Submit to Sensors Review for Sensors Edit a Special Issue

Journal Browser

► Journal Browser

Special Issue "Advanced Wireless Sensor Network Deployment in Smart Cities, Industry 4.0, and Agriculture 4.0"

Special Issue Editors
Special Issue Information
Published Papers

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

Deadline for manuscript submissions: closed (20 May 2023) | Viewed by 11370

Share This Special Issue

Special Issue Editors

Prof. Dr. Rafael Asorey-Cacheda

E-Mail Website
Guest Editor

Department of Information and Communication Technologies, Universidad Politécnica de Cartagena (UPCT), Campus Muralla del Mar, E-30202 Cartagena, Spain
Interests: wireless networks; Internet of Things; nanocommunications; streaming services
Special Issues, Collections and Topics in MDPI journals

Dr. Antonio-Javier Garcia-Sanchez

E-Mail Website
Guest Editor

Department of Information and Communication Technologies, Universidad Politecnica de Cartagenadisabled, 30202 Cartagena, Spain
Interests: WSN; Internet of Things; LPWAN; streaming services; performance evaluation of communication networks; smart data processing and nanocommunications
Special Issues, Collections and Topics in MDPI journals

Prof. Joan García-Haro

E-Mail Website
Guest Editor

Department of Information Technologies and Communications, Universidad Politécnica de Cartagena (UPCT), 30202 Cartagena, Spain
Interests: Wireless Networks; Vehicular Networks; RFIDMAC protocols
Special Issues, Collections and Topics in MDPI journals

Dr. Claudia Liliana Zuniga

E-Mail Website
Guest Editor

Barrio Pampalinda, Santiago de Cali, Universidad Santiago de Cali, Cra. 73 ##2a-80, Cali, Colombia
Interests: Urban Computing; Pervasive Computing; HCI

Special Issue Information

Dear Colleagues,

Wireless sensor networks (WSNs) are considered a well-established and sound ICT technology consisting of an arbitrarily large number of low-cost, small, portable, low-consumption, and connected devices that work collaboratively to offer a wide range of pervasive and ubiquitous smart services and innovative applications in many areas. Under this technological umbrella, advances in WSNs have become the cornerstone of recent paradigms such as the Internet of Things (IoT). The deployment of WSNs has already created new opportunities for the development of smart cities, Industry 4.0, and Agriculture 4.0, among other relevant sectors. This has enabled ground-breaking applications that represent a step forward to significantly improving our quality of life and promoting the progress of industry and agriculture. In fact, WSN deployment in these environments still has open issues that require attention from the community. The topics of interest include but are not limited to the following:

Internet of Things breakthroughs in smart cities, Industry 4.0, and Agriculture 4.0;
Low-power wide-area network solutions for smart cities, Industry 4.0, and Agriculture 4.0 applications;
Mobility management, roaming, and advanced routing techniques for WSNs and low-power wide-area networks;
Crowdsourcing/crowdsensing in IoT networks;
Artificial intelligence and machine learning applied to IoT networks;
Security and privacy issues in IoT networks for smart cities, Industry 4.0, and Agriculture 4.0.

Dr. Rafael Asorey-Cacheda
Prof. Antonio-Javier Garcia-Sanchez
Prof. Joan García-Haro
Dr. Claudia Liliana Zuniga
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 2400 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 (7 papers)

Download All Papers

Research

Open AccessArticle

Smart Air Quality Monitoring IoT-Based Infrastructure for Industrial Environments

Laura García

Antonio-Javier Garcia-Sanchez

Rafael Asorey-Cacheda

Joan Garcia-Haro

and

Claudia-Liliana Zúñiga-Cañón

Sensors 2022, 22(23), 9221; https://doi.org/10.3390/s22239221 - 27 Nov 2022

Cited by 1 | Viewed by 1032

Abstract

Deficient air quality in industrial environments creates a number of problems that affect both the staff and the ecosystems of a particular area. To address this, periodic measurements must be taken to monitor the pollutant substances discharged into the atmosphere. However, the deployed system should also be adapted to the specific requirements of the industry. This paper presents a complete air quality monitoring infrastructure based on the IoT paradigm that is fully integrable into current industrial systems. It includes the development of two highly precise compact devices to facilitate real-time monitoring of particulate matter concentrations and polluting gases in the air. These devices are able to collect other information of interest, such as the temperature and humidity of the environment or the Global Positioning System (GPS) location of the device. Furthermore, machine learning techniques have been applied to the Big Data collected by this system. The results identify that the Gaussian Process Regression is the technique with the highest accuracy among the air quality data sets gathered by the devices. This provides our solution with, for instance, the intelligence to predict when safety levels might be surpassed. Full article

(This article belongs to the Special Issue Advanced Wireless Sensor Network Deployment in Smart Cities, Industry 4.0, and Agriculture 4.0)

► Show Figures

Figure 1

Open AccessArticle

Model-Driven Impact Quantification of Energy Resource Redundancy and Server Rejuvenation on the Dependability of Medical Sensor Networks in Smart Hospitals

