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Abstract The sensor‐enabled in‐vehicle communication and infrastructure‐centric vehicle‐to‐everything (V2X) communications have significantly contributed to the spark in the amount of data exchange in the connected and autonomous vehicles (CAV) environment. The growing vehicular communications pose a potential cyber security risk considering online vehicle hijacking. Therefore, there is a critical need to prioritize the cyber security issues in the CAV research theme. In this context, this paper presents a cyber security analysis of connected vehicle traffic environments (CyACV). Specifically, potential cyber security attacks in CAV are critically investigated and validated via experimental data sets. Trust in V2X communication for connected vehicles is explored in detail focusing on trust computation and trust management approaches and related challenges. A wide range of trust‐based cyber security solutions for CAV have been critically investigated considering their strengths and weaknesses. Open research directions have been highlighted as potential new research themes in CAV cyber security area.

The growing development of the Internet of Things (IoT) is accelerating the emergence and growth of new IoT services and applications, which will result in massive amounts of data being generated, transmitted and processed in wireless communication networks. Mobile Edge Computing (MEC) is a desired paradigm to timely process the data from IoT for value maximization. In MEC, a number of computing-capable devices are deployed at the network edge near data sources to support edge computing, such that the long network transmission delay in cloud computing paradigm could be avoided. Since an edge device might not always have sufficient resources to process the massive amount of data, computation offloading is significantly important considering the cooperation among edge devices. However, the dynamic traffic characteristics and heterogeneous computing capabilities of edge devices challenge the offloading. In addition, different scheduling schemes might provide different computation delays to the offloaded tasks. Thus, offloading in mobile nodes and scheduling in the MEC server are coupled to determine service delay. This paper seeks to guarantee low delay for computation intensive applications by jointly optimizing the offloading and scheduling in such an MEC system. We propose a Delay-Greedy Computation Offloading (DGCO) algorithm to make offloading decisions for new tasks in distributed computing-enabled mobile devices. A Reinforcement Learning-based Parallel Scheduling (RLPS) algorithm is further designed to schedule offloaded tasks in the multi-core MEC server. With an offloading delay broadcast mechanism, the DGCO and RLPS cooperate to achieve the goal of delay-guarantee-ratio maximization. Finally, the simulation results show that our proposal can bound the end-to-end delay of various tasks. Even under slightly heavy task load, the delay-guarantee-ratio given by DGCO-RLPS can still approximate 95%, while that given by benchmarked algorithms is reduced to intolerable value. The simulation results are demonstrated the effectiveness of DGCO-RLPS for delay guarantee in MEC.

This paper proposes a system for measuring unknown resistances for metal oxide MQ gas sensors. The circuit configuration is based on the Anderson current loop interface, which connects directly to an Arduino Mega 2560. We analyze errors arising from variations in supply voltage of conventional divider circuits, including those introduced by the Analog-to-Digital Conversion (ADC) of microcontroller. To enhance the accuracy of resistance measurements, a voting technique for selecting the optimal unknown resistances is introduced. In this technique, the digital voltage at each node is analyzed to determine the frequency of occurrence of each level. If a particular voltage level has a frequency of occurrence greater than the reference threshold k, it is selected. If no voltage level meets this criterion, the average of the observed voltage levels is used. From the experimental results, unknown resistances were measured in the range of 362 – 15, $397~\Omega $ with a maximum approximated error of 0.55% with $k =90$ %, while the gas content was measured with a maximum error of approximately 0.24% under conditions of power supply voltage fluctuation from 4.9 to 5.1 V.

Green buildings are designed and constructed according to the principles of sustainable development and are an inevitable trend in future architectural development. Nowadays, many works have studied the application of intelligence or intelligent technology in green intelligent buildings, but there is still insufficient discussion on how to integrate intelligent technology into all aspects of buildings. In view of this, this paper summarizes the design concepts of modern green buildings and takes this as the starting point to explore the classification and construction of the core needs for achieving sustainable development throughout the life cycle of buildings from five aspects: building design, building materials, building construction, building renewal and management, and building damage, and analyze the integration of relevant intelligent technologies in buildings under different needs.

Network management is a crucial task to maintain modern systems and applications running. Some applications have become vital for society and are expected to have zero downtime. Software-defined networks is a paradigm that collaborates with the scalability, modularity and manageability of systems by centralizing the network’s controller. However, this creates a weak point for distributed denial of service attacks if unprepared. This study proposes an anomaly detection system to detect distributed denial of service attacks in software-defined networks using generative adversarial neural networks with gated recurrent units. The proposed system uses unsupervised learning to detect unknown attacks in an interval of 1 second. A mitigation algorithm is also proposed to stop distributed denial-of-service attacks from harming the network’s operation. Two datasets were used to validate this model: the first developed by the computer networks study group Orion from the State University of Londrina. The second is a well-known dataset: CIC-DDoS2019, widely used by the anomaly detection community. Besides the gated recurrent units, other types of neurons are also tested in this work, they are: long short-term memory, convolutional and temporal convolutional. The detection module reached an F1-score of 99@ in the first dataset and 98@ in the second, while the mitigation module could drop 99@ of malicious flows in both datasets.

