Orchestration‐based mechanism for sampling adaptation in sensing‐based applications. Issue 3 (10th March 2021)

• Record Type: Journal Article
• Authors: Harb, H.; Baalbaki, H.; Jaoude, C. Abou; Jaber, A.
• Other Names: Hancke Gerhard P. guestEditor.; Reza Salehizadeh Mohammad guestEditor.; Liu Xuan guestEditor.; Hu Jie guestEditor.; Abu‐Mahfouz Adnan M. guestEditor.; Thomos Nikolaos guestEditor.; Ishihara Susumu guestEditor.; Savaglio Claudio guestEditor.
• Is Part Of: IET smart cities. Volume 3:Issue 3(2021)
• Page Start: 158
• Page End: 170
• Publication Date: 2021-03-10
• Subjects: wireless sensor networks -- energy conservation -- data analysis -- signal sampling; Smart cities -- Periodicals; City planning -- Technological innovations -- Periodicals; Cities and towns -- Growth -- Periodicals; Sustainable urban development -- Periodicals; Sustainable development; City planning -- Technological innovations; Cities and towns -- Growth; Periodicals; 307.76
• DOI: 10.1049/smc2.12002
• Languages: English
• ISSNs: 2631-7680

Abstract

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.