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The Internet of Things refers to network of physical objects that use IP address for Internet connectivity and to communicate with other Internet-enabled devices and systems. The Internet of Things comes with a number of top-class applications and has also developed many things into smart devices. The enhanced objects would ultimately need to have...
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Smart Grid is a way of providing bidirectional energy flow with the integration of latest communication technologies and advanced control methods to overcome the issues associated with the current power grid such as unidirectional power flow, resource wastage, reliability, security, enhanced quality, and increasing demand of energy. Integrating Int...
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... These connected devices include every device and node capable of linking to the internet under supervision. With the initiation of IoT, there has been a growing impact on everyday life in terms of connectivity, speed, robustness, efficiency, development, and timely decision-making [2]. The changing trends and legal frameworks have highlighted the need to enhance security among these connected components. ...
The Internet of Things (IoT) has revolutionized how people involve with technological innovations. However, this development has also brought up significant security concerns. The increasing number of IoT attacks poses a serious risk to individuals and businesses equally. In response, this article introduces an ensemble feature engineering method for effective feature selection, based on a systematic behavioral analysis by means of artificial intelligence. This method identifies and highlights the most relevant features from IoT botnet dataset, facilitating accurate detection of both malicious and benign traffic. To detect IoT botnet attacks, the ensemble feature engineering method incorporates distinct approaches, including a genetic algorithm‐based genetic approach, filter selection methods such as mutual information, LASSO regularization, and forward‐backward search. A merger approach then combines these results, addressing redundancy and irrelevance. As well, a wrapper algorithm called recursive feature removal is applied to further refine the feature selection process. The effectiveness of the selected feature set is validated by means of deep learning algorithms (CNN, RNN, LSTM, and GRU) rooted in artificial intelligence, and applied to the IoT‐Botnet 2020 dataset. Results demonstrate encouraging performance, with precision between 97.88% and 98.99%, recall scores between 99.10% and 99.95%, detection accuracy between 98.05% and 99.21%, and an F1‐score ranging from 98.45% to 99.82%. Moreover, the ensemble feature engineering approach achieved precision of 98.26%, recall score of 99.68%, detection accuracy of 98.49%, F1‐measure of 99.00%, an AUC‐ROC of 82.37% and specificity of 98.38%. These outcomes highlight the method's robust performance in identifying both malicious and benign IoT botnet traffic.
... This architecture aims to address these challenges and leverage the benefits of IoT technology to enhance the efficiency, reliability, and sustainability of the smart The motivation for developing a power internet of things (IoT) architecture intended for a smart grid demand scheme arises from various challenges and opportunities in the energy sector. This architecture aims to address these challenges and leverage the benefits of IoT technology to enhance the efficiency, reliability, and sustainability of the smart grid's demand management as shown in Figure 2 [11]. Some of the key motivations include: ...
... Sci. 2024, 14, x FOR PEERREVIEW 4 Commonly used IoT technologies[11]. ...
... Commonly used IoT technologies[11]. ...
The global energy sector is currently undergoing a significant transformation to address sustainability, energy efficiency, and grid resilience. Smart grids, leveraging advanced technologies like the power internet of things (PIoT), play a crucial role in this transformation. This research focuses on enhancing the efficiency, reliability, and sustainability of electricity distribution through IoT technologies. It envisions a system where interconnected devices, sensors, and data analytics optimize energy consumption, monitor grid conditions, and manage demand response scenarios. Central to this effort is the integration of PIoT into the smart grid infrastructure, particularly in implementing dynamic pricing strategies for demand response. Leveraging power line communication (PLC) techniques, this innovative approach facilitates real-time communication between grid components and consumers. The results demonstrate improved grid stability through dynamic load management, effectively responding to demand fluctuations, and minimizing disruptions. The deployment of dynamic pricing methods using PLC-driven schemes empowers customers by offering access to real-time energy use data. This access incentivizes energy-efficient behavior. leading to a 30% increase in the adoption of energy-saving techniques among consumers. A utility company pilot study claimed a 12% drop in peak demand after adopting time-of-use charges, with an accuracy rate of 98.87% in total.
