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Special Issue "Energy Harvesting Sensors for Long Term Applications in the IoT Era"

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

Deadline for manuscript submissions: closed (31 August 2017)

Special Issue Editor

Guest Editor
Dr. Davide Brunelli

DII, University of Trento, I-38123 Trento, Italy
Website | E-Mail
Interests: Development of new techniques of Energy Scavenging for embedded electronic systems; Exploring interaction and design issues in embedded personal device and wireless sensor network; Development and optimization of low-power and low-cost Wireless Sensor Networks (WSN) for Climate and Environmental Monitoring; Development of algorithms for position analysis and tracking in indoor environments; Ubiquitous computing oriented to the cooperation through mobile devices and development of applications for portable systems

Special Issue Information

Dear Colleagues,

Smart sensor technologies are gaining popularity thank to the forthcoming IoT (Internet of Things) era: From raw sensor data gathered in the physical world to in situ sensor data processing on IoT devices. Applications, ranging from environmental monitoring, security management, medical applications, smart homes, agricultural and smart cities, are increasingly using IoT sensors. However, ensuring long term operation is still one of the most important challenge of such a technology, because low power sensing devices are becoming increasingly complex systems that combine hardware and firmware.

This call for papers emphasizes the challenges, issues, and opportunities in the research, design and engineering of low power sensing, focusing in techniques, strategies and algorithms applied to real example of IoT applications. It welcomes contribution in deployments and in-field tests and measurement of low power devices, as well as original and not previously-published submissions.

We are looking for contributions which are capable to show remarkable reduction of the overall power consumption and to extended the lifetime of the IoT sensors, as well as in applications that exploit energy-aware or low power algorithms. Emerging research, design, and engineering solutions about energy-efficient sensors which can achieve unlimited operating time, focusing on both techniques, strategies, and algorithms applied to IoT applications are the focus of the call. All submissions must be original and not previously published.

Potential topics include, but are not limited to:

- Power management algorithms for energy harvesting sensing systems
- Ultra-low power communication methodologies
- Experiences from real-world low-power IoT applications and deployments;
- Power management algorithms for energy harvesting sensing systems;
- Architectures for energy-neutral sensing systems;
- Resilient energy-neutral sensors;
- Internet of (battery-less) Things;
- Applications of low power Internet of sensors (IoS);
- Energy harvesting and energy aware design;
- Low power design of the sensors node;
- Energy-efficient algorithms for applications and networks;
- Smartification of sensing applications
- Experiences from real-world low-power IoT applications and deployments

Dr. Davide Brunelli
Guest Editors

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All papers will be peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Sensors is an international peer-reviewed open access monthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 1800 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Published Papers (8 papers)

