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Role of Sensors in IoT-Enabled Applications in Agriculture, Artificial Intelligence, Healthcare and Wireless Sensor Networks

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

Deadline for manuscript submissions: closed (25 August 2023) | Viewed by 13948

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Special Issue Editors

Dr. Sampathkumar Arumugam

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Guest Editor

Department of Applied Cybernetics, Faculty of Science, University of Hradec Kralove, Hradec Kralove, Czech Republic
Interests: artificial intelligence; wireless sensor networks; health informatics

Dr. Nebojsa Bacanin

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Guest Editor

Faculty of Informatics and Computing, Singidunum University, 11010 Belgrade, Serbia
Interests: swarm intelligence; optimization metaheuristics; machine learning; deep learning; computer vision; Internet of Things
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Special Issue Information

Dear Colleagues,

Sensors play a vital role in the widespread development of technology and the domination of Internet of Things (IoT) devices in the fields of agriculture, artificial intelligence, smart cities and healthcare systems. The development of these technologies, which are making significant contributions to a number of applications in various areas, has had a particularly powerful impact on, among other things, the healthcare industry. Smart sensors are progressively providing innovative answers to a number of significant healthcare concerns, including early cancer diagnosis and the minimally invasive treatment and prevention of high-burden diseases. The healthcare industry is producing more and more data, so automation makes things simpler. Deep learning techniques and machine learning algorithms are needed for the effective handling of these data in order to gain insights, make precise decisions, and make predictions. Artificial intelligence (AI) is essential in ensuring work completion as a result of the healthcare sector's digitalization. In other fields, AI technology could provide us with drastically improved agricultural practices, and it is widely known that AI's success is entirely dependent on the quality of data that are made available. Multiple sensors are used for the gathering of data in the agricultural industry, such as weather forecasting, crop monitoring, etc. Valuable data can be put to further use by being fed into a trained deep learning method such as an artificial neural network for prediction. In the field of wireless sensor networks (WSN), energy optimization is an important aspect when transmitting data from different clusters. The use of AI in the field of WSN enables placing the sensor nodes in the exact location. The proposed Special Issue intends to gather, archive, and attract high-quality original research works from academic experts and industry practitioners in the unique domain of sensors employing IoT in the fields of healthcare, artificial intelligence, agricultural industry, and wireless sensor networks. The primary technical research direction is to contribute to the Internet of Things in different industries through the analysis of data generated by multiple sensors. It also seeks to give scholars and practitioners from all around the world a perfect platform to develop innovative approaches to immediate issues.

Dr. Sampathkumar Arumugam
Dr. Nebojsa Bacanin
Guest Editors

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Keywords

artificial intelligence
healthcare
wireless sensor networks
sensors

Published Papers (6 papers)

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Research

19 pages, 3023 KiB

Open AccessArticle

Coverage Analysis of LoRa and NB-IoT Technologies on LPWAN-Based Agricultural Vehicle Tracking Application

by Hakkı Soy

Sensors 2023, 23(21), 8859; https://doi.org/10.3390/s23218859 - 31 Oct 2023

Cited by 2 | Viewed by 1320

Abstract

This study focuses on the recently emerged Internet of Vehicles (IoV) concept to provide an integrated agricultural vehicle/machinery tracking system through two leading low power wide area network (LPWAN) technologies, namely LoRa and NB-IoT. The main aim is to investigate the theoretical coverage limits by considering the urban, suburban, and rural environments. Two vehicle tracking units (VTUs) have been designed for LoRa and NB-IoT connectivity technologies that can be used as reference hardware in coverage analysis. On this basis, the closed-form explicit analytical expressions of the maximum transmission range have been derived using the Hata path loss model. Besides, the computer simulation results have been validated via the maps from XIRIO online radio planning tool. In light of the obtained findings, several evaluations have been made to enhance the LPWAN-based agricultural vehicle tracking feasibility in smart farms. Full article

(This article belongs to the Special Issue Role of Sensors in IoT-Enabled Applications in Agriculture, Artificial Intelligence, Healthcare and Wireless Sensor Networks)