... LPWAN protocols provide low data throughput, ensuring low power consumption [3]. Additionally, LPWAN technologies are capable of transmitting data between devices at distances of up to 10 km [4]. ...
In the past decade, with the rapid proliferation of Internet of Things (IoT) devices new applications have emerged in diverse fields such as agriculture, smart cities, and healthcare. In this context, LoRaWAN, a Low-Power Wide Area Network protocol, has gained significant adoption. Its advantages, including low-power consumption and long-range communication capabilities, make it an ideal solution for IoT communication. Simulation tools have played a crucial role in facilitating experimentation and advancing the implementation of the LoRaWAN protocol. However, effectively modelling these experiments present a significant challenge. Through a comprehensive survey of the literature on LoRaWAN, we have identified variations in parameter configuration among different works and observed a lack of sufficient information in most of them, thereby impeding result reproducibility. This paper offers a comprehensive overview of LoRaWAN technology, encompassing its architecture and underlying technologies such as LoRa and LPWAN. Furthermore, we present and compare the main simulation tools currently available for LoRaWAN, discuss the parameters that significantly impact the performance of a LoRaWAN network, and explore how researchers have configured these parameters to model their simulation experiments.
... Networks based on low-power technologies enables the development of long-distance wireless topologies with low energy consumption (Bahashwan et al., 2021;Kabalci & Ali, 2019). Due to its application in the IoT (Internet of Things), the implementation of topologies based on these technologies has grown significantly in recent years. ...
This work presents the employing of LoRaWAN (Long Range Wide Area Network) for location applications through a network simulation to determine a mobile node position. We rely on FLoRa (Framework for LoRa) and OMNeT++ (Objective Modular Network Testbed in C++) simulator, which uses Python feature tools, following the calculation of node placement using the trilateration technique. Our method differs from others in that we calculate the FLoRa power loss and determine different simulation settings using the shadowing feature of the log-distance path loss model. We approached RSSI (Received Signal Strength Indicator) to measure the distance between the LoRa gateways and the nodes, establishing a link between these parameters. Our work aims to promote the integration of open-source tools for verifying signal intensity values based on node distance from gateways. We consider it useful for engineers in predicting signal behaviors according to topology and settings variations. During the experimentation, the network underwent different performances according to the transmission parameters considered during the simulation. This was critical when increasing the number of mobile nodes, leading to consuming computer capacity and resources. Through repetition of tests, we confirmed the lower intensity of the received signal as the node moves to farther positions, reaching consistent power indicators and positioning accuracy. Overall, the results show that LoRaWAN integrated with trilateration techniques can be practical in providing adequate performance for node positioning accuracy and long-distance communication with low power consumption.
... The ultrasound communication has also been suggested for indoor IoT in [33], which offers 64 bps for a distance of 50 cm. The other widely used IoT communication technologies, namely Bluetooth Low Energy, RF Identification, Near Field Communication, ZigBee, Liquid Crystal Shutters, hybrid VLC/IRC and hybrid Backscattering, have different data rates and link length support [35][36][37][38][39]. ...
This paper presents an energy-efficient design of optical wireless communication (OWC) system for the indoor Internet of Things (IoT) with the assistance of machine learning (ML). A central coordinator (CC) has been proposed to interrogate the IoT devices and control the uplink formations with the prediction of transmission quality using ML classifiers. The passive reflective reflectors (PRF) are utilized in the IoT devices, which replaced the power-consuming active transmitters, formulate the zero-power consuming transmission links. The communication performance of the passive link establishments from the IoT devices have been investigated in terms of quality factor (Q-factor), bit error rate (BER), and signal-to-noise ratio (SNR) under different optical wireless channel conditions and link lengths. The ML classifiers have been evaluated on the prediction of transmission quality, and the results suggested the Euclidean K-nearest neighbor (KNN) with ten number of neighbors for the implementation. The IoT devices located within 1.2 m from the CC require a transmission power of 0.5 mW for links carrying 10 Gbps data, which increases the energy efficiency to 20 Gbps/mW with transmission energy consumption of 0.05 pJ/bit. This significant improvement in energy efficiency and passive communication ensures reliable, and green IoT links suitable for data-intensive indoor applications.
