Journal of Sensor and Actuator Networks

The microgrid is a group of smaller renewable energy sources (REs), which act in a coordinated manner to provide the required amount of active power and additional services when required. This article proposes coordinated power management for a microgrid with the integration of solar PV plants with maximum power point tracking (MPPT) to enhance power generation and conversion using a hybrid MPPT method based on particle swarm optimization-adaptive neuro-fuzzy inference system (PSO-ANFIS) to acquire rapid and maximum PV power along with battery energy storage control to maintain the stable voltage and frequency (V-f) of an isolated microgrid. In addition, it is proposed to provide active and reactive power (P-Q) regulation for the grid connected. The approach used provides more regulation due to the least root mean square error (RMSE), which improves photovoltaic (PV) potential extraction. The comparison results of the PSO-ANFIS and P&O controllers of the MPPT and the controller of the energy storage devices combined with the V-f (or P-Q) controller of the inverter all show effective coordination between the control systems. This is the most important need for contemporary microgrids, considering the potential of changing irradiance in the grid following mode, the grid forming mode under an island scenario, and back-to-grid synchronization. With the test model, the islanded and grid-islanded-grid connected modes are investigated separately. The results demonstrate conclusively that the proposed strategies are effective. To run the simulations, MATLAB and SimPowerSystems are utilized. Full article

Future mobile network operators and telecommunications authorities aim to provide reliable network coverage. Signal strength, normally assessed using standard drive tests over targeted areas, is an important factor strongly linked to user satisfaction. Drive tests are, however, time-consuming, expensive, and can be dangerous in hard-to-reach areas. An alternative safe method involves using drones or unmanned aerial vehicles (UAVs). The objective of this study was to use a drone to measure signal strength at discrete points a few meters above the ground and an artificial neural network (ANN) for processing the measured data and predicting signal strength at ground level. The drone was equipped with low-cost data logging equipment. The ANN was also used to classify specific ground locations in terms of signal coverage into poor, fair, good, and excellent. The data used in training and testing the ANN were collected by a measurement unit attached to a drone in different areas of Sultan Qaboos University campus in Muscat, Oman. A total of 12 locations with different topologies were scanned. The proposed method achieved an accuracy of 97% in predicting the ground level coverage based on measurements taken at higher altitudes. In addition, the performance of the ANN in predicting signal strength at ground level was evaluated using several test scenarios, achieving less than 3% mean square error (MSE). Additionally, data taken at different angles with respect to the vertical were also tested, and the prediction MSE was found to be less than approximately 3% for an angle of 68 degrees. Additionally, outdoor measurements were used to predict indoor coverage with an MSE of less than approximately 6%. Furthermore, in an attempt to find a globally accurate ANN module for the targeted area, all zones’ measurements were cross-tested on ANN modules trained for different zones. It was evaluated that, within the tested scenarios, an MSE of less than approximately 10% can be achieved with an ANN module trained on data from only one zone. Full article

As next-generation networks begin to take shape, the necessity of Optical Transport Networks (OTNs) in helping achieve the performance requirements of future networks is evident. Future networks are characterized as being data-centric and are expected to have ubiquitous artificial intelligence integration and deployment. To this end, the efficient and timely transportation of fresh data from producer to consumer is critical. The work presented in this paper outlines the role of OTNs in future networking generations. Furthermore, key emerging OTN technologies are discussed. Additionally, the role intelligence will play in the Management and Orchestration (MANO) of next-generation OTNs is discussed. Moreover, a set of challenges and opportunities for innovation to guide the development of future OTNs is considered. Finally, a use case illustrating the impact of network dynamicity and demand uncertainty on OTN MANO decisions is presented. Full article

The current work is an illustration of an empirical investigation conducted on a pharmaceutical water treatment plant that subsequently proposes potential ICT implications for optimizing the plant’s conventional operating procedures and improving production efficiency. Typically, the pilot plant incorporates a standard infrastructure for maintaining quality and production goals. In the study, a schematic of the reverse osmosis section of the pilot treatment plant was developed. A mathematical modeling and process simulation approach was adopted to carry out the linear process investigation and validation of key performance parameters. The study’s findings reveal that the performance and lifecycle of the RO treatment unit are primarily determined via the structured pre-treatment filtering procedures, including critical parameters such as volumetric flowrate, solute concentrations, and differential pressure across the membrane. These operational parameters were also found to be instrumental in increasing plant production and improving equipment efficiency. Based on our results, the study proposes cost-effective ICT implications for plant managers through which pilot organization can substantially save on their annual water and energy consumption. Full article

