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Green Sensors Networking
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Green Sensors Networking

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

Deadline for manuscript submissions: closed (30 June 2021) | Viewed by 18989

Special Issue Editors

Dr. Sergio Herrería Alonso

E-Mail Website
Guest Editor
atlanTTic Research Center for Telecommunications Technologies, Universidade de Vigo, 36310 Vigo, Spain
Interests: green networking; quality of service in the internet; performance analysis of computer networks; ICNs & NDN
Special Issues, Collections and Topics in MDPI journals
Dr. Miguel Rodríguez Pérez

E-Mail Website
Guest Editor
Department of Telematics Engineering, University of Vigo, 36310 Vigo, Spain
Interests: green networking; virtualization of network functions and services
Special Issues, Collections and Topics in MDPI journals
Prof. Dr. Rosario Giuseppe Garroppo

E-Mail Website
Guest Editor
Dipartimento di Ingegneria dell'Informazione, University of Pisa, Pisa, Italy
Interests: green networks; traffic optimization; traffic control and monitoring in cellular systems; QoE guarantee for MoIP services; routing in WMN; machine learning algorithms for network functions, IIoT communication technologies
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Recent improvements in integrated circuit technology have led to the development of new low-cost, tiny sensor nodes. Consequently, wireless sensor networks (WSNs) are increasingly being introduced into many different application areas, such as smart homes, smart cities, healthcare, wearables, transportation, security, surveillance, and other industrial needs. WSNs consist of a large number of self-sustainable, autonomous devices that are able to sense their environment and process and transmit related data. Sensor nodes are commonly hardware-constrained devices that must work under severe resource restrictions such as limited battery, communication, storage, and computing capabilities. In particular, to cope with the scarcity of available energy and to ensure long-term operation, sensor nodes must be designed with low energy radio and sensing units, employ energy efficient communication protocols, and implement optimal energy management policies.

The key for reducing energy consumption in WSNs is the wise utilization of network resources, including power, spectrum, time, and spatial resources. For example, in these networks involving a large number of distributed devices, cooperative power control techniques based on convex optimization or game theory permit governing transmission power efficiently. Furthermore, energy-harvesting technology is frequently used to provide a virtually uninterrupted power supply to the sensor nodes although, due to the intermittent and variable nature of ambient energy sources, accurate prediction schemes of future energy availability may be required to avoid energy shortages.

Energy demands of WSNs can also be reduced, alleviating mutual interference through efficient MAC protocols, employing energy-aware routing algorithms or exploiting available power-saving modes at the sensor nodes. Finally, advanced distributed storage and computation schemes that decrease the number of transmissions and receptions for data collection and dissemination can be adopted to save energy in these networks.

The purpose of this Special Issue is to present the most recent advances, or comprehensive reviews, relating to green wireless sensor networks. Potential topics include but are not limited to:

  • Energy harvesting;
  • Distributed power control schemes;
  • Energy efficient WSN architecture and design;
  • Energy efficient communication protocols for WSNs;
  • Green MAC protocols for WSNs;
  • Green routing algorithms for WSNs;
  • Green device-to-device communications;
  • Green computing for sensor nodes;
  • Energy management policies for sensor nodes;
  • Energy availability prediction schemes.

Dr. Sergio Herrería Alonso
Dr. Miguel Rodríguez Pérez
Dr. Rosario Giuseppe Garroppo
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 submissions that pass pre-check are 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 semimonthly 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 2600 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.

Keywords

  • Wireless sensor networks
  • Energy harvesting
  • Energy efficient data communication
  • Energy management
  • Energy prediction

Published Papers (7 papers)

