The integrity of Internet of Things (IoT) devices has caused a fast spread in an era of data-driven decision-making across businesses. This tutorial survey provides a comprehensive review of current IoT data handling advances, focusing on data quality management (DQM). The article starts with the key aspects of IoT data management. In this regard, we shed light on the data source, volume and velocity, variety, lifecycle, security and privacy, scalability and distribution processing, anomaly detection, and energy efficiency. Then, We present a comprehensive taxonomy of IoT DQM based on the application type, such as smart cities, healthcare, agriculture, environmental monitoring, retail and supply chain, and smart grids (SGs). As IoT data processing, analysis, and security play a significant role in DQM; this tutorial survey carefully addresses how modern technologies maintain this role. More particularly, this work investigates the use of edge computing for real-time data processing and the incorporation of synthetic data to supplement restricted resources incorporating the issues of managing the massive datasets created by IoT implementations. In addition, the paper addresses the use of machine learning (ML) algorithms for in-depth analysis of IoT data streams, DQ evaluation protocols, and detection tactics during data transfer. Moreover, the article investigates complete security measures for protecting sensitive data, such as access control regulations and several security techniques, including authentication, encryption, and secure communication protocols that enable IoT data management. Besides, blockchain technology’s significant roles in this regard have been comprehensively addressed. Along with summarizing and reviewing the latest efforts in DQM in IoT-based systems, we shed light on their strong and weak points and discuss upcoming trends and potential difficulties in IoT data management. Last but not least, we continue by emphasizing the cumulative impact of these advances and shedding light on the open issues and challenges. Finally, this in-depth tutorial survey aims to be a significant resource for academics, practitioners, and stakeholders interested in the changing environment of IoT data management, with a particular emphasis on DQ.

Earthquake early warning systems (EEWSs) often rely on fast determination of earthquake source parameters, namely, location, magnitude, and depth. In areas where the seismic network is coarse, the capability to determine source parameters based on data recorded by a single station is desirable. Moreover, being able to use a single component of the seismic data might increase the robustness of the system to sensor malfunction and might save on sensor cost and computation time. Here, we propose a hybrid deep learning (DL) model to estimate source parameters based on single-component data recorded by a single station at 3 s after the P-wave onset. The model, which we call EEWS-311, uses a convolutional neural network (CNN) and bidirectional long short-term memory. It is trained and tested on recordings of more than 14000 events by a single station of the Japanese Hi-net high-sensitivity short-period seismic network. Compared with source parameters obtained by conventional methods, our model achieves excellent performance (average errors in latitude, longitude, magnitude, and depth equal to 0.05°, 0.1°, 0.14 velocity magnitude (Mv), and 5.68 km, respectively). The results demonstrate the suitability of EEWS-311 for earthquake early warning in areas with sufficient training data.

The effects of explosions and other manmade seismic sources pose a threat to humanity. One of the most pressing issues currently confronting seismologists is contamination of seismicity catalogs. In order to distinguish tectonic from non-tectonic occurrences, an automated control system must be developed, and since detecting quarry blasts (QBs) is the initial and always tough stage, this is an absolute necessity. The need to locate and eliminate the man-made seismic disturbances has increased dramatically. In order to aid in precise seismic hazard identification and improve the planning of future urban developments, early treatments and cleaning of contaminated seismicity catalogs are necessary. Machine learning (ML) methods have allowed for greater precision in identifying synthetic seismic sources. Distinguishing between QBs and natural earthquakes is currently the focus of numerous methodologies, ML techniques, and varied processes, such as knowledge discovery. In order for intelligent systems to learn from repeated encounters and spot and identify patterns in a dataset, ML techniques provide a variety of probabilistic and statistical methods. The purpose of this research is to develop an algorithm that can identify QBs inside seismicity databases automatically. To be more specific, we use classical and ensemble ML classifiers to categorize reports of seismic activity. In order to improve performance, the suggested technique makes use only three features (Latitude, Longitude, and Magnitude). The accuracy of the proposed scheme is examined by R2, F1-score, MCC score, kappa score, elapsed time, learning curve, and confusion matrix. The proposed mode has demonstrated the superior performance as compared to the benchmarks with a testing accuracy of 97.21 %.

