SENET: A novel architecture for IoT-based body sensor networks. (2020)
- Record Type:
- Journal Article
- Title:
- SENET: A novel architecture for IoT-based body sensor networks. (2020)
- Main Title:
- SENET: A novel architecture for IoT-based body sensor networks
- Authors:
- Arabi Bulaghi, Zohre
Habibi Zad Navin, Ahmad
Hosseinzadeh, Mehdi
Rezaee, Ali - Abstract:
- Abstract: Wireless sensor networks (WSNs) have been applied to various fields of study including medicine, agriculture, and engineering. Although recently, many architecture styles have been proposed to manage WSNs, most of them have ignored the application of artificial intelligence (AI) in wireless body sensor networks (WBSN). To this end, the present study aims to introduce a novel architecture (SENET), which is based on AI techniques and consists of three main layers. After describing the proposed architecture, the performance of four efficient and popular algorithms, i.e., world competitive contests (WCC), particle swarm optimization (PSO), ant colony optimization (ACO), and genetic algorithm (GA) is investigated for covering WBSNs using k head clusters (the k-coverage problem). The results show not only that the proposed architecture saves energy consumed by the wireless sensors, but also that the WCC algorithm is a suitable option for determining the positions of sensors in the proposed architecture in terms of WSN energy-consumption, the total number of required sensors, and reliability. The results also show that the proposed WCC algorithm, with an average 38.44 value of score on nine scenarios, outperforms other techniques. Graphical abstract: Image 1 Highlights: A novel artificial intelligence-based architecture, named SENET, is proposed. SENET may be used for managing wireless body sensor networks (WBSN). The performance of three efficient algorithms isAbstract: Wireless sensor networks (WSNs) have been applied to various fields of study including medicine, agriculture, and engineering. Although recently, many architecture styles have been proposed to manage WSNs, most of them have ignored the application of artificial intelligence (AI) in wireless body sensor networks (WBSN). To this end, the present study aims to introduce a novel architecture (SENET), which is based on AI techniques and consists of three main layers. After describing the proposed architecture, the performance of four efficient and popular algorithms, i.e., world competitive contests (WCC), particle swarm optimization (PSO), ant colony optimization (ACO), and genetic algorithm (GA) is investigated for covering WBSNs using k head clusters (the k-coverage problem). The results show not only that the proposed architecture saves energy consumed by the wireless sensors, but also that the WCC algorithm is a suitable option for determining the positions of sensors in the proposed architecture in terms of WSN energy-consumption, the total number of required sensors, and reliability. The results also show that the proposed WCC algorithm, with an average 38.44 value of score on nine scenarios, outperforms other techniques. Graphical abstract: Image 1 Highlights: A novel artificial intelligence-based architecture, named SENET, is proposed. SENET may be used for managing wireless body sensor networks (WBSN). The performance of three efficient algorithms is investigated in SENET. State-of-the-art algorithms are essential for better managing of WBSNs. … (more)
- Is Part Of:
- Informatics in medicine unlocked. Volume 20(2020)
- Journal:
- Informatics in medicine unlocked
- Issue:
- Volume 20(2020)
- Issue Display:
- Volume 20, Issue 2020 (2020)
- Year:
- 2020
- Volume:
- 20
- Issue:
- 2020
- Issue Sort Value:
- 2020-0020-2020-0000
- Page Start:
- Page End:
- Publication Date:
- 2020
- Subjects:
- Architecture -- Energy-consumption -- Wireless body sensor networks -- World competitive contest algorithm
Medical informatics -- Periodicals
610.285 - Journal URLs:
- http://www.sciencedirect.com/science/journal/23529148/ ↗
http://www.sciencedirect.com/ ↗ - DOI:
- 10.1016/j.imu.2020.100365 ↗
- Languages:
- English
- ISSNs:
- 2352-9148
- Deposit Type:
- Legaldeposit
- View Content:
- Available online (eLD content is only available in our Reading Rooms) ↗
- Physical Locations:
- British Library DSC - BLDSS-3PM
British Library HMNTS - ELD Digital store - Ingest File:
- 14610.xml