Smart technologies are advancing the development of cutting-edge systems by exploring the future network. The Internet of Things (IoT) and many multimedia sensors interact with each other for collecting and transmitting visual data. However, managing enormous amounts of data from numerous network devices is one of the main research challenges. In this context, various IoT systems have been investigated and have provided efficient data retrieval and processing solutions. For multimedia systems, however, controlling inefficient bandwidth utilization and ensuring timely transmission of vital information are key research concerns. Moreover, to transfer multimedia traffic while balancing communication costs for the IoT system, a sustainable solution with intelligence in real-life applications is demanded. Furthermore, trust must be formed for technological advancement to occur; such an approach provides the smart communication paradigm with the incorporation of edge computing. This study proposed a model for optimizing multimedia using a combination of edge computing intelligence and authentic strategies. Mobile edges analyze network states to discover the system's status and minimize communication disruptions. Moreover, direct and indirect authentication determines the reliability of data forwarders and network stability. The proposed authentication approach minimizes the possibility of data compromise and increases trust in multimedia surveillance systems. Using simulation testing, the proposed model outperformed other comparable work in terms of byte delivery, packet overhead, packet delay, and data loss metrics.

Abstract For linear time varying (LTV) multiple input multiple output (MIMO) systems with vector relative degree, an open‐closed‐loop iterative learning control (ILC) strategy is developed in this article, where the time interval of operation is iteration dependent. To compensate the missing tracking signal caused by iteration dependent interval, the feedback control is introduced in ILC design. As the tracking signal of many continuous iterations is lost in a certain interval, the feedback control part can employ the tracking signal of current iteration for compensation. Under the assumption that the initial state vibrates around the desired initial state uniformly in mathematical expectation sense, the expectation of ILC tracking error can converge to zero as the number of iteration tends to infinity. Under the circumstance that the initial state varies around the desired initial state with a bound, as the number of iteration tends to infinity, the expectation of ILC tracking error can be driven to a bounded range, whose upper bound is proportional to the fluctuation. It is revealed that the convergence condition is dependent on the feedforward control gains, while the feedback control can accelerate convergence speed by selecting appropriate feedback control gains. As a special case, the controlled system with integrated high relative degree is also addressed by proposing a simplified iteration dependent interval based open‐closed‐loop ILC method. Finally, the effectiveness of the developed iteration dependent interval based open‐closed‐loop ILC is illustrated by a simulation example with two cases on initial state.

Projections indicate aquaculture will produce 106 million tonnes of fish by 2030, emphasizing the need for efficient and sustainable practices. New technologies can provide a valuable tool for adequate fish farm management. The aim of this paper is to explore the factors affecting fish well-being, the design of control systems for aquaculture, and the proposal of a smart system based on algorithms to improve efficiency and sustainability. First, we identify the domains affecting fish well-being: the production domain, abiotic domain, biotic domain, and control systems domain. Then, we evaluate the interactions between elements present in each domain to evaluate the key aspects to be monitored. This is conducted for two types of fish farming facilities: cages in the sea and recirculating aquaculture systems. A total of 86 factors have been identified, of which 17 and 32 were selected to be included in monitoring systems for sea cages and recirculating aquaculture systems. Then, a series of algorithms are proposed to optimize fish farming management. We have included predefined control algorithms, energy-efficient algorithms, fault tolerance algorithms, data management algorithms, and a smart control algorithm. The smart control algorithms have been proposed considering all the aforementioned factors, and two scenarios are simulated to evaluate the benefits of the smart control algorithm. In the simulated case, the turbidity when the control algorithm is used represents 12.5% of the turbidity when not used. Their use resulted in a 35% reduction in the energy consumption of the aerator system when the smart control was implemented.

Sixth-generation (6G) wireless networks demand a more efficient implementation of non-orthogonal multiple access (NOMA) schemes for severe multipath fading environments to serve multiple users. Using non-orthogonal multiple access (NOMA) schemes in IoT 6G networks is a promising solution to allow multiple users to share the same spectral and temporal resource, increasing spectral efficiency and improving the network’s capacity. In this work, we have evaluated the performance of a novel progressive pattern interleaver (PPI) employed to distinguish the users in interleaved division multiple access (IDMA) schemes, suggested by 3GPP guidelines as a NOMA scheme, with two multi-carrier modulation schemes known as single-carrier frequency-division multiple access (SC-FDMA) and orthogonal frequency-division multiplexing (OFDM), resulting in SC-FDMA-IDMA and OFDM-IDMA schemes. Both schemes are multi-carrier schemes with orthogonal sub-carriers to deal against inter-symbol interference (ISI) and orthogonal interleavers for the simultaneous access of multiple users. It has been suggested through simulation outcomes that PPI performance is adequate with SC-FDMA-IDMA and OFDM-IDMA schemes in terms of bit error rate (BER) under multipath channel conditions. Moreover, regarding bandwidth requirement and the implementation complexity of the transmitted interleaver structure, PPI is superior to the conventional random interleaver (RI).

Recently, Panama wilt disease that attacks banana leaves has caused enormous economic losses to farmers. Early detection of this disease and necessary preventive measures can avoid economic damage. This paper proposes an improved method to predict Panama wilt disease based on symptoms using an agro deep learning algorithm. The proposed deep learning model for detecting Panama wilts disease is essential because it can help accurately identify infected plants in a timely manner. It can be instrumental in large-scale agricultural operations where Panama wilts disease could spread quickly and cause significant crop loss. Additionally, deep learning models can be used to monitor the effectiveness of treatments and help farmers make informed decisions about how to manage the disease best. This method is designed to predict the severity of the disease and its consequences based on the arrangement of color and shape changes in banana leaves. The present proposed method is compared with its previous methods, and it achieved 91.56% accuracy, 91.61% precision, 88.56% recall and 81.56% F1-score.