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23 pages, 7267 KiB

Open AccessArticle

Soil-MobiNet: A Convolutional Neural Network Model Base Soil Classification to Determine Soil Morphology and Its Geospatial Location

by Emmanuel Kwabena Gyasi and Swarnalatha Purushotham

Sensors 2023, 23(15), 6709; https://doi.org/10.3390/s23156709 - 27 Jul 2023

Cited by 2 | Viewed by 2405

Abstract

Scholars have classified soil to understand its complex and diverse characteristics. The current trend of precision agricultural technology demands a change in conventional soil identification methods. For example, soil color observed using Munsell color charts is subjective and lacks consistency among observers. Soil classification is essential for soil management and sustainable land utilization, thereby facilitating communication between different groups, such as farmers and pedologists. Misclassified soil can mislead processes; for example, it can hinder fertilizer delivery, affecting crop yield. On the other hand, deep learning approaches have facilitated computer vision technology, where machine-learning algorithms trained for image recognition, comparison, and pattern identification can classify soil better than or equal to human eyes. Moreover, the learning algorithm can contrast the current observation with previously examined data. In this regard, this study implements a convolutional neural network (CNN) model called Soil-MobiNet to classify soils. The Soil-MobiNet model implements the same pointwise and depthwise convolutions of the MobileNet, except the model uses the weight of the pointwise and depthwise separable convolutions plus an additional three dense layers for feature extraction. The model classified the Vellore Institute of Technology Soil (VITSoil) dataset, which is made up of 4864 soil images belonging to nine categories. The VITSoil dataset samples for Soil-MobiNet classification were collected over the Indian states and it is made up of nine major Indian soil types prepared by experts in soil science. With a training and validation accuracy of 98.47% and an average testing accuracy of 93%, Soil-MobiNet showed outstanding performance in categorizing the VITSoil dataset. In particular, the proposed Soil-MobiNet model can be used for real-time soil classification on mobile phones since the proposed system is small and portable. Full article

(This article belongs to the Special Issue Role of Sensors in IoT-Enabled Applications in Agriculture, Artificial Intelligence, Healthcare and Wireless Sensor Networks)

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17 pages, 637 KiB

Open AccessArticle

Secure Data Aggregation Based on End-to-End Homomorphic Encryption in IoT-Based Wireless Sensor Networks

by Mukesh Kumar, Monika Sethi, Shalli Rani, Dipak Kumar Sah, Salman A. AlQahtani and Mabrook S. Al-Rakhami

Sensors 2023, 23(13), 6181; https://doi.org/10.3390/s23136181 - 6 Jul 2023

Cited by 3 | Viewed by 1730

Abstract

By definition, the aggregating methodology ensures that transmitted data remain visible in clear text in the aggregated units or nodes. Data transmission without encryption is vulnerable to security issues such as data confidentiality, integrity, authentication and attacks by adversaries. On the other hand, encryption at each hop requires extra computation for decrypting, aggregating, and then re-encrypting the data, which results in increased complexity, not only in terms of computation but also due to the required sharing of keys. Sharing the same key across various nodes makes the security more vulnerable. An alternative solution to secure the aggregation process is to provide an end-to-end security protocol, wherein intermediary nodes combine the data without decoding the acquired data. As a consequence, the intermediary aggregating nodes do not have to maintain confidential key values, enabling end-to-end security across sensor devices and base stations. This research presents End-to-End Homomorphic Encryption (EEHE)-based safe and secure data gathering in IoT-based Wireless Sensor Networks (WSNs), whereby it protects end-to-end security and enables the use of aggregator functions such as COUNT, SUM and AVERAGE upon encrypted messages. Such an approach could also employ message authentication codes (MAC) to validate data integrity throughout data aggregation and transmission activities, allowing fraudulent content to also be identified as soon as feasible. Additionally, if data are communicated across a WSN, then there is a higher likelihood of a wormhole attack within the data aggregation process. The proposed solution also ensures the early detection of wormhole attacks during data aggregation. Full article

(This article belongs to the Special Issue Role of Sensors in IoT-Enabled Applications in Agriculture, Artificial Intelligence, Healthcare and Wireless Sensor Networks)

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33 pages, 838 KiB

Open AccessArticle

Attribute-Based Encryption Schemes for Next Generation Wireless IoT Networks: A Comprehensive Survey

by Shruti, Shalli Rani, Dipak Kumar Sah and Gabriele Gianini

Sensors 2023, 23(13), 5921; https://doi.org/10.3390/s23135921 - 26 Jun 2023

Cited by 1 | Viewed by 1810

Abstract

Most data nowadays are stored in the cloud; therefore, cloud computing and its extension—fog computing—are the most in-demand services at the present time. Cloud and fog computing platforms are largely used by Internet of Things (IoT) applications where various mobile devices, end users, PCs, and smart objects are connected to each other via the internet. IoT applications are common in several application areas, such as healthcare, smart cities, industries, logistics, agriculture, and many more. Due to this, there is an increasing need for new security and privacy techniques, with attribute-based encryption (ABE) being the most effective among them. ABE provides fine-grained access control, enables secure storage of data on unreliable storage, and is flexible enough to be used in different systems. In this paper, we survey ABE schemes, their features, methodologies, benefits/drawbacks, attacks on ABE, and how ABE can be used with IoT and its applications. This survey reviews ABE models suitable for IoT platforms, taking into account the desired features and characteristics. We also discuss various performance indicators used for ABE and how they affect efficiency. Furthermore, some selected schemes are analyzed through simulation to compare their efficiency in terms of different performance indicators. As a result, we find that some schemes simultaneously perform well in one or two performance indicators, whereas none shines in all of them at once. The work will help researchers identify the characteristics of different ABE schemes quickly and recognize whether they are suitable for specific IoT applications. Future work that may be helpful for ABE is also discussed. Full article