This article delves at the potential of artificial intelligence (AI) and drone-based technologies for disaster relief. Potential applications of these technologies in disaster response are discussed; they include the use of drones to survey the scene and look for survivors, and the use of AI-based systems to offer real-time data to rescue workers. We discuss the future directions and research directions for AI and drone-based disaster management, including the integration of AI and drone-based technologies, the development of multi-agent systems, and the importance of explainable AI and ethical considerations. Finally, we end by stressing the need to advance AI and drone-based technologies for use in disaster management and their potential to lessen the global effect of natural disasters.

The need for an earthquake early-warning system (EEWS) is unavoidable in order to save lives. In terms of managing earthquake disasters and achieving effective risk mitigation, the quick identification of the earthquake’s intensity is a valuable factor. In light of this, the on-site intensity measurement can be transmitted over an Internet of Things (IoT) network. In this regard, a machine learning (ML) strategy based on numerous linear and non-linear models is proposed in this study for a quick determination of earthquake intensity after two seconds from the P-wave onset. We call this model an on-site two-second ML model-based earthquake intensity determination (2S-ML-EIOS). The utilized dataset INSTANCE for this model is observed by the number of 386 stations from the Italian national seismic network. Our model has been trained on 50,000 occurrences (150 thousand of 2s-three-component seismic windows). The model has the ability to deal with limited features of the waveform traces leading to reliable estimation of the earthquake intensity. The suggested model has a 98.59% accuracy rate in predicting earthquake intensity. The suggested 2S-ML-EIOS model can be used with a centralized IoT system to promptly send the alarm, and the IoT system will then instruct the affected administration to take the appropriate action. The 2S-ML-EIOS results are contrasted with those from the traditional manual solution approach, which corresponds to the ideal solution mean. Based on the extreme gradient boosting (XGB) model, the 2S-ML-EIOS can achieve the best intensity determination, and this improved performance demonstrates the methodology’s efficacy for EEWS.

Earthquake early-warning system (EEWS) is inevitable for saving human lives. The fast determination of the Earthquake’s (EQ’s) magnitude and its location is significant in disaster management and EQ risk mitigation. These parameters can be conveyed over the Internet-of-Things (IoT) network to alleviate an EQ disaster. In this article, a deep learning model based on integrating autoencoder (AE) and convolutional neural network (CNN) for a swift pinpointing of EQ magnitude and location after 3 s from the onset of the P-wave is proposed. Thus, we name it 3 s AE and CNN (3S-AE-CNN). The employed data set is observed by three stations from the Japanese Hi-net seismic network. We have trained our model on 12200 events (109.80 thousand 3-s-three-component seismic windows). The model facilitates the extraction of waveforms’ significant features leading to robust estimation of the EQ parameters. The proposed model predicts the magnitude and location of EQ with errors in magnitude, latitude, and longitude that reach 0.000028, 0.0000033, and 0.0001, respectively. The EQ’s parameters calculated by the proposed 3S-AE-CNN model are swiftly sent to a centralized IoT system that in turn directs the involved entity to take suitable action. The obtained results of the 3S-AE-CNN are compared to the conventional manual solution method, which represents the optimum solution mean. The 3S-AE-CNN shows an enhanced performance for the magnitude and location determination as compared with the benchmark method, which proves its effectiveness for EEWS.

Efficient handling and planning for the urban regions’ sustainable development require a vast range of up-to-date and thematic information. Besides, obtaining an uncontaminated catalog of seismic activity is desirable to study the earthquake (EQ) clusters’ spatial allocation, which is a key role in mitigating seismic hazards and alleviating EQ losses by enhancing the assessment of seismic hazards. This article considers the northeastern part of Egypt where the seismicity catalog is contaminated by quarry blasts (QBs) operated throughout the mapped area. Consequently, it is desirable to discriminate these QBs from the EQs for genuine seismicity and hazard analysis. Accordingly, we provide an efficient machine learning (ML) model for decontaminating the seismicity database so that EQ clusters can be properly delineated by relying on 870 events (EQs and QBs) observed by only one seismic station called “GLL,” a member of the Egyptian National Seismic Network (ENSN). The model focuses on magnitudes < 3 that have high uncertainty of being EQs or QBs and take a long time for analysis. The approach examines several linear and nonlinear ML models and, finally, selects the best model with only two features leading to the optimal classification between the EQs and QBs. The optimization process is accomplished throughout two stages. The obtained results prove that the proposed scheme achieves 100% discrimination between the EQs and QBs relying on the extreme gradient boosting (XGB) model.