The advancement in technology has led to the integration of internet-connected devices and systems into emergency management and response, known as the Internet of Emergency Services (IoES). This integration has the potential to revolutionize the way in which emergency services are provided, by allowing for real-time data collection and analysis, and improving coordination among various agencies involved in emergency response. This paper aims to explore the use of IoES in emergency response and disaster management, with an emphasis on the role of sensors and IoT devices in providing real-time information to emergency responders. We will also examine the challenges and opportunities associated with the implementation of IoES, and discuss the potential impact of this technology on public safety and crisis management. The integration of IoES into emergency management holds great promise for improving the speed and efficiency of emergency response, as well as enhancing the overall safety and well-being of citizens in emergency situations. However, it is important to understand the possible limitations and potential risks associated with this technology, in order to ensure its effective and responsible use. This paper aims to provide a comprehensive understanding of the Internet of Emergency Services and its implications for emergency response and disaster management. Full article

Internet of Things technologies use many sensors combined with wireless networks for cyber-physical systems in various applications. Mobility is an essential characteristic for many objects that use sensors. In mobile sensor networks, the availability of communication channels is crucial, especially for mission-critical applications. This article presents models for analyzing the availability of sensor services in a wireless network with aerial base station placement (ABSP), considering the real conditions for using unmanned aerial vehicles (UAVs). The studied system uses a UAV-assisted mobile edge computing architecture, including ABSP and a ground station for restoring the energy capacity of the UAVs, to maintain the availability of interaction with the sensors. The architecture includes a fleet of additional replacement UAVs to ensure continuous communication coverage for the sensor network during the charging period of the air-based station UAVs. Analytical expressions were obtained to determine the availability of sensor services in the system studied. Full article

Low-power wide-area networks (LPWANs) constitute a variety of modern-day Internet of Things (IoT) applications. Long range (LoRa) is a promising LPWAN technology with its long-range and low-power benefits. Performance enhancement of LoRa networks is one of the crucial challenges to meet application requirements, and it primarily depends on the optimal selection of transmission parameters. Reinforcement learning-based multi-armed bandit (MAB) is a prominent approach for optimizing the LoRa parameters and network performance. In this work, we propose a new discounted upper confidence bound (DUCB) MAB to maximize energy efficiency and improve the overall performance of the LoRa network. We designed novel discount and exploration bonus functions to maximize the policy rewards to increase the number of successful transmissions. The results show that the proposed discount and exploration functions give better mean rewards irrespective of the number of trials, which has significant importance for LoRa networks. The designed policy outperforms other policies reported in the literature and has a lesser time complexity, a comparable mean rewards, and improves the mean rewards by a minimum of 8%. Full article

A compact circular structured monopole antenna for ultrawideband (UWB) and UWB dual-band notch applications is designed and fabricated on an FR4 substrate. The UWB antenna has a hybrid configuration of the circle and three ellipses as the radiating plane and less than a quarter-lowered ground plane. The overall dimensions of the projected antennas are 16 × 11 × 1.6 mm³, having a −10 dB impedance bandwidth of 113% (3.7–13.3 GHz). Further, two frequency band notches were created using two inverted U-shaped slots on the radiator. These slots notch the frequency band from 5–5.6 GHz and 7.3–8.3 GHz, covering IEEE 802.11, Wi-Fi, WLAN, and the entire X-band satellite communication. A comprehensive frequency and time domain analysis is performed to validate the projected antenna design’s effectiveness. In addition, a circuit model of the projected antenna design is built, and its performance is evaluated. Furthermore, unlike the traditional technique, which uses the simulated surface current distribution to verify functioning, characteristic mode analysis (CMA) is used to provide deeper insight into distinct modes on the antenna. Full article

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

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

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

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

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

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

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

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

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

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