Turbidity is one of the crucial parameters of water quality. Even though many commercial devices, low-cost sensors, and remote sensing data can efficiently quantify turbidity, they are not valid tools for the classification it. In this paper, we design, calibrate, and test a novel optical low-cost sensor for turbidity quantification and classification. The sensor is based on an RGB light source and a light detector. The analyzed samples are characterized by turbidity values from 0.02 to 60 NTUs, and have four different sources. These samples were generated to represent natural turbidity sources and leaves in the marine areas close to agricultural lands. The data are gathered using 64 different combinations of light, generating complex matrix data. Machine learning models are compared to analyze this data, including training, validation, and test datasets. Moreover, different alternatives for data preprocessing and feature selection are assessed. Concerning the quantification of turbidity, the best results were obtained using averaged data and principal components analyses in conjunction with exponential gaussian process regression, achieving an R2 of 0.979. Regarding the classification of the turbidity, an accuracy of 91.23% is obtained with the fine K-Nearest-Neighbor classifier. The cases in which data were misclassified are characterized by turbidity values lower than 5 NTUs. The obtained results represent an improvement over the current solutions in terms of turbidity quantification and a completely novel approach to turbidity classification.

Studying soil composition is vital for agricultural and edaphology disciplines. Presently, colorimetry serves as a prevalent method for the on-site visual examination of soil characteristics. However, this technique necessitates the laboratory-based analysis of extracted soil fragments by skilled personnel, leading to substantial time and resource consumption. Contrastingly, sensor techniques effectively gather environmental data, though they mostly lack in situ studies. Despite this, sensors offer substantial on-site data generation potential in a non-invasive manner and can be included in wireless sensor networks. Therefore, the aim of the paper is to develop a low-cost red, green, and blue (RGB)-based sensor system capable of detecting changes in the composition of the soil. The proposed sensor system was found to be effective when the sample materials, including salt, sand, and nitro phosphate, were determined under eight different RGB lights. Statistical analyses showed that each material could be classified with significant differences based on specific light variations. The results from a discriminant analysis documented the 100% prediction accuracy of the system. In order to use the minimum number of colors, all the possible color combinations were evaluated. Consequently, a combination of six colors for salt and nitro phosphate successfully classified the materials, whereas all the eight colors were found to be effective for classifying sand samples. The proposed low-cost RGB sensor system provides an economically viable and easily accessible solution for soil classification.

One of the key indicators of water quality is dissolved oxygen. Even though oxygen is important in environmental monitoring, the sensors for dissolved oxygen are expensive and require periodic maintenance due to the use of membranes. In this paper, we propose using ultraviolet light absorption to estimate dissolved oxygen saturation in water samples. The absorption spectrum of dissolved oxygen in the ultraviolet range is investigated over a water matrix with different levels of complexity. First, the difference between different water matrixes is studied. The results indicate similar variations between river water and tap water matrices for comparative purposes. Both samples present much higher absorbance signals than distilled water. Thus, the rest of the tests were performed with only three water matrixes (ultrapure, distilled, and river water). By aerating, water samples were completely saturated. Then, nitrogen gas was used to remove dissolved oxygen from samples to obtain saturations of 75, 50, 25, and 3%. The absorption was measured from 190 to 380 nm, using LLG-uniSPEC 2. The obtained data were used to generate regression models for selected wavelengths (190, 210, 240, and 250 nm). The differences beyond 260 nm for the studied dissolved oxygen saturations were null. The generated models had correlation coefficients from 0.99 to 0.97 for ultrapure water, 0.98 to 0.95 for distilled water, and 0.90 to 0.83 for river water. The maximum differences were found between samples with 75 and 100% of saturation.

Abstract Storm sewerages are crucial infrastructures in water management. In this paper, a system was developed to detect the blockage of the sewerage and the presence of illegal spills in Storm sewerages. Different nodes with sensors measuring the water level, turbidity, conductivity, and oil presence are scattered in the sewerage. These nodes are connected to a master node for processing the information with a rain sensor. The rain and water level sensors are used to determine one of the four possibilities regarding the presence of water in the sewerage and whether it is raining. According to the combinations, it can be determined whether it is a normal situation or there are spills or blockages. It is shown that there are differences between sewerage with and without blockage via the water level. This can be used to determine the presence of a blockage. The identification of an illegal spill is performed with the use of conductivity, turbidity, and oil sensors. The authors determined that yellow and infrared light can determine the oil concentration in the oil sensor in a range of 0–2.2 mL oil/L water with yellow light and 0.4–20 mL oil/L water with infrared light. Finally, the conductivity sensor can determine water conductivity from 0.526 to 58.4 mS/cm.

Vehicle malfunctions have a direct impact on both human and road safety, making vehicle network security an important and critical challenge. Vehicular ad hoc networks (VANETs) have grown to be indispensable in recent years for enabling intelligent transport systems, guaranteeing traffic safety, and averting collisions. However, because of numerous types of assaults, such as Distributed Denial of Service (DDoS) and Denial of Service (DoS), VANETs have significant difficulties. A powerful Network Intrusion Detection System (NIDS) powered by Artificial Intelligence (AI) is required to overcome these security issues. This research presents an innovative method for creating an AI-based NIDS that uses Deep Learning methods. The suggested model specifically incorporates the Self Attention-Based Bidirectional Long Short-Term Memory (SA-BiLSTM) for classification and the Cascaded Convolution Neural Network (CCNN) for learning high-level features. The Multi-variant Gradient-Based Optimization algorithm (MV-GBO) is applied to improve CCNN and SA-BiLSTM further to enhance the model’s performance. Additionally, information gained using MV-GBO-based feature extraction is employed to enhance feature learning. The effectiveness of the proposed model is evaluated on reliable datasets such as KDD-CUP99, ToN-IoT, and VeReMi, which are utilized on the MATLAB platform. The proposed model achieved 99% accuracy on all the datasets.