... For the support of the communication between the nodes on a wireless sensor network, different wireless technologies are used depending on the application. On the one hand, short-range wireless technologies such as Wi-Fi and ZigBee support the deployment of a wireless sensor network on a smaller scale with a maximum wireless range of 100 m [7]. For example, a short-range wireless sensor network can be deployed on a smart industry facility. ...
... On the other hand, long-range wireless technologies such as LoRa, NB-IoT, or other LPWAN technologies support wireless communication over long distances with low energy consumption. On a long-range wireless sensor network, the distance between two nodes can reach up to a few kilometers [7]. These long-range wireless communications are utilized in applications such as precision agriculture, in which battery-powered devices are deployed over large agricultural fields, and therefore, as we present a smart agriculture use-case, this work focuses on large-scale LoRa wireless sensor networks covering long distances. ...
In the evolving landscape of Industry 4.0, the convergence of peer-to-peer (P2P) systems, LoRa-enabled wireless sensor networks (WSNs), and distributed hash tables (DHTs) represents a major advancement that enhances sustainability in the modern agriculture framework and its applications. In this study, we propose a P2P Chord-based ecosystem for sustainable and smart agriculture applications, inspired by the inner workings of the Chord protocol. The node-centric approach of WiCHORD+ is a standout feature, streamlining operations in WSNs and leading to more energy-efficient and straightforward system interactions. Instead of traditional key-centric methods, WiCHORD+ is a node-centric protocol that is compatible with the inherent characteristics of WSNs. This unique design integrates seamlessly with distributed hash tables (DHTs), providing an efficient mechanism to locate nodes and ensure robust data retrieval while reducing energy consumption. Additionally, by utilizing the MAC address of each node in data routing, WiCHORD+ offers a more direct and efficient data lookup mechanism, essential for the timely and energy-efficient operation of WSNs. While the increasing dependence of smart agriculture on cloud computing environments for data storage and machine learning techniques for real-time prediction and analytics continues, frameworks like the proposed WiCHORD+ appear promising for future IoT applications due to their compatibility with modern devices and peripherals. Ultimately, the proposed approach aims to effectively incorporate LoRa, WSNs, DHTs, cloud computing, and machine learning, by providing practical solutions to the ongoing challenges in the current smart agriculture landscape and IoT applications.
... Part of the current success of IoT is its incorporation to Low Power Wide Area Networks (LPWAN), which allows to extend the coverage of IoT applications to low power consumption devices and networks [3][4][5][6][7][8][9][10]. In turn, it has allowed to increase exponentially the possible applications of IoT applications, which now heavily rely in the transmission of data in real time [11]. One of the most promising applications of IoT working under the LPWAN principles is the development of sparse IoT networks with a long-range coverage [12][13][14][15][16][17][18]. ...
The development of wireless communication systems has had a great increase due to the needs of digital services that are increasingly demanded by today’s societies. Wireless networks have become the most used systems by an increasingly growing number of users. In this context, the same wireless networks have diversified; proof of this are the solutions known as IoT (Internet of Things) based on LPWAN networks (Low Power Wide Area Network), which are adaptable, of low cost and relatively low development complexity, which have the potential to be used in a wide type of applications. Despite the advantages that they represent and the benefits that they could provide, there are still challenges to overcome in terms of design and implementation. One of the main aspects that require attention is the development and adaptation of an information (data) encapsulation structure according to the needs of the system and the application, since such an information must be transported safely, fully and efficiently, always respecting the limitations of the communications system. This paper proposes a data framing protocol structure based on an LPWAN/IoT monitoring system, which is based on COTS components and has the function of acquiring environmental variables.
... Basically, big data analysis is used and has developed many techniques to extract all the information that is useful and hidden in large amounts of spread data [3,4]. Big data has taken nowadays a renewed interest with the emergence and popularisation of the Internet of Things (IoT) concept along with the emergence of Low Power Wide Area Networks (LPWAN) [5], which have made excessively affordable to build and deploy extremely large sensors and nodes networks which are continuously generating massive amounts of data which requires big data techniques for them to provide reliable and robust forecasting [6,7,8]. ...
