Insights on critical energy efficiency approaches in internet-of-things application

Internet-of-things (IoT) is one of the proliferated technologies that result in a larger scale of connection among different computational devices. However, establishing such a connection requires a fault-tolerant routing scheme. The existing routing scheme results in communication but does not address various problems directly linked with energy consumption. Cross layer-based scheme and optimization schemes are frequently used scheme for improving the energy efficiency performance in IoT. Therefore, this paper investigates the approaches where cross-layer-based schemes are used to retain energy efficiencies among resource-constrained devices. The paper discusses the effectivity of the approaches used to optimize network performance in IoT applications. The study outcome of this paper showcase that there are various open-end issues, which is required to be addressed effectively in order to improve the performance of application associated with the IoT system

Data-aided Sensing for Gaussian Process Regression in IoT Systems

In this paper, for efficient data collection with limited bandwidth, data-aided sensing is applied to Gaussian process regression that is used to learn data sets collected from sensors in Internet-of-Things systems. We focus on the interpolation of sensors' measurements from a small number of measurements uploaded by a fraction of sensors using Gaussian process regression with data-aided sensing. Thanks to active sensor selection, it is shown that Gaussian process regression with data-aided sensing can provide a good estimate of a complete data set compared to that with random selection. With multichannel ALOHA, data-aided sensing is generalized for distributed selective uploading when sensors can have feedback of predictions of their measurements so that each sensor can decide whether or not it uploads by comparing its measurement with the predicted one. Numerical results show that modified multichannel ALOHA with predictions can help improve the performance of Gaussian process regression with data-aided sensing compared to conventional multichannel ALOHA with equal uploading probability.Comment: 10 pages, 8 figures, to appear in IEEE IoT

A cross-layer approach to data-aided sensing using compressive random access

In this paper, data-aided sensing as a cross-layer approach in Internet-of-Things (IoT) applications is studied, where multiple IoT nodes collect measurements and transmit them to an Access Point (AP). It is assumed that measurements have a sparse representation (due to spatial correlation) and the notion of Compressive Sensing (CS) can be exploited for efficient data collection. For data-aided sensing, a node selection criterion is proposed to efficiently reconstruct a target signal through iterations with a small number of measurements from selected nodes. Together with Compressive Random Access (CRA) to collect measurements from nodes, compressive transmission request is proposed to efficiently send a request signal to a group of selected nodes. Error analysis on compressive transmission request is carried out and the impact of errors on the performance of data-aided sensing is studied. Simulation results show that data-aided sensing allows to reconstruct the target information with a small number of active nodes and is robust to nodes' decision errors on compressive transmission request.Comment: 10 pages, 10 figures, IEEE IoTJ (to be published