Nowadays, it is common for applications to require servers to run constantly and aim as close as possible to zero downtime. The slightest failure might cause significant financial losses and sometimes even lives. For this reason, security and management measures against network threats are fundamental and have been researched for years. Software-defined networks (SDN) are an advancement in network management due to their centralization of the control plane, as it facilitates equipment setup and administration over the local network. However, this centralization makes the controller a target to denial of service attacks (DoS). In this study, we aim to develop a network anomaly detection and mitigation system that uses gated recurrent unit (GRU) neural networks combined with fuzzy logic. The neural network is trained to forecast future traffic, and anomalies are detected when the forecasting fails. The system is designed to operate in software-defined networks since they provide network flow information and tools to manage forwarding tables. We also demonstrate how the neural network’s hyperparameters affect the detection module. The system was tested using two datasets: one with emulated traffic generated by the data communication and networking research group called Orion, from computer science department at state university of Londrina, and CICDDoS2019, a well-known dataset by the anomaly detection community. The results show that GRU networks combined with fuzzy logic are a viable option to detect anomalies in SDN and possibly in other anomaly detection applications. The system was compared with other deep learning techniques.

The implementation of Smart City projects has experimented a surge in the recent years with examples such as Smart Santander or Barcelona Smart City. Among the different domains that comprise the Smart City, water management has a great importance, more so in areas with water scarcity. Furthermore, water from different sources such as treated sewage water or collected rainwater can be utilized to address water needs where the use of potable water is not necessary. Therefore, the implementation of smart systems for the irrigation of urban gardens and other urban vegetated areas is of great importance to manage both water needs and the available resources. In this paper, a communication protocol for smart irrigation systems designed within the context of the Smart City is presented. The protocol enables the communication among devices with both LoRa and WiFi wireless technologies. Tests were performed with low-cost devices in an urban area. The results demonstrate the good performance of the proposal, obtaining the minimum packet loss by adding a 500 ms delay at the CH node when transmitting messages from WiFi to LoRa and vice versa.

The indoor object tracking by utilizing received signal strength indicator (RSSI) measurements with the help of wireless sensor network (WSN) is an interesting and important topic in the domain of location-based applications. Without the knowledge of location, the measurements obtained with WSN are of no use. The trilateration is a widely used technique to get location updates of target based on RSSI measurements from WSN. However, it suffers with high location estimation errors arising due to random variations in RSSI measurements. This paper presents a range-free radial basis function neural network (RBFN) and Kalman filtering- (KF-) based algorithm named RBFN+KF. The performance of the RBFN+KF algorithm is evaluated using simulated RSSIs and is compared against trilateration, multilayer perceptron (MLP), and RBFN-based estimations. The simulation results reveal that the proposed RBFN+KF algorithm shows very low location estimation errors compared to the rest of the three approaches. Additionally, it is also seen that RBFN-based approach is more energy efficient than trilateration and MLP-based localization approaches.

The increasing availability of Electric Vehicles (EVs) is driving a shift away from traditional gasoline-powered vehicles. Subsequently, the demand for Electric Vehicle Charging Systems (EVCS) is rising, leading to the significant growth of EVCS as public and private charging infrastructure. The cybersecurity-related risks in EVCS have significantly increased due to the growing network of EVCS. In this context, this paper presents a cybersecurity risk analysis of the network of EVCS. Firstly, the recent advancements in the EVCS network, recent EV adaptation trends, and charging use cases are described as a background of the research area. Secondly, cybersecurity aspects in EVCS have been presented considering infrastructure and protocol-centric vulnerabilities with possible cyber-attack scenarios. Thirdly, threats in EVCS have been validated with real-time data-centric analysis of EV charging sessions. The paper also highlights potential open research issues in EV cyber research as new knowledge for domain researchers and practitioners.

Essential oils are valuable in various industries, but their easy adulteration can cause adverse health effects. Electronic nasal sensors offer a solution for adulteration detection. This article proposes a new system for characterising essential oils based on low-cost sensor networks and machine learning techniques. The sensors used belong to the MQ family (MQ-2, MQ-3, MQ-4, MQ-5, MQ-6, MQ-7, and MQ-8). Six essential oils were used, including Cistus ladanifer, Pinus pinaster, and Cistus ladanifer oil adulterated with Pinus pinaster, Melaleuca alternifolia, tea tree, and red fruits. A total of up to 7100 measurements were included, with more than 118 h of measurements of 33 different parameters. These data were used to train and compare five machine learning algorithms: discriminant analysis, support vector machine, k-nearest neighbours, neural network, and naive Bayesian when the data were used individually or when hourly mean values were included. To evaluate the performance of the included machine learning algorithms, accuracy, precision, recall, and F1-score were considered. The study found that using k-nearest neighbours, accuracy, recall, F1-score, and precision values were 1, 0.99, 0.99, and 1, respectively. The accuracy reached 100% with k-nearest neighbours using only 2 parameters for averaged data or 15 parameters for individual data.

Acquiring the morphological parameters of fish with the traditional method (depending on human and non-automatic factors) not only causes serious problems, such as disease transmission, mortality due to stress, and carelessness and error, but it is also time-consuming and has low efficiency. In this paper, the speed of fish and their physical characteristics (maximum and minimum diameter, equivalent diameter, center of surface, and velocity of fish) were investigated by using a programmed online video-recording system. At first, using the spatial coordinates obtained from YOLOv2, the speed of the fish was calculated, and the morphological characteristics of the fish were also recorded using this program during two stages of feeding and normal conditions (when the fish are not in feeding condition). Statistical analysis was performed between the measured parameters due to the high correlation between the parameters, and the classification system with high accuracy was able to provide an accurate prediction of the fish in both normal and feeding conditions. In the next step, an artificial neural network (ANN) prediction model (with three neurons; four input, one hidden layer, and one output) was presented to plan the system online. The model has the lowest error (1.4 and 0.14, respectively) and the highest coefficient of explanation (0.95 and 0.94, respectively) in two modes, normal and feeding, which are presented by the ANN system for planning the online system. The high accuracy and low error of the system, in addition to having a high efficiency for continuous and online monitoring of live fish, can have a high economic benefit for fish breeders due to the simplicity of its equipment, and it can also check and diagnose the condition of fish in time and prevent economic damage.

