Journal of Sensor and Actuator Networks

45 pages, 16060 KiB

Open AccessArticle

PbDinEHR: A Novel Privacy by Design Developed Framework Using Distributed Data Storage and Sharing for Secure and Scalable Electronic Health Records Management

by Farida Habib Semantha, Sami Azam, Bharanidharan Shanmugam and Kheng Cher Yeo

J. Sens. Actuator Netw. 2023, 12(2), 36; https://doi.org/10.3390/jsan12020036 - 13 Apr 2023

Cited by 4 | Viewed by 3548

Abstract

Privacy in Electronic Health Records (EHR) has become a significant concern in today’s rapidly changing world, particularly for personal and sensitive user data. The sheer volume and sensitive nature of patient records require healthcare providers to exercise an intense quantity of caution during [...] Read more.

Privacy in Electronic Health Records (EHR) has become a significant concern in today’s rapidly changing world, particularly for personal and sensitive user data. The sheer volume and sensitive nature of patient records require healthcare providers to exercise an intense quantity of caution during EHR implementation. In recent years, various healthcare providers have been hit by ransomware and distributed denial of service attacks, halting many emergency services during COVID-19. Personal data breaches are becoming more common day by day, and privacy concerns are often raised when sharing data across a network, mainly due to transparency and security issues. To tackle this problem, various researchers have proposed privacy-preserving solutions for EHR. However, most solutions do not extensively use Privacy by Design (PbD) mechanisms, distributed data storage and sharing when designing their frameworks, which is the emphasis of this study. To design a framework for Privacy by Design in Electronic Health Records (PbDinEHR) that can preserve the privacy of patients during data collection, storage, access and sharing, we have analysed the fundamental principles of privacy by design and privacy design strategies, and the compatibility of our proposed healthcare principles with Privacy Impact Assessment (PIA), Australian Privacy Principles (APPs) and General Data Protection Regulation (GDPR). To demonstrate the proposed framework, ‘PbDinEHR’, we have implemented a Patient Record Management System (PRMS) to create interfaces for patients and healthcare providers. In addition, to provide transparency and security for sharing patients’ medical files with various healthcare providers, we have implemented a distributed file system and two permission blockchain networks using the InterPlanetary File System (IPFS) and Ethereum blockchain. This allows us to expand the proposed privacy by design mechanisms in the future to enable healthcare providers, patients, imaging labs and others to share patient-centric data in a transparent manner. The developed framework has been tested and evaluated to ensure user performance, effectiveness, and security. The complete solution is expected to provide progressive resistance in the face of continuous data breaches in the patient information domain. Full article

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19 pages, 2390 KiB

Open AccessArticle

Energy-Efficient Relay Tracking and Predicting Movement Patterns with Multiple Mobile Camera Sensors

by Zeinab Hussein and Omar Banimelhem

J. Sens. Actuator Netw. 2023, 12(2), 35; https://doi.org/10.3390/jsan12020035 - 13 Apr 2023

Cited by 3 | Viewed by 1400

Abstract

Camera sensor networks (CSN) have been widely used in different applications such as large building monitoring, social security, and target tracking. With advances in visual and actuator sensor technology in the last few years, deploying mobile cameras in CSN has become a possible [...] Read more.

Camera sensor networks (CSN) have been widely used in different applications such as large building monitoring, social security, and target tracking. With advances in visual and actuator sensor technology in the last few years, deploying mobile cameras in CSN has become a possible and efficient solution for many CSN applications. However, mobile camera sensor networks still face several issues, such as limited sensing range, the optimal deployment of camera sensors, and the energy consumption of the camera sensors. Therefore, mobile cameras should cooperate in order to improve the overall performance in terms of enhancing the tracking quality, reducing the moving distance, and reducing the energy consumed. In this paper, we propose a movement prediction algorithm to trace the moving object based on a cooperative relay tracking mechanism. In the proposed approach, the future path of the target is predicted using a pattern recognition algorithm by applying data mining to the past movement records of the target. The efficiency of the proposed algorithms is validated and compared with another related algorithm. Simulation results have shown that the proposed algorithm guarantees the continuous tracking of the object, and its performance outperforms the other algorithms in terms of reducing the total moving distance of cameras and reducing energy consumption levels. For example, in terms of the total moving distance of the cameras, the proposed approach reduces the distance by 4.6% to 15.2% compared with the other protocols that do not use prediction. Full article

