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Special Issue "Energy-Efficient Sensing in Wireless Sensor Networks"

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

Deadline for manuscript submissions: closed (1 March 2020).

Special Issue Editor

Prof. Dr. Masashi Sugano
Website
Guest Editor
Graduate School of Humanities and Sustainable System Sciences, Osaka Prefecture University, Sakai-shi, 5998531 Osaka, Japan
Interests: wireless sensor network; mobile computing; Internet of Things
Special Issues and Collections in MDPI journals

Special Issue Information

Dear Colleagues,

In a smart society, sensors are located everywhere, and we aim to realize a sustainable environment by utilizing the big data collected from these sensors. Thus, the sensor networks are essential as the infrastructure that supports smart society, and there is therefore a strong demand for low-cost, maintenance-free sensor network systems. A significant challenge for realizing such sensor networks is the acquisition of electrical energy to drive them. In a battery-driven sensor, it is crucial that it can operate for an extended period and this is achieved by controlling and suppressing power consumption. Progress is also being made on the practical application of devices and systems that can be driven by energy harvesting. Also, LPWA technology capable of long-distance communication using a low power consumption is becoming widespread.

This Special Issue presents the latest research on the use of energy saving, energy harvesting, and LPWA in sensor networks, and demonstrates the feasibility of sensor networks as a highly promising infrastructure for realizing a smart society.

Prof. Dr. Masashi Sugano
Guest Editor

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 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 2000 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

  • Energy-efficient IoT (Internet of Things)
  • Energy harvesting
  • LPWA (LoRa, Sigfox, etc.)
  • Application of energy-efficient sensor network
  • Energy-efficient communication protocol
  • Energy-efficient network design and control

Published Papers (3 papers)

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Open AccessArticle
Joint Resource Optimization for Orthogonal Frequency Division Multiplexing Based Cognitive Amplify and Forward Relaying Networks
Sensors 2020, 20(7), 2074; https://doi.org/10.3390/s20072074 - 07 Apr 2020
Abstract
This paper investigates two resource allocation problems in cognitive relaying networks where both secondary network and primary network coexist in the same frequency band and adopt orthogonal frequency division multiplexing (OFDM) technology. The first one is the sum rate maximization problem of a [...] Read more.
This paper investigates two resource allocation problems in cognitive relaying networks where both secondary network and primary network coexist in the same frequency band and adopt orthogonal frequency division multiplexing (OFDM) technology. The first one is the sum rate maximization problem of a secondary network under total power budget of a secondary network and tolerable interference constraint of a primary network. The second one is the sum rate maximization problem of a secondary network under separate power budgets of a secondary network and tolerable interference constraint of a primary network. These two optimization problems are completely different from those in traditional cooperative communication due to interference management constraint condition. A joint optimization algorithm is proposed, where power allocation and subcarrier pairing are decomposed into two subproblems with reasonable cost. The first one is a closed form solution of power allocation of the secondary network while managing the interference to a primary network under a constraint condition. The other is optimal subcarrier pairing at given power allocation. Simulation results reveal aspects of average signal to noise ratio (SNR), interference level, relay position, and power ratio on the sum rate of a secondary network. Full article
(This article belongs to the Special Issue Energy-Efficient Sensing in Wireless Sensor Networks)
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Open AccessArticle
Refining Network Lifetime of Wireless Sensor Network Using Energy-Efficient Clustering and DRL-Based Sleep Scheduling
Sensors 2020, 20(5), 1540; https://doi.org/10.3390/s20051540 - 10 Mar 2020
Cited by 2
Abstract
Over the recent era, Wireless Sensor Network (WSN) has attracted much attention among industrialists and researchers owing to its contribution to numerous applications including military, environmental monitoring and so on. However, reducing the network delay and improving the network lifetime are always big [...] Read more.
Over the recent era, Wireless Sensor Network (WSN) has attracted much attention among industrialists and researchers owing to its contribution to numerous applications including military, environmental monitoring and so on. However, reducing the network delay and improving the network lifetime are always big issues in the domain of WSN. To resolve these downsides, we propose an Energy-Efficient Scheduling using the Deep Reinforcement Learning (DRL) (E2S-DRL) algorithm in WSN. E2S-DRL contributes three phases to prolong network lifetime and to reduce network delay that is: the clustering phase, duty-cycling phase and routing phase. E2S-DRL starts with the clustering phase where we reduce the energy consumption incurred during data aggregation. It is achieved through the Zone-based Clustering (ZbC) scheme. In the ZbC scheme, hybrid Particle Swarm Optimization (PSO) and Affinity Propagation (AP) algorithms are utilized. Duty cycling is adopted in the second phase by executing the DRL algorithm, from which, E2S-DRL reduces the energy consumption of individual sensor nodes effectually. The transmission delay is mitigated in the third (routing) phase using Ant Colony Optimization (ACO) and the Firefly Algorithm (FFA). Our work is modeled in Network Simulator 3.26 (NS3). The results are valuable in provisions of upcoming metrics including network lifetime, energy consumption, throughput and delay. From this evaluation, it is proved that our E2S-DRL reduces energy consumption, reduces delays by up to 40% and enhances throughput and network lifetime up to 35% compared to the existing cTDMA, DRA, LDC and iABC methods. Full article
(This article belongs to the Special Issue Energy-Efficient Sensing in Wireless Sensor Networks)
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Open AccessConcept Paper
Energy and Delay Aware Data Aggregation in Routing Protocol for Internet of Things
Sensors 2019, 19(24), 5486; https://doi.org/10.3390/s19245486 - 12 Dec 2019
Cited by 2
Abstract
Energy conservation is one of the most critical problems in Internet of Things (IoT). It can be achieved in several ways, one of which is to select the optimal route for data transfer. IPv6 Routing Protocol for Low Power and Lossy Networks (RPL) [...] Read more.
Energy conservation is one of the most critical problems in Internet of Things (IoT). It can be achieved in several ways, one of which is to select the optimal route for data transfer. IPv6 Routing Protocol for Low Power and Lossy Networks (RPL) is a standardized routing protocol for IoT. The RPL changes its path frequently while transmitting the data from source to the destination, due to high data traffic in dense networks. Hence, it creates data traffic across the nodes in the networks. To solve this issue, we propose Energy and Delay Aware Data aggregation in Routing Protocol (EDADA-RPL) for IoT. It has two processes, namely parent selection and data aggregation. The process of parent selection uses routing metric residual energy (RER) to choose the best possible parent for data transmission. The data aggregation process uses the compressed sensing (CS) theory in the parent node to combine data packets from the child nodes. Finally, the aggregated data transmits from a downward parent to the sink. The sink node collects all the aggregated data and it performs the reconstruction operation to get the original data of the participant node. The simulation is carried out using the Contiki COOJA simulator. EDADA-RPL’s performance is compared to RPL and LA-RPL. The EDADA-RPL offers good performance in terms of network lifetime, delay, and packet delivery ratio. Full article
(This article belongs to the Special Issue Energy-Efficient Sensing in Wireless Sensor Networks)
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