Special Issue: Autonomous Low Power Monitoring Sensors

Kuru, Kaya orcid iconORCID: 0000-0002-4279-4166, Erogul, Osman and Xavier, Chavanne (2021) Special Issue: Autonomous Low Power Monitoring Sensors. MDPI.

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Official URL: https://www.mdpi.com/journal/sensors/special_issue...


Geo-distributed autonomous low-power sensing nodes – e.g., wireless sensor networks (WSN) and Internet of Things (IoT) networks - are harbouring more autonomous capabilities in the concepts of Internet of Everything (IoE) and Automation of Everything (AoE) [1] to serve more intelligent multiple larger synergistic cyber-physical systems (CPS) that perform more complex autonomous tasks by removing the human out of the loop [2]. They can be useful for scientific works, such as to build reliable climate models, by providing continuous series of some in-situ environment variables over years at high frequency (e.g. soil moisture in a catchment at 1 point/10 min). Due to spatial heterogeneity variables have to be retrieved at different points, which requires a network of such sensors over large area (> 1 km2 every 100 m)

However, the capabilities of sensor nodes within these domains and others are still highly limited concerning energy, storage, processing, sensing coverage, measurement quality, robustness, communication with risk of loss of data (e.g., network connectivity, low data rate in low-powered wide-area network (LPWAN)), latency, costs and cybersecurity [3]. These limitations, or contradictory requirements, need to be mitigated, partly or not depending on the sensor purpose. Moreover, energy-efficient deployment and data routing of low-power sensor nodes to lessen their energy consumption is of prime importance for prolonging their own network lifetime. With these in mind, this Special Issue covers topics related to autonomous resource-constraint sensor nodes, particularly, focuses on research attempts to improve their capabilities with increasing autonomous abilities leading to efficient adaption to their environment and meeting the different requirements.

We would like to invite the academic and industrial research community to submit original research as well as review articles to this Special Issue. Topics of interest include:

Main Topics:

- Low cost sensor
- Quality measurement
- Rugged autonomous sensor
- Autonomous resource-constraint sensor nodes
- Design and development of autonomous low-power sensors
- Energy-efficient deployment and data routing of low-power sensor nodes
- WSN communication topology
- Intelligent energy harvesting (EH) in autonomous low power monitoring sensors
- Self-prognosis, self-diagnosis and self-healing autonomous monitoring sensors
- Sensor networks
- Lifespan of a sensor network
- Multi low-power sensor integration
- End-to-end wireless communication of autonomous monitoring sensors
- Mobile data collection sinks in sensor networks
- Cybersecurity in sensors
- Miniaturized sensors
- Autonomous health monitoring (i.e., fault diagnosis) of remotely performing critical devices (e.g., power outage avoidance systems, condition monitoring of wind turbines or railways)
- Medical and biomedical sensing with low-power sensors
- Implementation of wireless sensors in urban environment, industry, agriculture, remote observatory
- Implementation of low-power sensors in the cloud and edge computing
- Indoor implementation of wireless sensors (e.g, security, fire monitoring, energy efficiency of buildings and structures)
- Disaster management using low-power sensors (flood, earthquake, tsunami)
- Computational intelligence in sensors
- Big data management in geo-distributed sensors
- Intelligent vision-based low-power sensors

1. K. Kuru and H. Yetgin, "Transformation to Advanced Mechatronics Systems Within New Industrial Revolution: A Novel Framework in Automation of Everything (AoE)," in IEEE Access, vol. 7, pp. 41395-41415, 2019. https://doi.org/10.1109/ACCESS.2019.2907809
2. K. Kuru, “Management of geo-distributed intelligence: Deep Insight as a Service (DINSaaS) on Forged Cloud Platforms (FCP),” in Journal of Parallel and Distributed Computing, 149 . pp. 103-118, 2021. https://doi.org/10.1016/j.jpdc.2020.11.009
3. K. Kuru, "Planning the Future of Smart Cities with Swarms of Fully Autonomous Unmanned Aerial Vehicles using a Novel Framework," in IEEE Access, vol. 9, pp. 6571-6595, 2021. https://doi.org/10.1109/ACCESS.2020.3049094

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