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Research

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17 pages, 1242 KiB  
Article
Energy-Efficient Wireless Communication Strategy for Precision Agriculture Irrigation Control
by Camilo Lozoya, Antonio Favela-Contreras, Alberto Aguilar-Gonzalez, L.C. Félix-Herrán and Luis Orona
Sensors 2021, 21(16), 5541; https://doi.org/10.3390/s21165541 - 18 Aug 2021
Cited by 7 | Viewed by 2338
Abstract
In smart farming, precision agriculture irrigation is essential to reduce water consumption and produce higher crop yields. Closed-loop irrigation based on soil moisture measurements has demonstrated the capability to achieve a considerable amount of water savings while growing healthy crops. Automated irrigation systems [...] Read more.
In smart farming, precision agriculture irrigation is essential to reduce water consumption and produce higher crop yields. Closed-loop irrigation based on soil moisture measurements has demonstrated the capability to achieve a considerable amount of water savings while growing healthy crops. Automated irrigation systems are typically implemented over wireless sensor networks, where the sensing devices are battery-powered, and thus they have to manage energy constraints by implementing efficient communication schemas. Self-triggered control is an aperiodic sampling strategy capable of reducing the number of networked messages compared to traditional periodical sampling. In this paper, we propose an energy-efficient communication strategy for closed-loop control irrigation, implemented over a wireless sensor network, where event-driven soil moisture measurements are conducted by the sensing devices only when needed. Thereby, the self-triggered algorithm estimates the occurrence of the next sampling period based on the process dynamics. The proposed strategy was evaluated in a pecan crop field and compared with periodical sampling implementations. The experimental results show that the proposed adaptive sampling rate technique decreased the number of communication messages more than 85% and reduced power consumption up to 20%, while still accomplishing the system control objectives in terms of the irrigation efficiency and water consumption. Full article
(This article belongs to the Special Issue Green Sensors Networking)
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17 pages, 425 KiB  
Article
Efficient Wind Speed Forecasting for Resource-Constrained Sensor Devices
by Sergio Herrería-Alonso, Andrés Suárez-González, Miguel Rodríguez-Pérez, Raúl F. Rodríguez-Rubio and Cándido López-García
Sensors 2021, 21(3), 983; https://doi.org/10.3390/s21030983 - 02 Feb 2021
Cited by 3 | Viewed by 2064
Abstract
Wind energy harvesting technology is one of the most popular power sources for wireless sensor networks. However, given its irregular nature, wind energy availability experiences significant variations and, therefore, wind-powered devices need reliable forecasting models to effectively adjust their energy consumption to the [...] Read more.
Wind energy harvesting technology is one of the most popular power sources for wireless sensor networks. However, given its irregular nature, wind energy availability experiences significant variations and, therefore, wind-powered devices need reliable forecasting models to effectively adjust their energy consumption to the dynamics of energy harvesting. On the other hand, resource-constrained devices with limited hardware capacities (such as sensor nodes) must resort to forecasting schemes of low complexity for their predictions in order to avoid squandering their scarce power and computing capabilities. In this paper, we present a new efficient ARIMA-based forecasting model for predicting wind speed at short-term horizons. The performance results obtained using real data sets show that the proposed ARIMA model can be an excellent choice for wind-powered sensor nodes due to its potential for achieving accurate enough predictions with very low computational burden and memory overhead. In addition, it is very simple to setup, since it can dynamically adapt to varying wind conditions and locations without requiring any particular reconfiguration or previous data training phase for each different scenario. Full article
(This article belongs to the Special Issue Green Sensors Networking)
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16 pages, 3145 KiB  
Article
Demand Management for Optimized Energy Usage and Consumer Comfort Using Sequential Optimization
by Mikhak Samadi, Javad Fattahi, Henry Schriemer and Melike Erol-Kantarci
Sensors 2021, 21(1), 130; https://doi.org/10.3390/s21010130 - 28 Dec 2020
Cited by 13 | Viewed by 2113
Abstract
The Energy-efficiency of demand management technologies and customer’s experience have emerged as important issues as consumers began to heavily adopt these technologies. In this context, where the electrical load imposed on the smart grid by residential users needs to be optimized, it can [...] Read more.