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Research

Open AccessArticle Autonomous Sensors Powered by Energy Harvesting from von Karman Vortices in Airflow
Sensors 2017, 17(9), 2100; doi:10.3390/s17092100
Received: 3 August 2017 / Revised: 5 September 2017 / Accepted: 8 September 2017 / Published: 13 September 2017
PDF Full-text (6815 KB) | HTML Full-text | XML Full-text
Abstract
In this paper an energy harvesting system based on a piezoelectric converter to extract energy from airflow and use it to power battery-less sensors is presented. The converter is embedded as a part of a flexure beam that is put into vibrations by
[...] Read more.
In this paper an energy harvesting system based on a piezoelectric converter to extract energy from airflow and use it to power battery-less sensors is presented. The converter is embedded as a part of a flexure beam that is put into vibrations by von Karman vortices detached from a bluff body placed upstream. The vortex street has been investigated by Computational Fluid Dynamics (CFD) simulations, aiming at assessing the vortex shedding frequency as a function of the flow velocity. From the simulation results the preferred positioning of the beam behind the bluff body has been derived. In the experimental characterization the electrical output from the converter has been measured for different flow velocities and beam orientations. Highest conversion effectiveness is obtained by an optimal orientation of the beam, to exploit the maximum forcing, and for flow velocities where the repetition frequency of the vortices allows to excite the beam resonant frequency at its first flexural mode. The possibility to power battery-less sensors and make them autonomous has been shown by developing an energy management and signal conditioning electronic circuit plus two sensors for measuring temperature and flow velocity and transmitting their values over a RF signal. A harvested power of about 650 μW with retransmission intervals below 2 min have been obtained for the optimal flow velocity of 4 m/s. Full article
(This article belongs to the Special Issue Energy Harvesting Sensors for Long Term Applications in the IoT Era)
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Open AccessArticle Dual-Source Linear Energy Prediction (LINE-P) Model in the Context of WSNs
Sensors 2017, 17(7), 1666; doi:10.3390/s17071666
Received: 12 May 2017 / Revised: 13 July 2017 / Accepted: 14 July 2017 / Published: 20 July 2017
PDF Full-text (1791 KB) | HTML Full-text | XML Full-text
Abstract
Energy harvesting technologies such as miniature power solar panels and micro wind turbines are increasingly used to help power wireless sensor network nodes. However, a major drawback of energy harvesting is its varying and intermittent characteristic, which can negatively affect the quality of
[...] Read more.
Energy harvesting technologies such as miniature power solar panels and micro wind turbines are increasingly used to help power wireless sensor network nodes. However, a major drawback of energy harvesting is its varying and intermittent characteristic, which can negatively affect the quality of service. This calls for careful design and operation of the nodes, possibly by means of, e.g., dynamic duty cycling and/or dynamic frequency and voltage scaling. In this context, various energy prediction models have been proposed in the literature; however, they are typically compute-intensive or only suitable for a single type of energy source. In this paper, we propose Linear Energy Prediction “LINE-P”, a lightweight, yet relatively accurate model based on approximation and sampling theory; LINE-P is suitable for dual-source energy harvesting. Simulations and comparisons against existing similar models have been conducted with low and medium resolutions (i.e., 60 and 22 min intervals/24 h) for the solar energy source (low variations) and with high resolutions (15 min intervals/24 h) for the wind energy source. The results show that the accuracy of the solar-based and wind-based predictions is up to approximately 98% and 96%, respectively, while requiring a lower complexity and memory than the other models. For the cases where LINE-P’s accuracy is lower than that of other approaches, it still has the advantage of lower computing requirements, making it more suitable for embedded implementation, e.g., in wireless sensor network coordinator nodes or gateways. Full article
(This article belongs to the Special Issue Energy Harvesting Sensors for Long Term Applications in the IoT Era)
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Open AccessArticle A Smart and Balanced Energy-Efficient Multihop Clustering Algorithm (Smart-BEEM) for MIMO IoT Systems in Future Networks
Sensors 2017, 17(7), 1574; doi:10.3390/s17071574
Received: 30 May 2017 / Revised: 26 June 2017 / Accepted: 1 July 2017 / Published: 5 July 2017
Cited by 1 | PDF Full-text (651 KB) | HTML Full-text | XML Full-text
Abstract
Wireless Sensor Networks (WSNs) are typically composed of thousands of sensors powered by limited energy resources. Clustering techniques were introduced to prolong network longevity offering the promise of green computing. However, most existing work fails to consider the network coverage when evaluating the
[...] Read more.