(This article belongs to the Special Issue Blockchain and Artificial Intelligence for Cyber Security in the Era of IoT and IIoT Applications)

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20 pages, 1080 KiB

Open AccessArticle

Reliability Evaluation for Chain Routing Protocols in Wireless Sensor Networks Using Reliability Block Diagram

by Oruba Alfawaz, Ahmed M. Khedr, Bader Alwasel and Walid Osamy

J. Sens. Actuator Netw. 2023, 12(2), 34; https://doi.org/10.3390/jsan12020034 - 10 Apr 2023

Viewed by 1613

Abstract

There are many different fields in which wireless sensor networks (WSNs) can be used such as environmental monitoring, healthcare, military, and security. Due to the vulnerability of WSNs, reliability is a critical concern. Evaluation of a WSN’s reliability is essential during the design [...] Read more.

There are many different fields in which wireless sensor networks (WSNs) can be used such as environmental monitoring, healthcare, military, and security. Due to the vulnerability of WSNs, reliability is a critical concern. Evaluation of a WSN’s reliability is essential during the design process and when evaluating WSNs’ performance. Current research uses the reliability block diagram (RBD) technique, based on component functioning or failure state, to evaluate reliability. In this study, a new methodology-based RBD, to calculate the energy reliability of various proposed chain models in WSNs, is presented. A new method called D-Chain is proposed, to form the chain starting from the nearest node to the base station (BS) and to choose the chain head based on the minimum distance D, and Q-Chain is proposed, to form the chain starting from the farthest node from the BS and select the head based on the maximum weight, Q. Each chain has three different arrangements: single chain/single-hop, multi-chain/single-hop, and multi-chain/multi-hop. Moreover, we applied dynamic leader nodes to all of the models mentioned. The simulation results indicate that the multi Q-Chain/single-hop has the best performance, while the single D-Chain has the least reliability in all situations. In the grid scenario, multi Q-Chain/single-hop achieved better average reliability, 11.12 times greater than multi D-Chain/single-hop. On the other hand, multi Q-Chain/single-hop achieved 6.38 times better average reliability than multi D-Chain/single-hop, in a random scenario. Full article

(This article belongs to the Topic Wireless Sensor Networks)

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19 pages, 2082 KiB

Open AccessArticle

Enhanced Traffic Sign Recognition with Ensemble Learning

by Xin Roy Lim, Chin Poo Lee, Kian Ming Lim and Thian Song Ong

J. Sens. Actuator Netw. 2023, 12(2), 33; https://doi.org/10.3390/jsan12020033 - 07 Apr 2023

Cited by 3 | Viewed by 2278

Abstract

With the growing trend in autonomous vehicles, accurate recognition of traffic signs has become crucial. This research focuses on the use of convolutional neural networks for traffic sign classification, specifically utilizing pre-trained models of ResNet50, DenseNet121, and VGG16. To enhance the accuracy and [...] Read more.

With the growing trend in autonomous vehicles, accurate recognition of traffic signs has become crucial. This research focuses on the use of convolutional neural networks for traffic sign classification, specifically utilizing pre-trained models of ResNet50, DenseNet121, and VGG16. To enhance the accuracy and robustness of the model, the authors implement an ensemble learning technique with majority voting, to combine the predictions of multiple CNNs. The proposed approach was evaluated on three different traffic sign datasets: the German Traffic Sign Recognition Benchmark (GTSRB), the Belgium Traffic Sign Dataset (BTSD), and the Chinese Traffic Sign Database (TSRD). The results demonstrate the efficacy of the ensemble approach, with recognition rates of 98.84% on the GTSRB dataset, 98.33% on the BTSD dataset, and 94.55% on the TSRD dataset. Full article

(This article belongs to the Special Issue Advances in Intelligent Transportation Systems (ITS))

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25 pages, 4161 KiB

Open AccessEditor’s ChoiceArticle

Smart Automotive Diagnostic and Performance Analysis Using Blockchain Technology

by Ahmed Mohsen Yassin, Heba Kamal Aslan and Islam Tharwat Abdel Halim

J. Sens. Actuator Netw. 2023, 12(2), 32; https://doi.org/10.3390/jsan12020032 - 07 Apr 2023

Cited by 3 | Viewed by 2824

Abstract

The automotive industry currently is seeking to increase remote connectivity to a vehicle, which creates a high demand to implement a secure way of connecting vehicles, as well as verifying and storing their data in a trusted way. Furthermore, much information must be [...] Read more.

