Time-Series clustering is one of the important concepts of data mining that is used to gain insight into the mechanism that generate the time-series and predicting the future values of the given time-series. Time-series data are frequently very large and elements of these kinds of data have temporal ordering. The clustering of time series is organized into three groups depending upon whether they work directly on raw data either in frequency or time domain, indirectly with the features extracted from the raw data or with model built from raw data. In this paper, we have shown the survey and summarization of previous work that investigated the clustering of time series in various application domains ranging from science, engineering, business, finance, economic, health care, to government.

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Crowdsensing utilizes the wisdom of crowd by sensing the information through different types of smart devices such as a smartphone, tablet, and sensors to serve the problem of common interests. It can be achieved by the concept of Internet of Things (IoT), which considers a large set of smart sensing interconnected devices. However, these devices are resource constrained in terms of storage, infrastructure, and computation power, and therefore, cloud technology is required. In order to minimize the storage overhead, only authentic extracted information from crowdsourcing is desirable to store in the cloud space. A number of signature‐based authentication schemes have been designed using either bilinear pairing or random oracle model (ROM) for the IoT devices. However, due to the high computational pairing cost and improper ROM implementation, schemes are found to be inefficient and vulnerable under the chosen‐message and chosen‐ID attack. To reduce the computational cost, we propose a pairing‐free quadratic residue–based signature scheme that authenticates IoT devices and cloud centric data. In addition, the proposed signature does not consider ROM, and it is secure in the standard model based on the proposed modified interactive quadratic residuosity assumption. Performance evaluation and comparisons ensure that our scheme is more efficient than earlier related schemes.

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