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Energy Harvesting Sensor Systems 2021-2023

A special issue of Sensors (ISSN 1424-8220). This special issue belongs to the section "Sensor Networks".

Deadline for manuscript submissions: closed (20 January 2023) | Viewed by 8520

Special Issue Editor

Department of Electronics Design, Mid Sweden University, 85170 Sundsvall, Sweden
Interests: energy harvesting; low-power embedded systems; sensor systems; sensor networks; autonomous systems; system modeling
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Autonomous sensor systems and networks are predicted to become integral technologies in a wide range of applications—from industrial automation to structural monitoring and smart cities. In many of these applications, the system needs to operate for long periods of time without access to a fixed power supply. With considerable maintenance, a high strain on the environment and strict limitations on operating conditions, existing battery technologies are a not desirable option in the long term.

Energy harvesting has become a competitive alternative for the supply of low-power electronic systems, utilizing ambient energy sources in the form of kinetic movements, thermal gradients or electromagnetic radiation. A number of commercial products are now available, but significant challenges still exist within the field: from more efficient or robust energy harvesters, through to the effective design and integration of systems, to the functionality of energy harvesting-powered applications.

In this Special Issue, we invite you to submit contributions covering any area of energy harvesting for sensor systems. This includes transducer design and optimization; system design, modeling and integration; and experimental verifications, case studies and field tests. Contributions supported by experimental results are particularly welcomed.

Dr. Sebastian Bader
Guest Editor

Manuscript Submission Information

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Published Papers (4 papers)