Wireless Sensor Networks (WSNs) are typically composed of thousands of sensors powered by limited energy resources. Clustering techniques were introduced to prolong network longevity offering the promise of green computing. However, most existing work fails to consider the network coverage when evaluating the lifetime of a network. We believe that balancing the energy consumption in per unit area rather than on each single sensor can provide better-balanced power usage throughout the network. Our former work—Balanced Energy-Efficiency (BEE) and its Multihop version BEEM can not only extend the network longevity, but also maintain the network coverage. Following WSNs, Internet of Things (IoT) technology has been proposed with higher degree of diversities in terms of communication abilities and user scenarios, supporting a large range of real world applications. The IoT devices are embedded with multiple communication interfaces, normally referred as Multiple-In and Multiple-Out (MIMO) in 5G networks. The applications running on those devices can generate various types of data. Every interface has its own characteristics, which may be preferred and beneficial in some specific user scenarios. With MIMO becoming more available on the IoT devices, an advanced clustering solution for highly dynamic IoT systems is missing and also pressingly demanded in order to cater for differing user applications. In this paper, we present a smart clustering algorithm (Smart-BEEM) based on our former work BEE(M) to accomplish energy efficient and Quality of user Experience (QoE) supported communication in cluster based IoT networks. It is a user behaviour and context aware approach, aiming to facilitate IoT devices to choose beneficial communication interfaces and cluster headers for data transmission. Experimental results have proved that Smart-BEEM can further improve the performance of BEE and BEEM for coverage sensitive longevity. Full article
(This article belongs to the Special Issue Energy Harvesting Sensors for Long Term Applications in the IoT Era)
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Open AccessArticle Joint Transmit Power Allocation and Splitting for SWIPT Aided OFDM-IDMA in Wireless Sensor Networks
Sensors 2017, 17(7), 1566; doi:10.3390/s17071566
Received: 5 June 2017 / Revised: 30 June 2017 / Accepted: 1 July 2017 / Published: 4 July 2017
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Abstract
In this paper, we propose to combine Orthogonal Frequency Division Multiplexing-Interleave Division Multiple Access (OFDM-IDMA) with Simultaneous Wireless Information and Power Transfer (SWIPT), resulting in SWIPT aided OFDM-IDMA scheme for power-limited sensor networks. In the proposed system, the Receive Node (RN) applies Power
[...] Read more.
In this paper, we propose to combine Orthogonal Frequency Division Multiplexing-Interleave Division Multiple Access (OFDM-IDMA) with Simultaneous Wireless Information and Power Transfer (SWIPT), resulting in SWIPT aided OFDM-IDMA scheme for power-limited sensor networks. In the proposed system, the Receive Node (RN) applies Power Splitting (PS) to coordinate the Energy Harvesting (EH) and Information Decoding (ID) process, where the harvested energy is utilized to guarantee the iterative Multi-User Detection (MUD) of IDMA to work under sufficient number of iterations. Our objective is to minimize the total transmit power of Source Node (SN), while satisfying the requirements of both minimum harvested energy and Bit Error Rate (BER) performance from individual receive nodes. We formulate such a problem as a joint power allocation and splitting one, where the iteration number of MUD is also taken into consideration as the key parameter to affect both EH and ID constraints. To solve it, a sub-optimal algorithm is proposed to determine the power profile, PS ratio and iteration number of MUD in an iterative manner. Simulation results verify that the proposed algorithm can provide significant performance improvement. Full article
(This article belongs to the Special Issue Energy Harvesting Sensors for Long Term Applications in the IoT Era)
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Open AccessArticle Improved Scheduling Mechanisms for Synchronous Information and Energy Transmission
Sensors 2017, 17(6), 1343; doi:10.3390/s17061343
Received: 14 March 2017 / Revised: 1 June 2017 / Accepted: 7 June 2017 / Published: 9 June 2017
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Abstract
Wireless energy collecting technology can effectively reduce the network time overhead and prolong the wireless sensor network (WSN) lifetime. However, the traditional energy collecting technology cannot achieve the balance between ergodic channel capacity and average collected energy. In order to solve the problem
[...] Read more.
Wireless energy collecting technology can effectively reduce the network time overhead and prolong the wireless sensor network (WSN) lifetime. However, the traditional energy collecting technology cannot achieve the balance between ergodic channel capacity and average collected energy. In order to solve the problem of the network transmission efficiency and the limited energy of wireless devices, three improved scheduling mechanisms are proposed: improved signal noise ratio (SNR) scheduling mechanism (IS2M), improved N-SNR scheduling mechanism (INS2M) and an improved Equal Throughput scheduling mechanism (IETSM) for different channel conditions to improve the whole network performance. Meanwhile, the average collected energy of single users and the ergodic channel capacity of three scheduling mechanisms can be obtained through the order statistical theory in Rayleig, Ricean, Nakagami-m and Weibull fading channels. It is concluded that the proposed scheduling mechanisms can achieve better balance between energy collection and data transmission, so as to provide a new solution to realize synchronous information and energy transmission for WSNs. Full article
(This article belongs to the Special Issue Energy Harvesting Sensors for Long Term Applications in the IoT Era)
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Open AccessArticle Joint Resource Allocation of Spectrum Sensing and Energy Harvesting in an Energy-Harvesting-Based Cognitive Sensor Network
