No abstract

In the application of the Internet of Things (IoT), a sensor board depends on a battery that has a limited lifetime to function. Furthermore, the IoT sensor board with multivariate sensors influences the battery lifetime , since there are additional data transmissions that must be supported by the board causing it to drain the battery much faster than the sensor board with one sensor. The main aim of this paper is to increase the battery life of the IoT sensor node. To do so, this paper proposes an efficient real-time data collection model for multivariate sensors in IoT/WSN applications named RDCM. The general structure of RDCM is composed of two main levels: the IoT sensor board level and the fusion center level. The IoT sensor board level is implemented in real time by all the IoT sensor boards simultaneously in each cycle and fusion center level is executed by the fusion center. The IoT sensor board level includes various stages as follows: check the physical conditions of the IoT edge device (board) stage and update data strategy stage, data validation stage, and sensed data reduction stage. The average of the total percentage of energy saved by the application of RDCM to real-time data sets injected with various percentages of errors for all nodes is 98%. In summary, the RDCM has a very high performance in terms of energy consumption compared with other algorithms. This paper concludes with the limitation of the current study and some further research opportunities.

This paper presents a new metric to assess the performance of different multivariate data reduction models in wireless sensor networks (WSNs). The proposed metric is called Updating Frequency Metric (UFM) which is defined as the frequency of updating the model reference parameters during data collection. A method for estimating the error threshold value during the training phase is also suggested. The proposed threshold of error is used to update the model reference parameters when it is necessary. Numerical analysis and simulation results show that the proposed metric validates its effectiveness in the performance of multivariate data reduction models in terms of the sensor node energy consumption. The adaptive threshold improves the frequency of updating the parameters by 80% and 52% in comparison to the non-adaptive threshold for multivariate data reduction models of MLR-B and PCA-B respectively.

No abstract

No abstract