The automotive industry currently is seeking to increase remote connectivity to a vehicle, which creates a high demand to implement a secure way of connecting vehicles, as well as verifying and storing their data in a trusted way. Furthermore, much information must be leaked in order to correctly diagnose the vehicle and determine when or how to remotely update it. In this context, we propose a Blockchain-based, fully automated remote vehicle diagnosis system. The proposed system provides a secure and trusted way of storing and verifying vehicle data and analyzing their performance in different environments. Furthermore, we discuss many aspects of the benefits to different parties, such as the vehicle’s owner and manufacturers. Furthermore, a performance evaluation via simulation was performed on the proposed system using MATLAB Simulink to simulate both the vehicles and Blockchain and give a prototype for the system’s structure. In addition, OMNET++ was used to measure the expected system’s storage and throughput given some fixed parameters, such as sending the periodicity and speed. The simulation results showed that the throughput, end-to-end delay, and power consumption increased as the number of vehicles increased. In general, Original Equipment Manufacturers (OEMs) can implement this system by taking into consideration either increasing the storage to add more vehicles or decreasing the sending frequency to allow more vehicles to join. By and large, the proposed system is fully dynamic, and its configuration can be adjusted to satisfy the OEM’s needs since there are no specific constraints while implementing it. Full article

(This article belongs to the Special Issue Blockchain and Artificial Intelligence for Cyber Security in the Era of IoT and IIoT Applications)

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19 pages, 3017 KiB

Open AccessArticle

Scaling Up Security and Efficiency in Financial Transactions and Blockchain Systems

by Nazar Abbas Saqib and Shahad Talla AL-Talla

J. Sens. Actuator Netw. 2023, 12(2), 31; https://doi.org/10.3390/jsan12020031 - 03 Apr 2023

Cited by 2 | Viewed by 2209

Abstract

Blockchain, the underlying technology powering the Bitcoin cryptocurrency, is a distributed ledger that creates a distributed consensus on a history of transactions. Cryptocurrency transaction verification takes substantially longer than it does for conventional digital payment systems. Despite blockchain’s appealing benefits, one of its [...] Read more.

Blockchain, the underlying technology powering the Bitcoin cryptocurrency, is a distributed ledger that creates a distributed consensus on a history of transactions. Cryptocurrency transaction verification takes substantially longer than it does for conventional digital payment systems. Despite blockchain’s appealing benefits, one of its main drawbacks is scalability. Designing a solution that delivers a quicker proof of work is one method for increasing scalability or the rate at which transactions are processed. In this paper, we suggest a solution based on parallel mining rather than solo mining to prevent more than two miners from contributing an equal amount of effort to solving a single block. Moreover, we propose the idea of automatically selecting the optimal manager over all miners by using the particle swarm optimization (PSO) algorithm. This process solves many problems of blockchain scalability and makes the system more scalable by decreasing the waiting time if the manager fails to respond. Additionally, the proposed model includes the process of a reward system and the distribution of work. In this work, we propose the particle swarm optimization proof of work (PSO-POW) model. Three scenarios have been tested including solo mining, parallel mining without using the PSO process, and parallel mining using the PSO process (PSO-POW model) to ensure the power and robustness of the proposed model. This model has been tested using a range of case situations by adjusting the difficulty level and the number of peers. It has been implemented in a test environment that has all the qualities required to perform proof of work for Bitcoin. A comparison between three different scenarios has been constructed against difficulty levels and the number of peers. Local experimental assessments were carried out, and the findings show that the suggested strategy is workable, solves the scalability problems, and enhances the overall performance of the blockchain network. Full article

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31 pages, 33015 KiB

Open AccessEditor’s ChoiceReview

A Comprehensive Review of IoT Networking Technologies for Smart Home Automation Applications

by Vasilios A. Orfanos, Stavros D. Kaminaris, Panagiotis Papageorgas, Dimitrios Piromalis and Dionisis Kandris

J. Sens. Actuator Netw. 2023, 12(2), 30; https://doi.org/10.3390/jsan12020030 - 03 Apr 2023

Cited by 14 | Viewed by 5145

Abstract

The expediential increase in Internet communication technologies leads to its expansion to interests beyond computer networks. MEMS (Micro Electro Mechanical Systems) can now be smaller with higher performance, leading to tiny sensors and actuators with enhanced capabilities. WSN (Wireless Sensor Networks) and IoT [...] Read more.