Eutrophication is the excessive growth of algae in water bodies that causes biodiversity loss, reducing water quality and attractiveness to people. This is an important problem in water bodies. In this paper, we propose a low-cost sensor to monitor eutrophication in concentrations between 0 to 200 mg/L and in different mixtures of sediment and algae (0, 20, 40, 60, 80, and 100% algae, the rest are sediment). We use two light sources (infrared and RGB LED) and two photoreceptors at 90° and 180° of the light sources. The system has a microcontroller (M5stacks) that powers the light sources and obtains the signal received by the photoreceptors. In addition, the microcontroller is responsible for sending information and generating alerts. Our results show that the use of infrared light at 90° can determine the turbidity with an error of 7.45% in NTU readings higher than 2.73 NTUs, and the use of infrared light at 180° can measure the solid concentration with an error of 11.40%. According to the determination of the % of algae, the use of a neural network has a precision of 89.3% in the classification, and the determination of the mg/L of algae in water has an error of 17.95%.

In coastal water monitoring, abrupt pH changes might indicate different pollution sources. Existing sensors for pH monitoring in coastal waters at low cost are mainly based on a glass membrane and a reference electrode. Virtual sensors are elements capable of measuring certain parameters based on data from other parameters or variables. The aim of this paper is to propose the use of a virtual pH sensor based on measuring different physical effects of H+ on the electromagnetic field generated by an inductor. Double inductors based on two solenoids of 40 and 80 spires are used as sensing elements. Samples with pH from 4 to 11 are used, and the effect of temperature is evaluated using samples from 10 to 40 °C. The induced voltage and the delay of the signal are measured for powering frequencies from 100 to 500 kHz. These data of delay, induced voltage, frequency, and temperature are included in a probabilistic neural network to classify these data according to the pH. The results indicate low accuracy for samples with a pH of 11. A second analysis, excluding these data, offered correctly classified cases of 88.9%. The system can achieve considerable high accuracy (87.5%) using data gathered at a single frequency, from 246 to 248 kHz. The predicted versus observed data is correlated with a linear model characterized by an R2 of 0.69, which is similar to the ones observed in other virtual sensors.

In this research paper, the spatial distributions of five different services—Voice over Internet Protocol (VoIP), Video Conferencing (VC), Hypertext Transfer Protocol (HTTP), and Electronic Mail—are investigated using three different approaches: circular, random, and uniform approaches. The amount of each service varies from one to another. In certain distinct settings, which are collectively referred to as mixed applications, a variety of services are activated and configured at predetermined percentages. These services run simultaneously. Furthermore, this paper has established a new algorithm to assess both the real-time and best-effort services of the various IEEE 802.11 technologies, describing the best networking architecture as either a Basic Service Set (BSS), an Extended Service Set (ESS), or an Independent Basic Service Set (IBSS). Due to this fact, the purpose of our research is to provide the user or client with an analysis that suggests a suitable technology and network configuration without wasting resources on unnecessary technologies or requiring a complete re-setup. In this context, this paper presents a network prioritization framework for enabling smart environments to determine an appropriate WLAN standard or a combination of standards that best supports a specific set of smart network applications in a specified environment. A network QoS modeling technique for smart services has been derived for assessing best-effort HTTP and FTP, and the real-time performance of VoIP and VC services enabled via IEEE 802.11 protocols in order to discover more optimal network architecture. A number of IEEE 802.11 technologies have been ranked by using the proposed network optimization technique with separate case studies for the circular, random, and uniform geographical distributions of smart services. The performance of the proposed framework is validated using a realistic smart environment simulation setting, considering both real-time and best-effort services as case studies with a range of metrics related to smart environments.

The monitoring of the coastal environment is a crucial factor in ensuring its proper management. Nevertheless, existing monitoring technologies are limited due to their cost, temporal resolution, and maintenance needs. Therefore, limited data are available for coastal environments. In this paper, we present a low-cost multiparametric probe that can be deployed in coastal areas and integrated into a wireless sensor network to send data to a database. The multiparametric probe is composed of physical sensors capable of measuring water temperature, salinity, and total suspended solids (TSS). The node can store the data in an SD card or send them. A real-time clock is used to tag the data and to ensure data gathering every hour, putting the node in deep sleep mode in the meantime. The physical sensors for salinity and TSS are created for this probe and calibrated. The calibration results indicate that no effect of temperature is found for both sensors and no interference of salinity in the measuring of TSS or vice versa. The obtained calibration model for salinity is characterised by a correlation coefficient of 0.9 and a Mean Absolute Error (MAE) of 0.74 g/L. Meanwhile, different calibration models for TSS were obtained based on using different light wavelengths. The best case was using a simple regression model with blue light. The model is characterised by a correlation coefficient of 0.99 and an MAE of 12 mg/L. When both infrared and blue light are used to prevent the effect of different particle sizes, the determination coefficient of 0.98 and an MAE of 57 mg/L characterised the multiple regression model.