Francisco Airton Silva

and

Paulo Romero Martin Maciel

Sensors 2022, 22(4), 1595; https://doi.org/10.3390/s22041595 - 18 Feb 2022

Cited by 3 | Viewed by 1102

Abstract

The spread of the Coronavirus (COVID-19) pandemic across countries all over the world urges governments to revolutionize the traditional medical hospitals/centers to provide sustainable and trustworthy medical services to patients under the pressure of the huge overload on the computing systems of wireless sensor networks (WSNs) for medical monitoring as well as treatment services of medical professionals. Uncertain malfunctions in any part of the medical computing infrastructure, from its power system in a remote area to the local computing systems at a smart hospital, can cause critical failures in medical monitoring services, which could lead to a fatal loss of human life in the worst case. Therefore, early design in the medical computing infrastructure’s power and computing systems needs to carefully consider the dependability characteristics, including the reliability and availability of the WSNs in smart hospitals under an uncertain outage of any part of the energy resources or failures of computing servers, especially due to software aging. In that regard, we propose reliability and availability models adopting stochastic Petri net (SPN) to quantify the impact of energy resources and server rejuvenation on the dependability of medical sensor networks. Three different availability models (A, B, and C) are developed in accordance with various operational configurations of a smart hospital’s computing infrastructure to assimilate the impact of energy resource redundancy and server rejuvenation techniques for high availability. Moreover, a comprehensive sensitivity analysis is performed to investigate the components that impose the greatest impact on the system availability. The analysis results indicate different impacts of the considered configurations on the WSN’s operational availability in smart hospitals, particularly 99.40%, 99.53%, and 99.64% for the configurations A, B, and C, respectively. This result highlights the difference of 21 h of downtime per year when comparing the worst with the best case. This study can help leverage the early design of smart hospitals considering its wireless medical sensor networks’ dependability in quality of service to cope with overloading medical services in world-wide virus pandemics. Full article

(This article belongs to the Special Issue Advanced Wireless Sensor Network Deployment in Smart Cities, Industry 4.0, and Agriculture 4.0)

► Show Figures

Figure 1

Open AccessArticle

Improving the Deployment of WSNs by Localized Detection of Covered Redundant Nodes in Industry 4.0 Applications

Marwa Hussein Hamad Aljubori

Vahid Khalilpour Akram

and

Moharram Challenger

Sensors 2022, 22(3), 942; https://doi.org/10.3390/s22030942 - 26 Jan 2022

Cited by 1 | Viewed by 1656

Abstract

Wireless sensor networks can be used as cost-effective monitoring and automation platforms in smart manufacturing and Industry 4.0. Maximizing the covered area and increasing the network lifetime are two challenging tasks in wireless sensor networks. A feasible solution for maximizing the coverage area and network lifetime is detecting and relocating the covered redundant nodes. A covered redundant node is a node whose covered area is also covered by the other active nodes in the network. After identifying the covered redundant nodes, putting them in sleep mode can increase the network lifetime. In addition, moving the detected redundant nodes to the uncovered locations can improve the overall covered area by the sensor nodes. However, finding the redundant nodes is an NP-complete problem. In this paper, we propose a localized distributed algorithm for identifying the redundant nodes based on the 2-hop local neighborhood information of the nodes. The proposed algorithm uses the existing connections between the neighbors of each sensor node to decide the redundancy of the node. The algorithm is localized and does not need the entire topology of the network or the coordinates of the nodes. Full article

(This article belongs to the Special Issue Advanced Wireless Sensor Network Deployment in Smart Cities, Industry 4.0, and Agriculture 4.0)

► Show Figures

Figure 1

Open AccessArticle

A Talk-Listen-Ack Beaconing Strategy for Neighbor Discovery Protocols in Wireless Sensor Networks

and

Sensors 2022, 22(1), 377; https://doi.org/10.3390/s22010377 - 04 Jan 2022

Cited by 1 | Viewed by 942

Abstract

Neighbor discovery is a fundamental function for sensor networking. Sensor nodes discover each other by sending and receiving beacons. Although many time-slotted neighbor discovery protocols (NDPs) have been proposed, the theoretical discovery latency is measured by the number of time slots rather than the unit of time. Generally, the actual discovery latency of a NDP is proportional to its theoretical discovery latency and slot length, and inversely proportional to the discovery probability. Therefore, it is desired to increase discovery probability while reducing slot length. This task, however, is challenging because the slot length and the discovery probability are two conflicting factors, and they mainly depend on the beaconing strategy used. In this paper, we propose a new beaconing strategy, called talk-listen-ack beaconing (TLA). We analyze the discovery probability of TLA by using a fine-grained slot model. Further, we also analyze the discovery probability of TLA that uses random backoff mechanism to avoid persistent collisions. Simulation and experimental results show that, compared with the 2-Beacon approach that has been widely used in time-slotted NDPs, TLA can achieve a high discovery probability even in a short time slot. TLA is a generic beaconing strategy that can be applied to different slotted NDPs to reduce their discovery latency. Full article