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19 pages, 355 KiB  
Article
Energy Harvesting and Task-Aware Multi-Robot Task Allocation in Robotic Wireless Sensor Networks
by Omer Melih Gul
Sensors 2023, 23(6), 3284; https://0-doi-org.brum.beds.ac.uk/10.3390/s23063284 - 20 Mar 2023
Cited by 4 | Viewed by 1240
Abstract
In this work, we investigate an energy-aware multi-robot task-allocation (MRTA) problem in a cluster of the robot network that consists of a base station and several clusters of energy-harvesting (EH) robots. It is assumed that there are M+1 robots in the [...] Read more.
In this work, we investigate an energy-aware multi-robot task-allocation (MRTA) problem in a cluster of the robot network that consists of a base station and several clusters of energy-harvesting (EH) robots. It is assumed that there are M+1 robots in the cluster and M tasks exist in each round. In the cluster, a robot is elected as the cluster head, which assigns one task to each robot in that round. Its responsibility (or task) is to collect the resultant data from the remaining M robots to aggregate and transmit directly to the BS. This paper aims to allocate the M tasks to the remaining M robots optimally or near optimally by considering the distance to be traveled by each node, the energy required for executing each task, the battery level at each node, and the energy-harvesting capabilities of the nodes. Then, this work presents three algorithms: Classical MRTA Approach, Task-aware MRTA Approach, EH and Task-aware MRTA Approach. The performances of the proposed MRTA algorithms are evaluated under both independent and identically distributed (i.i.d.) and Markovian energy-harvesting processes for different scenarios with five robots and 10 robots (with the same number of tasks). EH and Task-aware MRTA Approach shows the best performance among all MRTA approaches by keeping up to 100% more energy in the battery than the Classical MRTA Approach and keeping up to 20% more energy in the battery than the Task-aware MRTA Approach. Full article
(This article belongs to the Special Issue Energy Harvesting Sensor Systems 2021-2023)
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24 pages, 2475 KiB  
Article
Performance Analysis of Dual-Hop AF Relaying with Non-Linear/Linear Energy Harvesting
by Mohammadreza Babaei, Lütfiye Durak-Ata and Ümit Aygölü
Sensors 2022, 22(16), 5987; https://0-doi-org.brum.beds.ac.uk/10.3390/s22165987 - 10 Aug 2022
Cited by 1 | Viewed by 1526
Abstract
Massive device-to-device communication nodes and Internet of Things (IoT) devices are expected to be crucial components in next-generation wireless networks. However, the energy constraint of these nodes presents a challenge since the energy of the batteries is limited. Motivated by this, radio frequency [...] Read more.
Massive device-to-device communication nodes and Internet of Things (IoT) devices are expected to be crucial components in next-generation wireless networks. However, the energy constraint of these nodes presents a challenge since the energy of the batteries is limited. Motivated by this, radio frequency energy harvesting (EH) has been developed as an efficient strategy to overcome the energy constraint of IoT devices and sensor networks. In this paper, a wireless-powered dual-hop amplify-and-forward relaying system, in the absence of a direct link between the source (S) and the destination (D), is considered. It is assumed that a dedicated power beacon (PB) transmits an energy-bearing signal from which the power-constrained S and relay (R) harvest energy. Theoretical derivations of bit error probability, outage probability, and throughput expressions are performed for both linear and non-linear energy harvesting models. Moreover, the theoretical results provided for different system parameters are validated via Monte Carlo simulations. The obtained results reveal the difference between the realistic non-linear EH model and the conventional linear EH model, which overestimates the system performance at high levels of harvested energy. Thus, it leads to misunderstanding the real performance of the EH systems. However, at low levels of harvested energy, both models behave similarly and provide realistic results. Full article
(This article belongs to the Special Issue Energy Harvesting Sensor Systems 2021-2023)
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16 pages, 3400 KiB  
Article
Battery-Less Environment Sensor Using Thermoelectric Energy Harvesting from Soil-Ambient Air Temperature Differences
by Priyesh Pappinisseri Puluckul and Maarten Weyn
Sensors 2022, 22(13), 4737; https://0-doi-org.brum.beds.ac.uk/10.3390/s22134737 - 23 Jun 2022
Cited by 6 | Viewed by 2140
Abstract
Energy harvesting is an effective technique for prolonging the lifetime of Internet of Things devices and Wireless Sensor Networks. In applications such as environmental sensing, which demands a deploy-and-forget architecture, energy harvesting is an unavoidable technology. Thermal energy is one of the most [...] Read more.
Energy harvesting is an effective technique for prolonging the lifetime of Internet of Things devices and Wireless Sensor Networks. In applications such as environmental sensing, which demands a deploy-and-forget architecture, energy harvesting is an unavoidable technology. Thermal energy is one of the most widely used sources for energy harvesting. A thermal energy harvester can convert a thermal gradient into electrical energy. Thus, the temperature difference between the soil and air could act as a vital source of energy for an environmental sensing device. In this paper, we present a proof-of-concept design of an environmental sensing node that harvests energy from soil temperature and uses the DASH7 communication protocol for connectivity. We evaluate the soil temperature and air temperature based on the data collected from two locations: one in Belgium and the other in Iceland. Using these datasets, we calculate the amount of energy that is producible from both of these sites. We further design power management and monitoring circuit and use a supercapacitor as the energy storage element, hence making it battery-less. Finally, we deploy the proof-of-concept prototype in the field and evaluate its performance. We demonstrate that the system can harvest, on average, 178.74 mJ and is enough to perform at least 5 DASH7 transmissions and 100 sensing tasks per day. Full article
(This article belongs to the Special Issue Energy Harvesting Sensor Systems 2021-2023)
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18 pages, 1754 KiB  
Article
Design Optimization and Comparison of Cylindrical Electromagnetic Vibration Energy Harvesters
by Tra Nguyen Phan, Jesus Javier Aranda, Bengt Oelmann and Sebastian Bader
Sensors 2021, 21(23), 7985; https://0-doi-org.brum.beds.ac.uk/10.3390/s21237985 - 30 Nov 2021
Cited by 10 | Viewed by 2334
Abstract
Investigating the coil–magnet structure plays a significant role in the design process of the electromagnetic energy harvester due to the effect on the harvester’s performance. In this paper, the performance of four different electromagnetic vibration energy harvesters with cylindrical shapes constrained in the [...] Read more.
Investigating the coil–magnet structure plays a significant role in the design process of the electromagnetic energy harvester due to the effect on the harvester’s performance. In this paper, the performance of four different electromagnetic vibration energy harvesters with cylindrical shapes constrained in the same volume were under investigation. The utilized structures are (i) two opposite polarized magnets spaced by a mild steel; (ii) a Halbach array with three magnets and one coil; (iii) a Halbach array with five magnets and one coil; and (iv) a Halbach array with five magnets and three coils. We utilized a completely automatic optimization procedure with the help of an optimization algorithm implemented in Python, supported by simulations in ANSYS Maxwell and MATLAB Simulink to obtain the maximum output power for each configuration. The simulation results show that the Halbach array with three magnets and one coil is the best for configurations with the Halbach array. Additionally, among all configurations, the harvester with two opposing magnets provides the highest output power and volume power density, while the Halbach array with three magnets and one coil provides the highest mass power density. The paper also demonstrates limitations of using the electromagnetic coupling coefficient as a metric for harvester optimization, if the ultimate goal is maximization of output power. Full article
(This article belongs to the Special Issue Energy Harvesting Sensor Systems 2021-2023)
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