Journal of Management Information and Decision Sciences (Print ISSN: 1524-7252; Online ISSN: 1532-5806)


An Energy-Efficient Model for Internet of Things Using Compressive Sensing

Author(s): Jain, J. K., & Chauhan, D.

The Internet of Things is one of the most important and promising technologies today. Some researchers estimate that there are more than 20 billion connected devices and counting. Around us, there are smartphones, wearables, and other devices, all of which use sensors. Nowadays, sensors play an important role in our everyday life and in IoT. The sensor network provides service of IoTs applications on the user's side. In the sensor network, the utilization of energy is a major issue. The maximum utilization of energy enhances the IoTs application.  The main aim of this paper was to the development of an efficient model to minimize the energy for wireless sensor networks. In this paper, we proposed the dual probability-based energy estimation model in the wireless sensor network. The dual probability-based function measures the expected value of energy for the transmission of data. This function creates a subgroup of networks based on energy function and carries out the operation of energy management in the context of sensor node data processing. This function also integrates cloud-based services with sensor networks. The benefit of this function is that it increases the throughput of the network and the quality of service.  The proposed model was simulated in the MATLAB R-2014a environment and the results were obtained using different scenarios of network density.  Finally, we analyzed the performance of our proposed work with respect to the following metrics: data utility, energy consumptions, and data reconstruction error.

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