(This article belongs to the Special Issue Role of Sensors in IoT-Enabled Applications in Agriculture, Artificial Intelligence, Healthcare and Wireless Sensor Networks)

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20 pages, 3088 KiB

Open AccessArticle

Efficient Deep Learning Based Hybrid Model to Detect Obstructive Sleep Apnea

by Prashant Hemrajani, Vijaypal Singh Dhaka, Geeta Rani, Praveen Shukla and Durga Prasad Bavirisetti

Sensors 2023, 23(10), 4692; https://doi.org/10.3390/s23104692 - 12 May 2023

Cited by 3 | Viewed by 3084

Abstract

An increasing number of patients and a lack of awareness about obstructive sleep apnea is a point of concern for the healthcare industry. Polysomnography is recommended by health experts to detect obstructive sleep apnea. The patient is paired up with devices that track patterns and activities during their sleep. Polysomnography, being a complex and expensive process, cannot be adopted by the majority of patients. Therefore, an alternative is required. The researchers devised various machine learning algorithms using single lead signals such as electrocardiogram, oxygen saturation, etc., for the detection of obstructive sleep apnea. These methods have low accuracy, less reliability, and high computation time. Thus, the authors introduced two different paradigms for the detection of obstructive sleep apnea. The first is MobileNet V1, and the other is the convergence of MobileNet V1 with two separate recurrent neural networks, Long-Short Term Memory and Gated Recurrent Unit. They evaluate the efficacy of their proposed method using authentic medical cases from the PhysioNet Apnea-Electrocardiogram database. The model MobileNet V1 achieves an accuracy of 89.5%, a convergence of MobileNet V1 with LSTM achieves an accuracy of 90%, and a convergence of MobileNet V1 with GRU achieves an accuracy of 90.29%. The obtained results prove the supremacy of the proposed approach in comparison to the state-of-the-art methods. To showcase the implementation of devised methods in a real-life scenario, the authors design a wearable device that monitors ECG signals and classifies them into apnea and normal. The device employs a security mechanism to transmit the ECG signals securely over the cloud with the consent of patients. Full article

(This article belongs to the Special Issue Role of Sensors in IoT-Enabled Applications in Agriculture, Artificial Intelligence, Healthcare and Wireless Sensor Networks)

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15 pages, 4967 KiB

Open AccessArticle

Energy-Efficient Clustering and Routing Using ASFO and a Cross-Layer-Based Expedient Routing Protocol for Wireless Sensor Networks

by Venkatesan Cherappa, Thamaraimanalan Thangarajan, Sivagama Sundari Meenakshi Sundaram, Fahima Hajjej, Arun Kumar Munusamy and Ramalingam Shanmugam

Sensors 2023, 23(5), 2788; https://doi.org/10.3390/s23052788 - 3 Mar 2023

Cited by 28 | Viewed by 2436

Abstract

Today’s critical goals in sensor network research are extending the lifetime of wireless sensor networks (WSNs) and lowering power consumption. A WSN necessitates the use of energy-efficient communication networks. Clustering, storage, communication capacity, high configuration complexity, low communication speed, and limited computation are also some of the energy limitations of WSNs. Moreover, cluster head selection remains problematic for WSN energy minimization. Sensor nodes (SNs) are clustered in this work using the Adaptive Sailfish Optimization (ASFO) algorithm with K-medoids. The primary purpose of research is to optimize the selection of cluster heads through energy stabilization, distance reduction, and latency minimization between nodes. Because of these constraints, achieving optimal energy resource utilization is an essential problem in WSNs. An energy-efficient cross-layer-based expedient routing protocol (E-CERP) is used to determine the shortest route, dynamically minimizing network overhead. The proposed method is used to evaluate the packet delivery ratio (PDR), packet delay, throughput, power consumption, network lifetime, packet loss rate, and error estimation, and the results were superior to existing methods. PDR (100%), packet delay (0.05 s), throughput (0.99 Mbps), power consumption (1.97 mJ), network lifespan (5908 rounds), and PLR (0.5%) for 100 nodes are the performance results for quality-of-service parameters. Full article

(This article belongs to the Special Issue Role of Sensors in IoT-Enabled Applications in Agriculture, Artificial Intelligence, Healthcare and Wireless Sensor Networks)

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