Abstract The emergence of technology and communication system paved way for the development of the internet of things (IoT). The IoT system generates diversified data and the quantity of the data is also huge. The IoT systems are developed to adhere to the situation and to make intelligent decisions in a specified time. Hence, the IoT system necessitates high processing and storage environment, which makes the effective response in a short‐duration. The data transmission across the mobile nodes and cloud service has made huge utilization of energy. The storage and energy consumption are considered as major issues in the IoT system whereas these issues will reflect in the performance of the IoT system. Initiation of edge computing into the IoT system permits the workload to be offloaded from the cloud providers, which is attained from the closer location of the source of data. This improves privacy, minimizes the saving time, and traffic. In this article, an Effective Energy usage and Data Compression Approach using Data Mining proposed for IoT data. The proposed approach is investigated by considering the driving behaviour and it achieves effective compression without influencing the quality of the data. The stress level of the driver is also identified with high accuracy.

There is a need to prevent the use of modulated voice signals to conduct criminal activities. Voice signal change detection based on convolutional neural networks is proposed. We use three commonly used voice processing software (Audacity, CoolEdit, and RTISI) to change tones in voice libraries. The research further raises each voice by five semitones and are recorded at different levels (+4, +5, +6, +7, and +8, respectively). Simultaneously, every voice is lowered by five halftones, represented as –4, –5, –6, –7, and –8, respectively. The convolution neural network corresponding to network b-3 is determined as the final classifier in this article through experiments. The average accuracy A1 of its three categories has reached more than 97%, the detection accuracy A2 of electronic tone sandhi speech has reached more than 97%, and the false alarm rate of the original speech is less than 1.9%. The outcomes obtained shows that the detection algorithm in this article is effective, and it has good generalization ability.

Surface temperature is one of the most important parameter for estimating the effect of climatic change on glaciers and it is a key requirement for input to energy balance models. For the Indian Himalayas, such measurements are scarce and difficult to obtain. Weather station network is very sparse and mostly weather stations are established near around glacier terminus or far from glaciers at lower. Since temperature analysis of different altitude zones are important for observing the effects of climate change with respect of altitude. Unfortunately, estimating surface temperature using traditional weather-station based meteorological observations is not a feasible solution for upper reach of glaciers. Therefore, through remote sensing studies, a synoptic view of the Himalayan region can be established, and used for regional climatological studies. In this paper, an attempt has been made to analyze the relationships between glacier surface temperature with altitude and location.

Terrestrial Water Storage (TWS) in the Bhagirathi Ganga and Vishnu Ganga basins is an important hydrologic component for the Ganga River. Improved estimation of TWS in the Bhagirathi Ganga and Vishnu Ganga basins is crucial for water resources management for India. Despite its importance, storage and change of TWS in the study area has not been well studied at a larger scale. In this investigation, TWS and its change (TWSC) is estimated in the Bhagirathi Ganga and Vishnu Ganga basins during the period of 2004 to 2010. TWS is calculated from two methods: The first method employs the GRACE remote sensing satellite while the second employs estimation by use of climate data of study area provided by NOAA. TWSC is estimated from the TWS by subtraction of two consecutive monthly values. In addition, Total water storage change is also estimated from a water balance approach using a combination of precipitation, runoff and transevaporation. The spatial average values of GRACE and NOAA from 1°×1° spatial resolutions was extracted using a watershed mask derived from the DEM of the Bhagirathi Ganga and Vishnu Ganga basins. The TWS result from the GRACE and NOAA show decreasing trend in TWS of 0.54 cm/year and 0.2 cm/year, respectively due to decrease in precipitation, high evaporation of the study area. The results indicate the great potential of GRACE and water balance approach using NOAA data for providing hydrological information to water resource management in Uttarakhand.