Sensors 2017, 17(3), 600; doi:10.3390/s17030600
Received: 11 February 2017 / Revised: 10 March 2017 / Accepted: 13 March 2017 / Published: 16 March 2017
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Abstract
The cognitive sensor (CS) can transmit data to the control center in the same spectrum that is licensed to the primary user (PU) when the absence of the PU is detected by spectrum sensing. However, the battery energy of the CS is limited
[...] Read more.
The cognitive sensor (CS) can transmit data to the control center in the same spectrum that is licensed to the primary user (PU) when the absence of the PU is detected by spectrum sensing. However, the battery energy of the CS is limited due to its small size, deployment in atrocious environments and long-term working. In this paper, an energy-harvesting-based CS is described, which senses the PU together with collecting the radio frequency energy to supply data transmission. In order to improve the transmission performance of the CS, we have proposed the joint resource allocation of spectrum sensing and energy harvesting in the cases of a single energy-harvesting-based CS and an energy-harvesting-based cognitive sensor network (CSN), respectively. Based on the proposed frame structure, we have formulated the resource allocation as a class of joint optimization problems, which seek to maximize the transmission rate of the CS by jointly optimizing sensing time, harvesting time and the numbers of sensing nodes and harvesting nodes. Using the half searching method and the alternating direction optimization, we have achieved the sub-optimal solution by converting the joint optimization problem into several convex sub-optimization problems. The simulation results have indicated the predominance of the proposed energy-harvesting-based CS and CSN models. Full article
(This article belongs to the Special Issue Energy Harvesting Sensors for Long Term Applications in the IoT Era)
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Open AccessArticle Enhanced Passive RF-DC Converter Circuit Efficiency for Low RF Energy Harvesting
Sensors 2017, 17(3), 546; doi:10.3390/s17030546
Received: 9 January 2017 / Revised: 3 March 2017 / Accepted: 6 March 2017 / Published: 9 March 2017
Cited by 3 | PDF Full-text (7112 KB) | HTML Full-text | XML Full-text
Abstract
For radio frequency energy transmission, the conversion efficiency of the receiver is decisive not only for reducing sending power, but also for enabling energy transmission over long and variable distances. In this contribution, we present a passive RF-DC converter for energy harvesting at
[...] Read more.
For radio frequency energy transmission, the conversion efficiency of the receiver is decisive not only for reducing sending power, but also for enabling energy transmission over long and variable distances. In this contribution, we present a passive RF-DC converter for energy harvesting at ultra-low input power at 868 MHz. The novel converter consists of a reactive matching circuit and a combined voltage multiplier and rectifier. The stored energy in the input inductor and capacitance, during the negative wave, is conveyed to the output capacitance during the positive one. Although Dickson and Villard topologies have principally comparable efficiency for multi-stage voltage multipliers, the Dickson topology reaches a better efficiency within the novel ultra-low input power converter concept. At the output stage, a low-pass filter is introduced to reduce ripple at high frequencies in order to realize a stable DC signal. The proposed rectifier enables harvesting energy at even a low input power from −40 dBm for a resistive load of 50 kΩ. It realizes a significant improvement in comparison with state of the art solutions. Full article
(This article belongs to the Special Issue Energy Harvesting Sensors for Long Term Applications in the IoT Era)
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Open AccessArticle Dynamic Voltage-Frequency and Workload Joint Scaling Power Management for Energy Harvesting Multi-Core WSN Node SoC
Sensors 2017, 17(2), 310; doi:10.3390/s17020310
Received: 9 January 2017 / Revised: 2 February 2017 / Accepted: 2 February 2017 / Published: 8 February 2017
Cited by 1 | PDF Full-text (1895 KB) | HTML Full-text | XML Full-text | Supplementary Files
Abstract
This paper proposes a scheduling and power management solution for energy harvesting heterogeneous multi-core WSN node SoC such that the system continues to operate perennially and uses the harvested energy efficiently. The solution consists of a heterogeneous multi-core system oriented task scheduling algorithm
[...] Read more.
This paper proposes a scheduling and power management solution for energy harvesting heterogeneous multi-core WSN node SoC such that the system continues to operate perennially and uses the harvested energy efficiently. The solution consists of a heterogeneous multi-core system oriented task scheduling algorithm and a low-complexity dynamic workload scaling and configuration optimization algorithm suitable for light-weight platforms. Moreover, considering the power consumption of most WSN applications have the characteristic of data dependent behavior, we introduce branches handling mechanism into the solution as well. The experimental result shows that the proposed algorithm can operate in real-time on a lightweight embedded processor (MSP430), and that it can make a system do more valuable works and make more than 99.9% use of the power budget. Full article
(This article belongs to the Special Issue Energy Harvesting Sensors for Long Term Applications in the IoT Era)
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