The Energy-efficiency of demand management technologies and customer’s experience have emerged as important issues as consumers began to heavily adopt these technologies. In this context, where the electrical load imposed on the smart grid by residential users needs to be optimized, it can be better managed when customer’s comfort parameters are used, such as thermal comfort and preferred appliance usage time interval. In this paper a multi-layer architecture is proposed that uses a multi-objective optimization model at the energy consumption level to take consumer comfort and experience into consideration. The paper shows how our proposed Clustered Sequential Management (CSM) approach could improve consumer comfort via appliance use scheduling. To quantify thermal comfort, we use thermodynamic solutions for a Heating Ventilation and Air Conditioner (HVAC) system and then apply our scheduling model to find the best time slot for such thermal loads, linking consumer experience to power consumption. In addition to thermal loads, we also include non-thermal loads in the cost minimization and the enhanced consumer experience. In this hierarchal algorithm, we classified appliances by their load profile including degrees of freedom for consumer appliance prioritization. Finally, we scheduled consumption within a Time of Use (ToU) pricing model. In this model, we used Mixed Integer Linear Programming (MILP) and Linear Programming (LP) optimization for different categories with different constraints for various loads. We eliminate the customer’s inconvenience on thermal load considering ASHRAE standard, increase the satisfaction on EV optimal chagrining constrained by minimum cost and achieve the preferred usage time for the non-interruptible deferrable loads. The results show that our model is typically able to achieve cost minimization almost equal to 13% and Peak-to-Average Ratios (PAR) reduction with almost 45%. Full article
(This article belongs to the Special Issue Green Sensors Networking)
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19 pages, 1704 KiB  
Article
An Energy Efficient Sink Location Service for Continuous Objects in Wireless Sensor Networks
by Cheonyong Kim, Sangdae Kim, Hyunchong Cho, Sangha Kim and Seungmin Oh
Sensors 2020, 20(24), 7282; https://doi.org/10.3390/s20247282 - 18 Dec 2020
Cited by 3 | Viewed by 1570
Abstract
In wireless sensor networks (WSNs), detection and report of continuous object, such as forest fire and toxic gas leakage, is one of the major applications. In large-scale continuous object tracking in WSNs, there might be many source nodes simultaneously, detecting the continuous object. [...] Read more.
In wireless sensor networks (WSNs), detection and report of continuous object, such as forest fire and toxic gas leakage, is one of the major applications. In large-scale continuous object tracking in WSNs, there might be many source nodes simultaneously, detecting the continuous object. Each nodes reports its data to both a base station and mobile workers in the industry field. For communication between the source nodes and a mobile worker, sink location service is needed to continuously notify the location of the mobile worker. But, as the application has a large number of sources, it causes a waste of energy consumption. To address this issue, in this paper, we propose a two-phase sink location service scheme. In the first phase, the proposed scheme constructs a virtual grid structure for merging the source nodes. Then, the proposed scheme aggregates the merging points from an originated merging point as the second phase. Simulation results show that the proposed scheme is superior to other schemes in terms of energy consumption. Full article
(This article belongs to the Special Issue Green Sensors Networking)
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19 pages, 2168 KiB  
Article
Energy-Efficient Connected-Coverage Scheme in Wireless Sensor Networks
by Yun Xu, Wanguo Jiao and Mengqiu Tian
Sensors 2020, 20(21), 6127; https://doi.org/10.3390/s20216127 - 28 Oct 2020
Cited by 9 | Viewed by 2522
Abstract
In the wireless sensor network, the lifetime of the network can be prolonged by improving the efficiency of limited energy. Existing works achieve better energy utilization, either through node scheduling or routing optimization. In this paper, an efficient solution combining node scheduling with [...] Read more.
In the wireless sensor network, the lifetime of the network can be prolonged by improving the efficiency of limited energy. Existing works achieve better energy utilization, either through node scheduling or routing optimization. In this paper, an efficient solution combining node scheduling with routing protocol optimization is proposed in order to improve the network lifetime. Firstly, to avoid the redundant coverage, a node scheduling scheme that is based on a genetic algorithm is proposed to find the minimum number of sensor nodes to monitor all target points. Subsequently, the algorithm prolongs the lifetime of the network through choosing redundant sleep nodes to replace the dead node. Based on the obtained minimum coverage set, a new routing protocol, named Improved-Distributed Energy-Efficient Clustering (I-DEEC), is proposed. When considering the energy and the distance of the sensor node to the sink, a new policy choosing the cluster head is proposed. To make the energy load more balanced, uneven clusters are constructed. Meanwhile, the data communication way of sensor nodes around the sink is also optimized. The simulation results show that the proposed sensor node scheduling algorithm can reduce the number of redundant sensor nodes, while the I-DEEC routing protocol can improve the energy efficiency of data transmission. The lifetime of the network is greatly extended. Full article
(This article belongs to the Special Issue Green Sensors Networking)
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Review

