Orchestration‐based mechanism for sampling adaptation in sensing‐based applications

IET Smart Cities

H. Baalbaki¹  A. Jaber¹  H. Harb¹  C. Abou Jaoude² 

[1] Computer Science Department Faculty of Sciences Lebanese University Beirut Lebanon;Faculty of Engineering TICKET Lab Antonine University Baabda Lebanon;

关键词: wireless sensor networks;    energy conservation;    data analysis;    signal sampling;   

DOI  :  10.1049/smc2.12002

来源: DOAJ

【 摘 要 】

Abstract Currently, the world witnesses a boom in the sensing‐based applications where the number of connected devices is becoming higher than the people. Such small sensing devices are now deployed in billions around the world, collecting data about the surroundings and reporting them to the data analysis centres. This fact allows a better understanding of the world and helps to reduce the effects of potential risks. However, while the benefits of such devices are real and significant, sensing‐based applications face two major challenges: big data collection and restricted power of sensor battery. In order to overcome these challenges, data reduction and sampling sensor adaptation techniques have been proposed to reduce data collection and to save the sensor energy. The authors propose an orchestration‐based mechanism (OM) for adapting the sampling rate of the sensors in the network. OM is two‐fold: first, it proposes a data transmission model at the sensor level, based on the clustering and Spearman coefficient, in order to reduce the amount of data transmitted to the sink; second, it proposes a sampling rate mechanism at the cluster‐head level that allows searching the similarity between data collected by the neighbouring sensors, and then to adapt their sensing frequencies accordingly. A set of simulations on real sensor data have been conducted to evaluate the efficiency of OM, in terms of data reduction and energy conservation, compared to other exiting techniques.

【 授权许可】

Unknown