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Distributed Sensor Networks: Development and Applications

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

Deadline for manuscript submissions: closed (15 July 2021) | Viewed by 15318

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Special Issue Editors

Dr. Thanasis Loukopoulos

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Guest Editor

Dept. of Computer Science and Biomedical Informatics, University of Thessaly, Greece
Interests: distributed systems; wireless sensor networks; parallel processing; cloud computing; video compression; green computing; scheduling algorithms

Dr. Nikos Tziritas

E-Mail Website
Guest Editor

Department of Computer Science and Telecommunications, School of Sciences, University of Thessaly, Volos, Greece
Interests: parallel and distributed systems; distributed machine learning; performance optimization; IoT/IIoT; real-time big data analytics; cloud computing
Special Issues, Collections and Topics in MDPI journals

Dr. Samee U. Khan

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Guest Editor

Department of Electrical and Computer Engineering, North Dakota State University, Fargo, ND, USA
Interests: optimization; robustness; security; sensor networks; distributed algorithms; cloud computing

Special Issue Information

Dear Colleagues,

Distributed sensor networks (DSNs) have received significant attention from both academia and industry, as advances in sensor technology and computer networks have enabled their evolution from a small local scale to large distributed clusters of micro-sensors. The financial and societal impact of such upscaling can be tremendous, as sensor networks are an integral part of the IoT domain, while applications of interest include military sector, environmental protection, industrial monitoring, smart cities, and many more. However, to realize their potential, technological and research advances are needed to achieve scalability in a sustainable manner. Such advances span multiple fields indicatively: network protocols, middleware and operating systems, data processing algorithms, etc. At the same time, programming frameworks, tools, and paradigms that enable the development and deployment of applications in an adaptive, transparent, and portable manner are especially important for building scalable and sustainable solutions that involve minimum customization. This Special Issue seeks original, high-quality submissions in the domain of distributed sensor networks with a particular focus on scalability and sustainability. Research advances in networking, systems, programming frameworks, applications, and algorithms are welcome. Papers surveying existing research in one or more fields of interest to the Special Issue are especially encouraged. Submitted papers must not be under consideration for any other venue.

Dr. Thanasis Loukopoulos
Dr. Nikos Tziritas
Prof. Samee U. Khan
Guest Editors

Manuscript Submission Information

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Keywords

Dynamic topologies;
Architecture, algorithms, protocols, and complexity issues
Learning patterns from distributed sensor sources
Machine learning and AI applications for distributed sensor sources
Energy harvesting and energy management
Security issues in distributed sensor networks
Lifetime optimization
Deployment strategies
IoT applications
Cloud computing for distributed sensor networks
Fog computing for distributed sensor networks
Edge computing for distributed sensor networks
Data management and big data in distributed sensor networks
Reliability, scalability, availability, and fault tolerance
Network dynamics and mobility
Data aggregation and fusion
Synchronization, coordination, and localization

Published Papers (5 papers)

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Research

20 pages, 5872 KiB

Open AccessArticle

ScienceIoT: Evolution of the Wireless Infrastructure of KREONET

by Cheonyong Kim, Joobum Kim, Ki-Hyeon Kim, Sang-Kwon Lee, Kiwook Kim, Syed Asif Raza Shah and Young-Hoon Goo

Sensors 2021, 21(17), 5852; https://doi.org/10.3390/s21175852 - 30 Aug 2021

Cited by 2 | Viewed by 2160

Abstract

Here, we introduce the current stage and future directions of the wireless infrastructure of the Korea Research Environment Open NETwork (KREONET), a representative national research and education network in Korea. In 2018, ScienceLoRa, a pioneering wireless network infrastructure for scientific applications based on low-power wide-area network technology, was launched. Existing in-service applications in monitoring regions, research facilities, and universities prove the effectiveness of using wireless infrastructure in scientific areas. Furthermore, to support the more stringent requirements of various scientific scenarios, ScienceLoRa is evolving toward ScienceIoT by employing high-performance wireless technology and distributed computing capability. Specifically, by accommodating a private 5G network and an integrated edge computing platform, ScienceIoT is expected to support cutting-edge scientific applications requiring high-throughput and distributed data processing. Full article