The expediential increase in Internet communication technologies leads to its expansion to interests beyond computer networks. MEMS (Micro Electro Mechanical Systems) can now be smaller with higher performance, leading to tiny sensors and actuators with enhanced capabilities. WSN (Wireless Sensor Networks) and IoT (Internet of Things) have become a way for devices to communicate, share their data, and control them remotely. Machine-to-Machine (M2M) scenarios can be easily implemented as the cost of the components needed in that network is now affordable. Some of these solutions seem to be more affordable but lack important features, while other ones provide them but at a higher cost. Furthermore, there are ones that can cover great distances and surpass the limits of a Smart Home, while others are more specialized for operation in small areas. As there is a variety of choices available, a more consolidated view of their characteristics is needed to figure out the pros and cons of each of these technologies. As there are a great number of technologies examined in this paper, they are presented regarding their connectivity: Wired, Wireless, and Dual mode (Wired and Wireless). Their oddities are examined with metrics based on user interaction, technical characteristics, data integrity, and cost factor. In the last part of this article, a comparison of these technologies is presented as an effort to assist home automation users, administrators, or installers in making the right choice among them. Full article

(This article belongs to the Special Issue Applications of Wireless Sensor Networks: The State of the Art and Future Trends)

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19 pages, 3051 KiB

Open AccessEditor’s ChoiceArticle

Intrusion Detection System Using Feature Extraction with Machine Learning Algorithms in IoT

by Dhiaa Musleh, Meera Alotaibi, Fahd Alhaidari, Atta Rahman and Rami M. Mohammad

J. Sens. Actuator Netw. 2023, 12(2), 29; https://doi.org/10.3390/jsan12020029 - 29 Mar 2023

Cited by 23 | Viewed by 4573

Abstract

With the continuous increase in Internet of Things (IoT) device usage, more interest has been shown in internet security, specifically focusing on protecting these vulnerable devices from malicious traffic. Such threats are difficult to distinguish, so an advanced intrusion detection system (IDS) is [...] Read more.

With the continuous increase in Internet of Things (IoT) device usage, more interest has been shown in internet security, specifically focusing on protecting these vulnerable devices from malicious traffic. Such threats are difficult to distinguish, so an advanced intrusion detection system (IDS) is becoming necessary. Machine learning (ML) is one of the promising techniques as a smart IDS in different areas, including IoT. However, the input to ML models should be extracted from the IoT environment by feature extraction models, which play a significant role in the detection rate and accuracy. Therefore, this research aims to introduce a study on ML-based IDS in IoT, considering different feature extraction algorithms with several ML models. This study evaluated several feature extractors, including image filters and transfer learning models, such as VGG-16 and DenseNet. Additionally, several machine learning algorithms, including random forest, K-nearest neighbors, SVM, and different stacked models were assessed considering all the explored feature extraction algorithms. The study presented a detailed evaluation of all combined models using the IEEE Dataport dataset. Results showed that VGG-16 combined with stacking resulted in the highest accuracy of 98.3%. Full article

(This article belongs to the Special Issue Blockchain and Artificial Intelligence for Cyber Security in the Era of IoT and IIoT Applications)

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18 pages, 458 KiB

Open AccessArticle

A Rapid Review on the Use of Free and Open Source Technologies and Software Applied to Precision Agriculture Practices

by Rogério P. dos Santos, Nuno Fachada, Marko Beko and Valderi R. Q. Leithardt

J. Sens. Actuator Netw. 2023, 12(2), 28; https://doi.org/10.3390/jsan12020028 - 24 Mar 2023

Viewed by 2948

Abstract

Technology plays a crucial role in the management of natural resources in agricultural production. Free and open-source software and sensor technology solutions have the potential to promote more sustainable agricultural production. The goal of this rapid review is to find exclusively free and [...] Read more.