The Internet of Things (IoT) refers to an emerging paradigm to seamlessly and ubiquitously integrate a large number of smart things with intra/inter links to the physical and cyber worlds. Sensor-enabled communication technologies connect billions of things by efficiently utilizing their locations in the real world. This has fostered active participation and tangible creation of benefits to the economy and ultimately to society. As the so-called smart things are extremely diverse and heterogeneous in terms of computing and communication technology and resource capability, there is no standalone solution toward realization of service-centric provisioning in the IoT. The service domain in IoT environments is also diverse, including energy efficiency, computing capability, coordination time, resource harvesting capacity, dimension of things, and so on. The growing diversity enforces the necessity to address the most significant issue of technological complexity, for practical enhancement and evaluation of quality of service (QoS) and quality of experience (QoE), for service-oriented use cases in IoT environments.

The remote location of agricultural fields leads to the difficulty of deploying Precision Agriculture (PA) systems as there is no Internet access in those areas. Therefore, the use of long-range wireless technologies such as LoRa can provide connectivity to rural areas and allow monitoring PA systems remotely. In this paper, a heterogeneous architecture and protocol that allows communication with both WiFi and LoRa, including multiple hops in LoRa are presented. The design is based on a tree topology comprised of electronic devices deployed on different areas of interest for PA systems such as the canals of irrigation water, the fields, and the urban areas that generate wastewater. A set of practical tests with different configurations have been performed to determine the correct operation of the proposed protocol. The results show that the consumed bandwidth for both 433 MHz and 868 MHz frequency bands remained within the limits for the most restrictive LoRa configurations. Therefore, different deployment needs can be addressed with the implementation of this proposal. Furthermore, the use of packet transmission delays of 500 ms at the CH node results in high successful packet delivery rates.

Abstract Cellular networks based on new generation standards are the major enabler for Internet of things (IoT) communication. Narrowband-IoT and Long Term Evolution for Machines are the newest wide area network-based cellular technologies for IoT applications. The deployment of unmanned aerial vehicles (UAVs) has gained the popularity in cellular networks by using temporary ubiquitous coverage in the areas where the infrastructure-based networks are either not available or have vanished due to some disasters. The major challenge in such networks is the efficient UAVs deployment that covers maximum users and area with the minimum number of UAVs. The performance and sustainability of UAVs is largely dependent upon the available residual energy especially in mission planning. Although energy harvesting techniques and efficient storage units are available, but these have their own constraints and the limited onboard energy still severely hinders the practical realization of UAVs. This paper employs neglected parameters of UAVs energy consumption in order to get actual status of available energy and proposed a solution that more accurately estimates the UAVs operational airtime. The proposed model is evaluated in test bed and simulation environment where the results show the consideration of such explicit usage parameters achieves significant improvement in airtime estimation.

Internet of Things (IoT) is rapidly gaining momentum in the scenario of telecommunications. Conventional networks allow for interactivity and data exchange, but these networks have not been designed for the new features and functions of IoT devices. In this paper, an algorithm is proposed to share common recourse among Things, that is, between different types of smart appliances. This proposal is based on an IoT network with centralized management architecture, controlled by an Artificial Intelligence (AI). The AI controller uses an algorithm which based on machine learning techniques, collecting information on the network through an information protocol. Every smart thing that connects to the network is announces through a protocol message called Function and Service Discovery Protocol (DFSP) over the queued message telemetry transport protocol (MQTT). The proposed algorithm is responsible for discovering and allocating resources in the networks. As a result, using our proposed algorithm in communication system provides the outperform efficiency and availability than that used in conventional communication systems for the integrate IoT devices.

Computer networks become complex and dynamic structures. As a result of this fact, the configuration and the managing of this whole structure is a challenging activity. Software-Defined Networks(SDN) is a new network paradigm that, through an abstraction of network plans, seeks to separate the control plane and data plane, and tends as an objective to overcome the limitations in terms of network infrastructure configuration. As in the traditional network environment, the SDN environment is also liable to security vulnerabilities. This work presents a system of detection and mitigation of Distributed Denial of Service (DDoS) attacks and Portscan attacks in SDN environments (LSTM-FUZZY). The LSTM-FUZZY system presented in this work has three distinct phases: characterization, anomaly detection, and mitigation. The system was tested in two scenarios. In the first scenario, we applied IP flows collected from the SDN Floodlight controllers through emulation on Mininet. On the other hand, in the second scenario, the CICDDoS 2019 dataset was applied. The results gained show that the efficiency of the system to assist in network management, detect and mitigate the occurrence of the attacks.

The IEEE 802.15.4 standard is one of the widely adopted networking specification for Internet of Things (IoT). It defines several physical layer (PHY) options and medium access control (MAC) sub-layer protocols for interconnection of constrained wireless devices. These devices are usually battery-powered and need to support requirements like low-power consumption and low-data rates. The standard has been revised twice to incorporate new PHY layers and improvements learned from implementations. Research in this direction has been primarily centered around improving the energy consumption of devices. Recently, to meet specific Quality-of-Service (QoS) requirements of different industrial applications, the IEEE 802.15.4e amendment was released that focuses on improving reliability, robustness and latency. In this paper, we carry out a performance-to-cost analysis of Deterministic and Synchronous Multi-channel Extension (DSME) and Time-slotted Channel Hopping (TSCH) MAC modes of IEEE 802.15.4e with 802.15.4 MAC protocol to analyze the trade-off of choosing a particular MAC mode over others. The parameters considered for performance are throughput and latency, and the cost is quantified in terms of energy. A Markov model has been developed for TSCH MAC mode to compare its energy costs with 802.15.4 MAC. Finally, we present the applicability of different MAC modes to different application scenarios.