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34 pages, 614 KiB  
Review
Current Trends on Green Wireless Sensor Networks
by J. Carlos López-Ardao, Raúl F. Rodríguez-Rubio, Andrés Suárez-González, Miguel Rodríguez-Pérez and M. Estrella Sousa-Vieira
Sensors 2021, 21(13), 4281; https://doi.org/10.3390/s21134281 - 23 Jun 2021
Cited by 22 | Viewed by 5276
Abstract
The issue of energy balancing in Wireless Sensor Networks is a pivotal one, crucial in their deployment. This problem can be subdivided in three areas: (i) energy conservation techniques, usually implying minimizing the cost of communication at the nodes since it is known [...] Read more.
The issue of energy balancing in Wireless Sensor Networks is a pivotal one, crucial in their deployment. This problem can be subdivided in three areas: (i) energy conservation techniques, usually implying minimizing the cost of communication at the nodes since it is known that the radio is the biggest consumer of the available energy; (ii) energy-harvesting techniques, converting energy from not full-time available environmental sources and usually storing it; and (iii) energy transfer techniques, sharing energy resources from one node (either specialized or not) to another one. In this article, we survey the main contributions in these three areas and identify the main trending topics in recent research. A discussion and some future directions are also included. Full article
(This article belongs to the Special Issue Green Sensors Networking)
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25 pages, 1075 KiB  
Review
Opportunistic Large Array Propagation Models: A Comprehensive Survey
by Farhan Nawaz, Hemant Kumar, Syed Ali Hassan and Haejoon Jung
Sensors 2021, 21(12), 4206; https://doi.org/10.3390/s21124206 - 19 Jun 2021
Cited by 1 | Viewed by 2168
Abstract
Enabled by the fifth-generation (5G) and beyond 5G communications, large-scale deployments of Internet-of-Things (IoT) networks are expected in various application fields to handle massive machine-type communication (mMTC) services. Device-to-device (D2D) communications can be an effective solution in massive IoT networks to overcome the [...] Read more.
Enabled by the fifth-generation (5G) and beyond 5G communications, large-scale deployments of Internet-of-Things (IoT) networks are expected in various application fields to handle massive machine-type communication (mMTC) services. Device-to-device (D2D) communications can be an effective solution in massive IoT networks to overcome the inherent hardware limitations of small devices. In such D2D scenarios, given that a receiver can benefit from the signal-to-noise-ratio (SNR) advantage through diversity and array gains, cooperative transmission (CT) can be employed, so that multiple IoT nodes can create a virtual antenna array. In particular, Opportunistic Large Array (OLA), which is one type of CT technique, is known to provide fast, energy-efficient, and reliable broadcasting and unicasting without prior coordination, which can be exploited in future mMTC applications. However, OLA-based protocol design and operation are subject to network models to characterize the propagation behavior and evaluate the performance. Further, it has been shown through some experimental studies that the most widely-used model in prior studies on OLA is not accurate for networks with networks with low node density. Therefore, stochastic models using quasi-stationary Markov chain are introduced, which are more complex but more exact to estimate the key performance metrics of the OLA transmissions in practice. Considering the fact that such propagation models should be selected carefully depending on system parameters such as network topology and channel environments, we provide a comprehensive survey on the analytical models and framework of the OLA propagation in the literature, which is not available in the existing survey papers on OLA protocols. In addition, we introduce energy-efficient OLA techniques, which are of paramount importance in energy-limited IoT networks. Furthermore, we discuss future research directions to combine OLA with emerging technologies. Full article
(This article belongs to the Special Issue Green Sensors Networking)
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