IoT has been proposed, which consists of three layers, namely, sensing and control, information processing, and presentation. The architectural design allows the system to predict the sleep interval of sensors based upon their remaining battery level, their previous usage history, and quality of information required for a particular application. The predicted value can be used to boost the utilization of cloud resources by reprovisioning the allocated resources when the corresponding sensory nodes are in sleep mode. This mechanism allows the energy-efficient utilization of all the IoT resources. The experimental results show a significant amount of energy saving in the case of sensor nodes and improved resource utilization of cloud resources.

In the present scenario, performance evaluation of employees in industries is done manually, in which there are ample chances of biases. It is observed that manual employee evaluation systems can be efficiently eliminated by using ubiquitous sensing capabilities of Internet of things (IoT) devices to monitor industrial employees. However, none of the authors have used IoT data for automating performance evaluation systems of employees. Hence, this paper proposes a game theoretic approach for an IoT-based employee performance evaluation in industry. The system infers useful results about the performance of employees by mining data collected by the sensory nodes using the MapReduce model. The information hence obtained is then used to draw automated decisions for employees using game theory. The system is analyzed both experimentally and mathematically. The experimental evaluation compares the proposed system with other techniques of data mining and decision making. The results depict that the proposed system evaluates the performance of employees efficiently and shows a performance improvement over other techniques. The mathematical evaluation shows that correct evaluation of employees by the system effectively motivates employees in favor of the industry. Thus, the proposed system effectively and efficiently automates the employee evaluation system and decision-making process in the industry.

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Summary Various Internet‐based applications such as social media, business transactions, mobile applications, cyber‐physical systems, and Internet of Things have led to the generation of big data streams in every field. The growing need to extract knowledge from big data streams has pioneered the challenge of selecting appropriate cloud resources. The current techniques allocate resources based on data characteristics. But because of the stochastic nature of data generation, the characteristics of data in big data streams are unknown. This poses difficulty in selecting and allocating appropriate resources to big data stream. Working towards this direction, this paper proposes a system that predicts the data characteristics in terms of volume, velocity, variety, variability, and veracity. The predicted values are expressed in a quadruple called Characteristics of Big data (CoBa). Thereafter, the proposed system uses self‐organizing maps to dynamically create clusters of cloud resources. One of these clusters is allocated to the big data stream based on its CoBa. The proposed system is dynamic in the sense that it changes the cloud cluster allocated to big data stream if its CoBa changes. Experimental results show that the proposed system has a performance edge over other streaming data processing tools such as Storm, Flume, and S4.

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