Internet of Things (IoT) is becoming highly supportive in innovative technological solutions for assisting impaired people. Some of these IoT solutions are still in a prototyping phase ignoring possible attacks and the corresponding security defenses. This article proposes a learning-based approach for defending against perception-layer attacks performed on specific sensor types in smart furniture for impaired people. This approach is based on the analysis of time series by means of dynamic time warping algorithm for calculating similarity and a novel detector for identifying anomalies. This approach has been illustrated by defending against simulated perception-layer magnetic attacks on a smart cupboard with door magnetic sensors. The results show the performance of the proposed approach for properly identifying these attacks. In particular, these results advocate an accuracy about 95.5% per day.

Cloud computing is one of the most successful business models for providing a simple pay-as-you-go, and therefore is gaining much popularity in the industry. Customers and enterprises can maintain or scale-up a business easily while cutting down on their budget. However, energy consumption is one of the biggest problems in current cloud computing. It is both essential and urgent for governmental and industrial institutions to address this, to achieve rapid growth. The development of energy-efficient cloud computing has to be taken into consideration, which relies on the development of several key technologies: More energy-efficient mediums can be used in cloud computing at the platform level; energy-efficient scheduling algorithms, memory systems, storage systems, resource management policies, etc., can be adopted at the hypervisor level; energy-efficient scheduling, communications, and applications can be applied at the virtual machine level; and energy-efficient mobile cloud computing will be developed, involving green networking and wireless communications, cloud-based mobile applications, and limited resources management.

Internet of Vehicle (IoV) is a sub class of vehicular ad hoc networks with more advanced cloud and Internet-enabled services. These networks offer various types of safety and infotainment services and provide comfortability and safety to passengers as well as to the drivers. Due to the high mobility of nodes, the nodes are out from its communication range and the information becomes outdated and causes link disconnections and packet dropping. Most feasible routing protocols are needed to provide in-time data communication, handle high mobility of nodes, dynamic topologies and unpredictable environments of these networks. In this paper, we proposed SACBR (Self-Assessment Cluster-based Routing) protocol in which the Cluster Heads (CHs) can communicate with other CHs. Every vehicle node initiates a self-assessment approach based on more appropriate routing metrics and elects the CH for every cluster and then collects the data from member nodes and further forward the data to other CHs. The CH is responsible to manage its own and member nodes' data forwarding process. The proposed protocol provides more stability and less overhead compared to the aggregation method where every node exchanges its data with a one-hop neighbor. Proposed protocol suites sparse and dense traffic scenarios where most of the time vehicle nodes are moving in platoons or snaking structures. The experimental results show the better performance of SACBR compared to state-of-the-art protocols.

In general, reliable PV generation prediction is required to increase complete control quality and avoid potential damage. Accurate forecasting of direct solar radiation trends in PV power production could limit the influence of uncertainties on photovoltaics, enhance organizational dependability, and maximize the utilization factor of the PV systems for something such as an energy management system (EMS) of microgrids. This paper proposes an intelligent prediction of energy production level in large PV plants through AUTO-encoder-based Neural-Network (AUTO-NN) with Restricted Boltzmann feature extraction. Here, the solar energy output may be projected using prior sun illumination and meteorological data. The feature selection and prediction modules use an AUTO encoder-based Neural Network to improve the process of energy prediction (AUTO-NN). Restricted Boltzmann Machines (RBM) can be used during a set of regulations for development-based feature extraction. The proposed model’s result is evaluated using various constraints. As a result, the proposed AUTO-NN achieved 58.72% of RMSE (Root Mean Square Error), 62.72% of nRMSE (Normalized Root Mean Square Error), 48.04% of MaxAE (Maximum Absolute Error), 48.66% of (Mean Absolute Error), and 46.76% of (Mean Absolute Percentage Error).

Location awareness is the key to success to many location-based services applications such as indoor navigation, elderly tracking, emergency management, and so on. Trilateration-based localization using received signal strength measurements is widely used in wireless sensor network–based localization and tracking systems due to its simplicity and low computational cost. However, localization accuracy obtained with the trilateration technique is generally very poor because of fluctuating nature of received signal strength measurements. The reason behind such notorious behavior of received signal strength is dynamicity in target motion and surrounding environment. In addition, the significant localization error is induced during each iteration step during trilateration, which gets propagated in the next iterations. To address this problem, this article presents an improved trilateration-based architecture named Trilateration Centroid Generalized Regression Neural Network. The proposed Trilateration Centroid Generalized Regression Neural Network–based localization algorithm inherits the simplicity and efficiency of three concepts namely trilateration, centroid, and Generalized Regression Neural Network. The extensive simulation results indicate that the proposed Trilateration Centroid Generalized Regression Neural Network algorithm demonstrates superior localization performance as compared to trilateration, and Generalized Regression Neural Network algorithm.

Cloud computing leads to efficient resource allocation for network users. In order to achieve efficient allocation, many research activities have been conducted so far. Some researchers focus on classical optimisation theory techniques (such as multi-objective optimisation, evolutionary optimisation, game theory, etc.) to satisfy network providers and network users’ service-level agreement (SLA) requirements. Normally, in a cloud data centre network (CDCN), it is difficult to jointly satisfy both the cloud provider and cloud customer’ utilities, and this leads to complex combinatorial problems, which are usually NP-hard. Recently, machine learning and artificial intelligence techniques have received much attention from the networking community because of their capability to solve complicated networking problems. In the current work, at first, the holistic utility satisfaction for the cloud data centre provider and customers is formulated as a reinforcement learning (RL) problem with a specific reward function, which is a convex summation of users’ utility functions and cloud provider’s utility. The user utility functions are modelled as a function of cloud virtualised resources (such as storage, CPU, RAM), connection bandwidth, and also, the network-based expected packet loss and round-trip time factors associated with the cloud users. The cloud provider utility function is modelled as a function of resource prices and energy dissipation costs. Afterwards, a Q-learning implementation of the mentioned RL algorithm is introduced, which is able to converge to the optimal solution in an online and fast manner. The simulation results exhibit the enhanced convergence speed and computational complexity properties of the proposed method in comparison with similar approaches from the joint cloud customer/provider utility satisfaction perspective. To evaluate the scalability property of the proposed method, the results are also repeated for different cloud user population scenarios (small, medium, and large).