Technology plays a crucial role in the management of natural resources in agricultural production. Free and open-source software and sensor technology solutions have the potential to promote more sustainable agricultural production. The goal of this rapid review is to find exclusively free and open-source software for precision agriculture, available in different electronic databases, with emphasis on their characteristics and application formats, aiming at promoting sustainable agricultural production. A thorough search of the Google Scholar, GitHub, and GitLab electronic databases was performed for this purpose. Studies reporting and/or repositories containing up-to-date software were considered for this review. The various software packages were evaluated based on their characteristics and application formats. The search identified a total of 21 free and open-source software packages designed specifically for precision agriculture. Most of the identified software was shown to be extensible and customizable, while taking into account factors such as transparency, speed, and security, although some limitations were observed in terms of repository management and source control. This rapid review suggests that free and open-source software and sensor technology solutions play an important role in the management of natural resources in sustainable agricultural production, and highlights the main technological approaches towards this goal. Finally, while this review performs a preliminary assessment of existing free and open source solutions, additional research is needed to evaluate their effectiveness and usability in different scenarios, as well as their relevance in terms of environmental and economic impact on agricultural production. Full article

(This article belongs to the Special Issue Internet of Things for Smart Agriculture)

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14 pages, 814 KiB

Open AccessArticle

Machine Learning-Based Detection for Unauthorized Access to IoT Devices

by Malak Aljabri, Amal A. Alahmadi, Rami Mustafa A. Mohammad, Fahd Alhaidari, Menna Aboulnour, Dorieh M. Alomari and Samiha Mirza

J. Sens. Actuator Netw. 2023, 12(2), 27; https://doi.org/10.3390/jsan12020027 - 20 Mar 2023

Cited by 4 | Viewed by 3146

Abstract

The Internet of Things (IoT) has become widely adopted in businesses, organizations, and daily lives. They are usually characterized by transferring and processing sensitive data. Attackers have exploited this prospect of IoT devices to compromise user data’s integrity and confidentiality. Considering the dynamic [...] Read more.

The Internet of Things (IoT) has become widely adopted in businesses, organizations, and daily lives. They are usually characterized by transferring and processing sensitive data. Attackers have exploited this prospect of IoT devices to compromise user data’s integrity and confidentiality. Considering the dynamic nature of the attacks, artificial intelligence (AI)-based techniques incorporating machine learning (ML) are promising techniques for identifying such attacks. However, the dataset being utilized features engineering techniques, and the kind of classifiers play significant roles in how accurate AI-based predictions are. Therefore, for the IoT environment, there is a need to contribute more to this context by evaluating different AI-based techniques on datasets that effectively capture the environment’s properties. In this paper, we evaluated various ML models with the consideration of both binary and multiclass classification models validated on a new dedicated IoT dataset. Moreover, we investigated the impact of different features engineering techniques including correlation analysis and information gain. The experimental work conducted on bagging, k-nearest neighbor (KNN), J48, random forest (RF), logistic regression (LR), and multi-layer perceptron (MLP) models revealed that RF achieved the highest performance across all experiment sets, with a receiver operating characteristic (ROC) of 99.9%. Full article

(This article belongs to the Special Issue Feature Papers in Network Security and Privacy)

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29 pages, 1791 KiB

Open AccessArticle

Fusion Objective Function on Progressive Super-Resolution Network

by Amir Hajian and Supavadee Aramvith

J. Sens. Actuator Netw. 2023, 12(2), 26; https://doi.org/10.3390/jsan12020026 - 20 Mar 2023

Cited by 2 | Viewed by 1546

Abstract

Recent advancements in Single-Image Super-Resolution (SISR) have explored the network architecture of deep-learning models to achieve a better perceptual quality of super-resolved images. However, the effect of the objective function, which contributes to improving the performance and perceptual quality of super-resolved images, has [...] Read more.

Recent advancements in Single-Image Super-Resolution (SISR) have explored the network architecture of deep-learning models to achieve a better perceptual quality of super-resolved images. However, the effect of the objective function, which contributes to improving the performance and perceptual quality of super-resolved images, has not gained much attention. This paper proposes a novel super-resolution architecture called Progressive Multi-Residual Fusion Network (PMRF), which fuses the learning objective functions of

L 2

and Multi-Scale SSIM in a progressively upsampling framework structure. Specifically, we propose a Residual-in-Residual Dense Blocks (RRDB) architecture on a progressively upsampling platform that reconstructs the high-resolution image during intermediate steps in our super-resolution network. Additionally, the Depth-Wise Bottleneck Projection allows high-frequency information of early network layers to be bypassed through the upsampling modules of the network. Quantitative and qualitative evaluation of benchmark datasets demonstrate that the proposed PMRF super-resolution algorithm with novel fusion objective function (