... Ozone O3 Suspended particles PM2. 5 Lead Pb UV Index ...
Currently, due to the increase in the use of accessible electronic devices and elements for the implementation of data acquisition networks, data acquisition of variables for specific studies can have significant dimensions, hindering the processing and analysis of massive datasets. This is the case of meteorological, pollutants and atmospheric chemestry data in Mexico City, which are available through the Sistema de Monitoreo Atmosf´erico (SIMAT). The hourly records of the time series recorded at SIMAT, considering different variables acquired in different meteorological stations distributed throughout Mexico City, consist of massive records that bring together more than 300 million entries, turning it into a dataset which is impossible to manage using conventional tools such as spreadsheets or plain text editors. The possibility of improving the way in which massive amounts of data are treated is an area with various application opportunities, where areas such as Big Data, prediction and processing are found, since it represents a considerable reduction in work while allowing information organization in a logc and precise way. This work proposes the implementation and use of relational databases to manage, organize and store large amounts of data using the SQL query language. The proposal will allow fast and practically real-time access to highly massive databases in Earth Sciences and/or related areas.
... IoT devices bring unparalleled convenience into our daily routines, enabling remote control of household appliances, enhancing energy efficiency, and facilitating seamless access to information. This remarkable growth, coupled with the promise of elevated living standards, underscores the profound significance of IoT in our modern age [1]. ...
The significant surge in Internet of Things (IoT) devices presents substantial challenges to network security. Hackers are afforded a larger attack surface to exploit as more devices become interconnected. Furthermore, the sheer volume of data these devices generate can overwhelm conventional security systems, compromising their detection capabilities. To address these challenges posed by the increasing number of interconnected IoT devices and the data overload they generate, this paper presents an approach based on meta-learning principles to identify attacks within IoT networks. The proposed approach constructs a meta-learner model by stacking the predictions of three Deep-Learning (DL) models: RNN, LSTM, and CNN. Subsequently, the identification by the meta-learner relies on various methods, namely Logistic Regression (LR), Multilayer Perceptron (MLP), Support Vector Machine (SVM), and Extreme Gradient Boosting (XGBoost). To assess the effectiveness of this approach, extensive evaluations are conducted using the IoT dataset from 2020. The XGBoost model showcased outstanding performance, achieving the highest accuracy (98.75%), precision (98.30%), F1-measure (98.53%), and AUC-ROC (98.75%). On the other hand, the SVM model exhibited the highest recall (98.90%), representing a slight improvement of 0.14% over the performance achieved by XGBoost.
... The Internet of Things (IoT) has emerged as a transformative innovation, enabling various everyday products and gadgets to connect to the web and exchange data. This technology can potentially revolutionize several industries, fostering increased efficiency, improved decision-making, and the development of novel services [1]. However, along with its advantages, the rapid expansion of IoT has led to growing security concerns. ...
The Internet of Things (IoT) has transformed our interaction with technology and introduced security challenges. The growing number of IoT attacks poses a significant threat to organizations and individuals. This paper proposes an approach for detecting attacks on IoT networks using ensemble feature selection and deep learning models. Ensemble feature selection combines filter techniques such as variance threshold, mutual information, Chi-square, ANOVA, and L1-based methods. By leveraging the strengths of each technique, the ensemble is formed by the union of selected features. However, this union operation may overlook redundancy and irrelevance, potentially leading to a larger feature set. To address this, a wrapper algorithm called Recursive Feature Elimination (RFE) is applied to refine the feature selection. The impact of the selected feature set on the performance of Deep Learning (DL) models (CNN, RNN, GRU, and LSTM) is evaluated using the IoT-Botnet 2020 dataset, considering detection accuracy, precision, recall, F1-measure, and False Positive Rate (FPR). All DL models achieved the highest detection accuracy, precision, recall, and F1 measure values, ranging from 97.05% to 97.87%, 96.99% to 97.95%, 99.80% to 99.95%, and 98.45% to 98.87%, respectively.