(This article belongs to the Special Issue Distributed Sensor Networks: Development and Applications)

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20 pages, 827 KiB

Open AccessArticle

An Algorithm to Minimize Energy Consumption and Elapsed Time for IoT Workloads in a Hybrid Architecture

by Julio C. S. dos Anjos, João L. G. Gross, Kassiano J. Matteussi, Gabriel V. González, Valderi R. Q. Leithardt and Claudio F. R. Geyer

Sensors 2021, 21(9), 2914; https://doi.org/10.3390/s21092914 - 21 Apr 2021

Cited by 25 | Viewed by 3839

Abstract

Advances in communication technologies have made the interaction of small devices, such as smartphones, wearables, and sensors, scattered on the Internet, bringing a whole new set of complex applications with ever greater task processing needs. These Internet of things (IoT) devices run on batteries with strict energy restrictions. They tend to offload task processing to remote servers, usually to cloud computing (CC) in datacenters geographically located away from the IoT device. In such a context, this work proposes a dynamic cost model to minimize energy consumption and task processing time for IoT scenarios in mobile edge computing environments. Our approach allows for a detailed cost model, with an algorithm called TEMS that considers energy, time consumed during processing, the cost of data transmission, and energy in idle devices. The task scheduling chooses among cloud or mobile edge computing (MEC) server or local IoT devices to achieve better execution time with lower cost. The simulated environment evaluation saved up to 51.6% energy consumption and improved task completion time up to 86.6%. Full article

(This article belongs to the Special Issue Distributed Sensor Networks: Development and Applications)

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15 pages, 1081 KiB

Open AccessArticle

Spectrum Based Power Management for Congested IoT Networks

by Kedir Mamo Besher, Juan Ivan Nieto-Hipolito, Raymundo Buenrostro-Mariscal and Mohammed Zamshed Ali

Sensors 2021, 21(8), 2681; https://doi.org/10.3390/s21082681 - 10 Apr 2021

Cited by 5 | Viewed by 2375

Abstract

With constantly increasing demand in connected society Internet of Things (IoT) network is frequently becoming congested. IoT sensor devices lose more power while transmitting data through congested IoT networks. Currently, in most scenarios, the distributed IoT devices in use have no effective spectrum based power management, and have no guarantee of a long term battery life while transmitting data through congested IoT networks. This puts user information at risk, which could lead to loss of important information in communication. In this paper, we studied the extra power consumed due to retransmission of IoT data packet and bad communication channel management in a congested IoT network. We propose a spectrum based power management solution that scans channel conditions when needed and utilizes the lowest congested channel for IoT packet routing. It also effectively measured power consumed in idle, connected, paging and synchronization status of a standard IoT device in a congested IoT network. In our proposed solution, a Freescale Freedom Development Board (FREDEVPLA) is used for managing channel related parameters. While supervising the congestion level and coordinating channel allocation at the FREDEVPLA level, our system configures MAC and Physical layer of IoT devices such that it provides the outstanding power utilization based on the operating network in connected mode compared to the basic IoT standard. A model has been set up and tested using freescale launchpads. Test data show that battery life of IoT devices using proposed spectrum based power management increases by at least 30% more than non-spectrum based power management methods embedded within IoT devices itself. Finally, we compared our results with the basic IoT standard, IEEE802.15.4. Furthermore, the proposed system saves lot of memory for IoT devices, improves overall IoT network performance, and above all, decrease the risk of losing data packets in communication. The detail analysis in this paper also opens up multiple avenues for further research in future use of channel scanning by FREDEVPLA board. Full article