L 2

and MS-SSIM) improves our model’s perceptual quality and accuracy compared to other state-of-the-art models. Moreover, this model demonstrates robustness against noise degradation and achieves an acceptable trade-off between network efficiency and accuracy. Full article

(This article belongs to the Topic Advanced Signal Processing and Data Analysis for Smart IoT Ecosystems)

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4 pages, 198 KiB

Open AccessEditorial

Smart Cities and Homes: Current Status and Future Possibilities

by Subhas Mukhopadhyay and Nagender Kumar Suryadevara

J. Sens. Actuator Netw. 2023, 12(2), 25; https://doi.org/10.3390/jsan12020025 - 16 Mar 2023

Cited by 4 | Viewed by 2575

Abstract

The advancement of sensing technologies, embedded systems, wireless communication technologies, nanomaterials, miniaturization, vision sensing and processing speed have made it possible to develop smart technologies that can generate data seamlessly [...] Full article

(This article belongs to the Special Issue Smart Cities and Homes: Current Status and Future Possibilities)

30 pages, 15900 KiB

Open AccessArticle

A Quadruple Notch UWB Antenna with Decagonal Radiator and Sierpinski Square Fractal Slots

by Om Prakash Kumar, Pramod Kumar, Tanweer Ali, Pradeep Kumar and Subhash B. K

J. Sens. Actuator Netw. 2023, 12(2), 24; https://doi.org/10.3390/jsan12020024 - 14 Mar 2023

Cited by 3 | Viewed by 1299

Abstract

A novel quadruple-notch UWB (ultrawideband) antenna for wireless applications is presented. The antenna consists of a decagonal-shaped radiating part with Sierpinski square fractal slots up to iteration 3. The ground part is truncated and loaded with stubs and slots. Each individual stub at [...] Read more.

A novel quadruple-notch UWB (ultrawideband) antenna for wireless applications is presented. The antenna consists of a decagonal-shaped radiating part with Sierpinski square fractal slots up to iteration 3. The ground part is truncated and loaded with stubs and slots. Each individual stub at the ground plane creates/controls a particular notch band. Initially, a UWB antenna is designed with the help of truncation at the ground plane. Miniaturization in this design is achieved with the help of Sierpinski square fractal slots. Additionally, these slots help improve the UWB impedance bandwidth. This design is then extended to achieve a quadruple notch by loading the ground with various rectangular-shaped stubs. The final antenna shows the UWB range from 4.21 to 13.92 GHz and notch frequencies at 5.02 GHz (C-band), 7.8 GHz (satellite band), 9.03, and 10.86 GHz (X-band). The simulated and measured results are nearly identical, which shows the efficacy of the proposed design. Full article

(This article belongs to the Topic Electronic Communications, IOT and Big Data)

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15 pages, 5320 KiB

Open AccessArticle

Automated and Optimized Regression Model for UWB Antenna Design

by Sameena Pathan, Praveen Kumar, Tanweer Ali and Pradeep Kumar

J. Sens. Actuator Netw. 2023, 12(2), 23; https://doi.org/10.3390/jsan12020023 - 10 Mar 2023

Cited by 3 | Viewed by 1764

Abstract

Antenna design involves continuously optimizing antenna parameters to meet the desired requirements. Since the process is manual, laborious, and time-consuming, a surrogate model based on machine learning provides an effective solution. The conventional approach for selecting antenna parameters is mapped to a regression [...] Read more.

Antenna design involves continuously optimizing antenna parameters to meet the desired requirements. Since the process is manual, laborious, and time-consuming, a surrogate model based on machine learning provides an effective solution. The conventional approach for selecting antenna parameters is mapped to a regression problem to predict the antenna performance in terms of S parameters. In this regard, a heuristic approach is employed using an optimized random forest model. The design parameters are obtained from an ultrawideband (UWB) antenna simulated using the high-frequency structure simulator (HFSS). The designed antenna is an embedded structure consisting of a circular monopole with a rectangle. The ground plane of the proposed antenna is reduced to realize the wider impedance bandwidth. The lowered ground plane will create a new current channel that affects the uniform current distribution and helps in achieving the wider impedance bandwidth. Initially, data were preprocessed, and feature extraction was performed using additive regression. Further, ten different regression models with optimized parameters are used to determine the best values for antenna design. The proposed method was evaluated by splitting the dataset into train and test data in the ratio of 60:40 and by employing a ten-fold cross-validation scheme. A correlation coefficient of 0.99 was obtained using the optimized random forest model. Full article