(This article belongs to the Special Issue Advanced Wireless Sensor Network Deployment in Smart Cities, Industry 4.0, and Agriculture 4.0)

► Show Figures

Figure 1

Open AccessArticle

An Optimized Framework for WSN Routing in the Context of Industry 4.0

and

Sensors 2021, 21(19), 6474; https://doi.org/10.3390/s21196474 - 28 Sep 2021

Cited by 41 | Viewed by 1643

Abstract

The advancements in Industry 4.0 have opened up new ways for the structural deployment of Smart Grids (SGs) to face the endlessly rising challenges of the 21st century. SGs for Industry 4.0 can be better managed by optimized routing techniques. In Mobile Ad hoc Networks (MANETs), the topology is not fixed and can be encountered by interference, mobility of nodes, propagation of multi-paths, and path loss. To extenuate these concerns for SGs, in this paper, we have presented a new version of the standard Optimized Link State Routing (OLSR) protocol for SGs to improve the management of control intervals that enhance the efficiency of the standard OLSR protocol without affecting its reliability. The adapted fault tolerant approach makes the proposed protocol more reliable for industrial applications. The process of grouping of nodes supports managing the total network cost by reducing severe flooding and evaluating an optimized head of clusters. The head of the unit is nominated according to the first defined expectation factor. With a sequence of rigorous performance evaluations under simulation parameters, the simulation results show that the proposed version of OLSR has proliferated Quality of Service (QoS) metrics when it is compared against the state-of-the-art-based conventional protocols, namely, standard OLSR, DSDV, AOMDV and hybrid routing technique. Full article

(This article belongs to the Special Issue Advanced Wireless Sensor Network Deployment in Smart Cities, Industry 4.0, and Agriculture 4.0)

► Show Figures

Figure 1

Open AccessArticle

What Drives the Usage of Management Tools Supporting Industry 4.0 in Organizations?

Zlatko Nedelko

Sensors 2021, 21(10), 3512; https://doi.org/10.3390/s21103512 - 18 May 2021

Cited by 1 | Viewed by 1859

Abstract

The main purpose of this study was to examine how personal and organizational drivers influence the utilization of management tools aimed at supporting organizational working in Industry 4.0 settings. We built our research upon the recognized importance of management tools for organizational working under Industry 4.0 settings and explored the key personal and organizational drivers of management tool usage. Calculations were performed based on the responses of 222 employees working in organizations across Europe. The results revealed that, among personal drivers, a higher level of education leads to significantly higher usage of six sigma, rapid prototyping, outsourcing, customer relationship management, knowledge management, core competencies, and strategic planning. More experienced employees use significantly more six sigma, total quality management, supply chain management, knowledge management, and core competences than their less experienced peers. The impact of organizational drivers is substantially weaker, where only industry shows significant influence, indicating that lean production, six sigma, and supply chain management are used more in manufacturing than in service organizations. Gender, one’s position in the organization, and the organization size do not play a substantial role in management tool usage. Managers should recognize the role of personal and organizational drivers of management tool usage in order to more quickly implement Industry 4.0 principles in organizations. Full article

(This article belongs to the Special Issue Advanced Wireless Sensor Network Deployment in Smart Cities, Industry 4.0, and Agriculture 4.0)

Open AccessArticle

An Automatized Contextual Marketing System Based on a Wi-Fi Indoor Positioning System

José-Antonio López-Pastor

Antonio-Jesús Ruiz-Ruiz

Antonio-Javier García-Sánchez

and

José-Luis Gómez-Tornero

Sensors 2021, 21(10), 3495; https://doi.org/10.3390/s21103495 - 17 May 2021

Cited by 2 | Viewed by 1972

Abstract

A complete contextual marketing platform including an indoor positioning system (IPS) for smartphones is proposed and evaluated to later be deployed in large infrastructures, such as malls. To this end, we design and implement a novel methodology based on location-as-a-service (LAAS), comprising all the required phases of IPS generation: mall digital map creation, the tools/procedures for offline calibration fingerprint acquisition, the location algorithm, the smartphone app acquiring the fingerprint data, and a validation procedure. To select an appropriate fingerprint location algorithm, a comparison among K-nearest neighbors (KNN), support vector machine (SVM), and Freeloc is accomplished by employing a set of different smartphones in two malls and assessing different occupancy levels. We demonstrate that our solution can be quickly deployed at shop level accuracy in any new location, resulting in a robust and scalable proposal. Full article

(This article belongs to the Special Issue Advanced Wireless Sensor Network Deployment in Smart Cities, Industry 4.0, and Agriculture 4.0)

► Show Figures

Figure 1

Journal Menu

Journal Browser

Special Issue "Advanced Wireless Sensor Network Deployment in Smart Cities, Industry 4.0, and Agriculture 4.0"

Share This Special Issue

Special Issue Editors

Special Issue Information

Published Papers (7 papers)

Research

Further Information

Guidelines

MDPI Initiatives

Follow MDPI