(This article belongs to the Special Issue Distributed Sensor Networks: Development and Applications)

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11 pages, 1208 KiB

Open AccessCommunication

Estimation of the Path-Loss Exponent by Bayesian Filtering Method

by Piotr Wojcicki, Tomasz Zientarski, Malgorzata Charytanowicz and Edyta Lukasik

Sensors 2021, 21(6), 1934; https://doi.org/10.3390/s21061934 - 10 Mar 2021

Cited by 14 | Viewed by 2695

Abstract

Regarding wireless sensor network parameter estimation of the propagation model is a most important issue. Variations of the received signal strength indicator (RSSI) parameter are a fundamental problem of a system based on signal strength. In the present paper, we propose an algorithm based on Bayesian filtering techniques for estimating the path-loss exponent of the log-normal shadowing propagation model for outdoor RSSI measurements. Furthermore, in a series of experiments, we will demonstrate the usefulness of the particle filter for estimating the RSSI data. The stability of this algorithm and the differences in determined path-loss exponent for both method were also analysed. The proposed method of dynamic estimation results in significant improvements of the accuracy of RSSI values when compared with the experimental measurements. It should be emphasised that the path-loss exponent mainly depends on the RSSI data. Our results also indicate that increasing the number of inserted particles does not significantly raise the quality of the estimated parameters. Full article

(This article belongs to the Special Issue Distributed Sensor Networks: Development and Applications)

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19 pages, 1269 KiB

Open AccessArticle

Optimal Relay Selection Scheme with Multiantenna Power Beacon for Wireless-Powered Cooperation Communication Networks

by Oussama Messadi, Aduwati Sali, Vahid Khodamoradi, Asem A. Salah, Gaofeng Pan, Shaiful J. Hashim and Nor K. Noordin

Sensors 2021, 21(1), 147; https://doi.org/10.3390/s21010147 - 29 Dec 2020

Cited by 13 | Viewed by 2825

Abstract

Unlike the fixed power grid cooperative networks, which are mainly based on the reception reliability parameter while choosing the best relay, the wireless-powered cooperative communication network (WPCCN) and in addition to the reception reliability the transmission requirement consideration is important for relay selection schemes. Hence, enabling efficient transmission techniques that address high attenuation of radio frequency (RF) signals according to the distance without increasing the total transmission power is an open issue worth studying. In this relation, a multiantennas power beacon (PB) that assists wireless-powered cooperative communication network (PB-WPCCN) is studied in this paper. The communication between source and destination is achieved with the aid of multiple relays, where both the source and the multiple relays need to harvest energy from the PB in the first place to enable their transmission functionalities. A novel relay selection scheme is proposed, named as two-round relay selection (2-RRS), where a group of relays that successfully decode the source information is selected in the first round selection. In the second round, the optimal relay is selected to forward the recorded information to the destination. The proposed 2-RRS scheme is compared with two existing relay selection schemes, i.e., partial relay selection (PRS) and opportunistic relay selection (ORS). The analytical closed-form expressions of outage probability and average system throughput are derived and validated by numerical simulation. The comparison results between different relay selection schemes show: (I) The superiority of the proposed 2-RRS scheme as it achieves around 17% better throughput compared to the conventional ORS scheme and 40% better than the PRS scheme, particularly when PB transmit power is 10 dB; (II) The proposed 2-RRS scheme guarantees the lowest outage probability, especially when the PB is equipped with multiantennas and performs beamforming technique; (III) The optimal localisation of the PB between the source and N relays depends on the adopted relay selection scheme; (IV) The exhaustive search of the maximum system throughput value shows that the proposed 2-RRS scheme required shorter energy harvesting time compared to other schemes. The increase in energy harvesting time and number of relays do not necessarily reflect positively on the system throughput performance; hence tradeoffs should be taken into consideration. Full article

(This article belongs to the Special Issue Distributed Sensor Networks: Development and Applications)

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