(This article belongs to the Topic Electronic Communications, IOT and Big Data)

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16 pages, 1835 KiB

Open AccessArticle

Determining Commercial Parking Vacancies Employing Multiple WiFiRSSI Fingerprinting Method

by Elmer Magsino, Juan Miguel Carlo Barrameda, Andrei Puno, Spencer Ong, Cyrill Siapco and Jolo Vibal

J. Sens. Actuator Netw. 2023, 12(2), 22; https://doi.org/10.3390/jsan12020022 - 10 Mar 2023

Cited by 3 | Viewed by 1380

Abstract

In this study, we implemented a parking occupancy/vacancy detection system (POVD) in a scaled-down model of a parking system for commercial centers by employing multiple WiFi access points. By exploiting the presence of WiFi routers installed in a commercial establishment, the WiFi’s received [...] Read more.

In this study, we implemented a parking occupancy/vacancy detection system (POVD) in a scaled-down model of a parking system for commercial centers by employing multiple WiFi access points. By exploiting the presence of WiFi routers installed in a commercial establishment, the WiFi’s received signal strength indicator (RSSI) signals were collected to establish the parking fingerprints and then later used to predict the number of occupied/vacant slots. Our extensive experiments were divided into two phases, namely: offline training and online matching phases. During the offline stage, the POVD collects available WiFi RSSI readings to determine the parking lot’s fingerprint based on a given scenario and stores them in a fingerprint database that can be updated periodically. On the other hand, the online stage predicts the number of available parking slots based on the actual scenario compared to the stored database. We utilized multiple router setups in generating WiFi signals and exhaustively considered all possible parking scenarios given the combination of 10 maximum access points and 10 cars. From two testing locations, our results showed that, given a parking area dimension of 13.40 m

^{2}

and 6.30 m

^{2}

and with the deployment of 4 and 10 routers, our system acquired the best accuracy of 88.18% and 100%, respectively. Moreover, the developed system serves as experiential evidence on how to exploit the available WiFi RSSI readings towards the realization of a smart parking system. Full article

(This article belongs to the Special Issue Smart Cities and Homes: Current Status and Future Possibilities)

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25 pages, 3510 KiB

Open AccessArticle

Hybrid Deep Learning-Based Intrusion Detection System for RPL IoT Networks

by Yahya Al Sawafi, Abderezak Touzene and Rachid Hedjam

J. Sens. Actuator Netw. 2023, 12(2), 21; https://doi.org/10.3390/jsan12020021 - 08 Mar 2023

Cited by 5 | Viewed by 1987

Abstract

Internet of things (IoT) has become an emerging technology transforming everyday physical objects to be smarter by using underlying technologies such as sensor networks. The routing protocol for low-power and lossy networks (RPL) is considered one of the promising protocols designed for the [...] Read more.

Internet of things (IoT) has become an emerging technology transforming everyday physical objects to be smarter by using underlying technologies such as sensor networks. The routing protocol for low-power and lossy networks (RPL) is considered one of the promising protocols designed for the IoT networks. However, due to the constrained nature of the IoT devices in terms of memory, processing power, and network capabilities, they are exposed to many security attacks. Unfortunately, the existing Intrusion Detection System (IDS) approaches using machine learning that have been proposed to detect and mitigate security attacks in internet networks are not suitable for analyzing IoT traffics. This paper proposed an IDS system using the hybridization of supervised and semi-supervised deep learning for network traffic classification for known and unknown abnormal behaviors in the IoT environment. In addition, we have developed a new IoT specialized dataset named IoTR-DS, using the RPL protocol. IoTR-DS is used as a use case to classify three known security attacks (DIS, Rank, and Wormhole). The proposed Hybrid DL-Based IDS is evaluated and compared to some existing ones, and the results are promising. The evaluation results show an accuracy detection rate of 98% and 92% in f1-score for multi-class attacks when using pre-trained attacks (known traffic) and an average accuracy of 95% and 87% in f1-score when predicting untrained attacks for two attack behaviors (unknown traffic). Full article