Wireless sensor networks (WSNs)-based Internet of Things (IoT) are among the fast booming technologies that drastically contribute to different systems' management and resilience data accessibility. Designing a robust IoT network imposes some challenges, such as data trustworthiness (DT) and power management. This article presents a repeated game model to enhance clustered WSNs-based IoT security and DT against the selective forwarding (SF) attack. Besides, the model is capable of detecting the hardware (HW) failure of the cluster members (CMs), preserving the network stability, and conserving the power consumption due to packet retransmission. The model relies on the TDMA protocol to facilitate the detection process and to avoid collision between the delivered packets at the cluster head (CH). The proposed model aims to keep packets transmitting, isotropic or nonisotropic transmission, from the CMs to the CH for maximizing the DT and aims to distinguish between the malicious CM and the one suffering from the HW failure. Accordingly, it can manage the consequently lost power due to the malicious attack effect or HW malfunction. The simulation results indicate the proposed mechanism improved performance with TDMA over six different environments against the SF attack that achieves the Pareto-optimal DT as compared to a noncooperative defense mechanism.

In the era of internet‐of‐things (IoT), many applications utilise wireless sensor networks (WSNs)including smart grids (SGs). Designing WSNs to fulfill the SGs requirementsimposes some challenges such as limited power and signal propagationimpairments, especially, in harsh environments. Consequently, saving powerconsumption in WSNs‐based SGs is among the most significant challenges. Thetotal power required at a certain sensor depends on two main parameters: thepacket length and inter‐node distance. This paper investigates the optimalpacket length and inter‐node distance to be utilised in a SG over six differentenvironments aiming at maximising the network lifetime. The investigation isbased on a link‐layer model using Tmote Sky nodes taking into consideration thesix environments impact. A mixed‐integer programming (MIP) model is utilised todetermine the best packet length and number of nodes for maximising the networklifetime. This model analyses the performance of maximum SG network lifetimeover those environments and addresses the inter‐node distance effect on thenetwork lifetime maximisation. Simulation results show that decreasing thenumber of nodes covering a certain area is preferable to prolonging the networklifetime. Furthermore, for the considered models, the longer the packet lengthis, the longer the network lifetime will be.

Due to the fast boom of security threats in wireless sensor networks (WSNs) sensitive applications, we propose a game-theoretic protection approach for sensor nodes in a clustered WSN based on a repeated game. The proposed game model is developed for detecting malicious sensor nodes that drop the high priority packets (HPPs) aiming at maximizing the high priority data trustworthiness (HPT). Simulation results indicate the improved HPT of the proposed protection model which attains the Pareto optimal HPT as compared to a non-cooperative defense mechanism.

Wireless Sensor Networks (WSNs) security plays an intrinsic role to guarantee efficient data transmission, stable network topologies, and robust routing algorithms. In this paper, we propose a modified Stackelberg game of a previous work for high assurance of data trustworthiness in a Power Grid Network (PGN). The proposed approach is presented to mitigate a more severe attack scenario compared to that considered in the previous work; this attack scenario frequently manipulates sets of the deployed nodes in the PGN, which cannot be treated using the previously proposed approach. Our proposed scheme reduces the required number of nodes to be protected to achieve the desired data trustworthiness. Simulation results prove efficient detection for corrupted transmitted data based on limited number of nodes as compared to the previously proposed approach.

The authors propose a game‐theoretic approach using the Stackelberg game for securing cognitive radio sensor network (CRSN) against the spectrum sensing data falsification attack; this attack aims at corrupting the spectrum decisions communicated from the ambient sensor nodes (ASNs) to the fusion centre by imposing interference power. The proposed game approach is developed for two different attack–defence scenarios. In the first scenario, the attacker selects to attack a group of delivered reports of the ASNs that have a protection degree below a specific threshold. In the second scenario, the attacker applies its maximum attack interference power to the delivered reports of the ASNs that have been reported to be least protected in the previous round. Simulation results indicate the improved performance of the proposed protection model as compared with two baseline defence mechanisms, namely, the random and equal‐protection defence mechanisms with static signal‐to‐noise ratio (SNR) and variable SNRs. Consequently, Stackelberg game features prove to be beneficial for securing communication over CRSN.

In this paper, we propose a game-theoretic protection model for Wireless Sensor Network (WSN) nodes within a cluster based on a Stackelberg game. The proposed game approach is developed for two different attack-defense scenarios. In the first scenario, the attacker selects to attack a group of nodes that have a protection degree below a specific threshold. In the second scenario, the attacker targets the nodes that have been reported to be least protected in the previous round. Simulation results indicate the improved performance of the proposed protection model as compared to the no-defense case.