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39 pages, 1795 KiB

Open AccessArticle

Telemedicine: A Survey of Telecommunication Technologies, Developments, and Challenges

by Caroline Omoanatse Alenoghena, Henry Ohiani Ohize, Achonu Oluwole Adejo, Adeiza James Onumanyi, Emmanuel Esebanme Ohihoin, Aliyu Idris Balarabe, Supreme Ayewoh Okoh, Ezra Kolo and Benjamin Alenoghena

J. Sens. Actuator Netw. 2023, 12(2), 20; https://doi.org/10.3390/jsan12020020 - 02 Mar 2023

Cited by 11 | Viewed by 10467

Abstract

The emergence of the COVID-19 pandemic has increased research outputs in telemedicine over the last couple of years. One solution to the COVID-19 pandemic as revealed in literature is to leverage telemedicine for accessing health care remotely. In this survey paper, we review [...] Read more.

The emergence of the COVID-19 pandemic has increased research outputs in telemedicine over the last couple of years. One solution to the COVID-19 pandemic as revealed in literature is to leverage telemedicine for accessing health care remotely. In this survey paper, we review several articles on eHealth and Telemedicine with emphasis on the articles’ focus area, including wireless technologies and architectures in eHealth, communications protocols, Quality of Service, and Experience Standards, among other considerations. In addition, we provide an overview of telemedicine for new readers. This survey reviews several telecommunications technologies currently being proposed along with their standards and challenges. In general, an encompassing survey on the developments in telemedicine technology, standards, and protocols is presented while acquainting researchers with several open issues. Special mention of the state-of-the-art specialist application areas are presented. We conclude the survey paper by presenting important research challenges and potential future directions as they pertain to telemedicine technology. Full article

(This article belongs to the Topic Intelligent Health Monitoring and Assistance Systems and Frameworks)

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27 pages, 11909 KiB

Open AccessEditor’s ChoiceArticle

A Novel Multi Algorithm Approach to Identify Network Anomalies in the IoT Using Fog Computing and a Model to Distinguish between IoT and Non-IoT Devices

by Rami J. Alzahrani and Ahmed Alzahrani

J. Sens. Actuator Netw. 2023, 12(2), 19; https://doi.org/10.3390/jsan12020019 - 28 Feb 2023

Cited by 8 | Viewed by 2295

Abstract

Botnet attacks, such as DDoS, are one of the most common types of attacks in IoT networks. A botnet is a collection of cooperated computing machines or Internet of Things gadgets that criminal users manage remotely. Several strategies have been developed to reduce [...] Read more.

Botnet attacks, such as DDoS, are one of the most common types of attacks in IoT networks. A botnet is a collection of cooperated computing machines or Internet of Things gadgets that criminal users manage remotely. Several strategies have been developed to reduce anomalies in IoT networks, such as DDoS. To increase the accuracy of the anomaly mitigation system and lower the false positive rate (FPR), some schemes use statistical or machine learning methodologies in the anomaly-based intrusion detection system (IDS) to mitigate an attack. Despite the proposed anomaly mitigation techniques, the mitigation of DDoS attacks in IoT networks remains a concern. Because of the similarity between DDoS and normal network flows, leading to problems such as a high FPR, low accuracy, and a low detection rate, the majority of anomaly mitigation methods fail. Furthermore, the limited resources in IoT devices make it difficult to implement anomaly mitigation techniques. In this paper, an efficient anomaly mitigation system has been developed for the IoT network through the design and implementation of a DDoS attack detection system that uses a statistical method that combines three algorithms: exponentially weighted moving average (EWMA), K-nearest neighbors (KNN), and the cumulative sum algorithm (CUSUM). The integration of fog computing with the Internet of Things has created an effective framework for implementing an anomaly mitigation strategy to address security issues such as botnet threats. The proposed module was evaluated using the Bot-IoT dataset. From the results, we conclude that our model has achieved a high accuracy (99.00%) with a low false positive rate (FPR). We have also achieved good results in distinguishing between IoT and non-IoT devices, which will help networking teams make the distinction as well. Full article

(This article belongs to the Topic Internet of Things: Latest Advances)

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J. Sens. Actuator Netw., Volume 12, Issue 2 (April 2023) – 18 articles

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