Mobility in urban and interurban areas, mainly by cars, is a day-to-day activity of many people. However, some of its main drawbacks are traffic jams and accidents. Newly made vehicles have pre-installed driving evaluation systems, which can prevent accidents. However, most cars on our roads do not have driver assessment systems. In this paper, we propose an approach for recognising driving styles and enabling drivers to reach safer and more efficient driving. The system consists of two physical sensors connected to a device node with a display and a speaker. An artificial neural network (ANN) is included in the node, which analyses the data from the sensors, and then recognises the driving style. When an abnormal driving pattern is detected, the speaker will play a warning message. The prototype was assembled and tested using an interurban road, in particular on a conventional road with three driving styles. The gathered data were used to train and validate the ANN. Results, in terms of accuracy, indicate that better accuracy is obtained when the velocity, position (latitude and longitude), time, and turning speed for the 3-axis are used, offering an average accuracy of 83%. If the classification is performed considering just two driving styles, normal and aggressive, then the accuracy reaches 92%. When the geo-information and time data are included, the main novelty of this paper, the classification accuracy is improved by 13%.

In recent years, gesture recognition and speech recognition, as important input methods in Human–Computer Interaction (HCI), have been widely used in the field of virtual reality. In particular, with the rapid development of deep learning, artificial intelligence, and other computer technologies, gesture recognition and speech recognition have achieved breakthrough research progress. The search platform used in this work is mainly the Google Academic and literature database Web of Science. According to the keywords related to HCI and deep learning, such as “intelligent HCI”, “speech recognition”, “gesture recognition”, and “natural language processing”, nearly 1000 studies were selected. Then, nearly 500 studies of research methods were selected and 100 studies were finally selected as the research content of this work after five years (2019–2022) of year screening. First, the current situation of the HCI intelligent system is analyzed, the realization of gesture interaction and voice interaction in HCI is summarized, and the advantages brought by deep learning are selected for research. Then, the core concepts of gesture interaction are introduced and the progress of gesture recognition and speech recognition interaction is analyzed. Furthermore, the representative applications of gesture recognition and speech recognition interaction are described. Finally, the current HCI in the direction of natural language processing is investigated. The results show that the combination of intelligent HCI and deep learning is deeply applied in gesture recognition, speech recognition, emotion recognition, and intelligent robot direction. A wide variety of recognition methods were proposed in related research fields and verified by experiments. Compared with interactive methods without deep learning, high recognition accuracy was achieved. In Human–Machine Interfaces (HMIs) with voice support, context plays an important role in improving user interfaces. Whether it is voice search, mobile communication, or children’s speech recognition, HCI combined with deep learning can maintain better robustness. The combination of convolutional neural networks and long short-term memory networks can greatly improve the accuracy and precision of action recognition. Therefore, in the future, the application field of HCI will involve more industries and greater prospects are expected.

The Wireless Medium Access Control (WMAC) protocol functions by handling various data frames in order to forward them to neighbor sensor nodes. Under this circumstance, WMAC policies need secure data communication rules and intrusion detection procedures to safeguard the data from attackers. The existing secure Medium Access Control (MAC) policies provide expected and predictable practices against channel attackers. These security policies can be easily breached by any intelligent attacks or malicious actions. The proposed Wireless Interleaved Honeypot-Framing Model (WIHFM) newly implements distributed honeypot-based security mechanisms in each sensor node to act reactively against various attackers. The proposed WIHFM creates an optimal Wireless Sensor Network (WSN) channel model, Wireless Interleaved Honeypot Frames (WIHFs), secure hash-based random frame-interleaving principles, node-centric honeypot engines, and channel-covering techniques. Compared to various existing MAC security policies, the proposed model transforms unpredictable IHFs into legitimate frame sequences against channel attackers. Additionally, introducing WIHFs is a new-fangled approach for distributed WSNs. The successful development of the proposed WIHFM ensures resilient security standards and neighbor-based intrusion alert procedures for protecting MAC frames. Particularly, the proposed wireless honeypot methodology creates a novel idea of using honeypot frame traps against open wireless channel attacks. The development of a novel wireless honeypot traps deals with various challenges such as distributed honeypot management principles (node-centric honeypot, secretly interleaved-framing principles, and interleaving/de-interleaving procedures), dynamic network backbone management principles (On Demand Acyclic Connectivity model), and distributed attack isolation policies. This effort provides an effective wireless attack-trapping solution in dynamic WSNs. The simulation results show the advantage of the proposed WIHFM over the existing techniques such as Secure Zebra MAC (SZ-MAC), Blockchain-Assisted Secure-Routing Mechanism (BASR), and the Trust-Based Node Evaluation (TBNE) procedure. The experimental section confirms the proposed model attains a 10% to 14% superior performance compared to the existing techniques.

In recent decades, networked smart devices and cutting-edge technology have been exploited in many applications for the improvement of agriculture. The deployment of smart sensors and intelligent farming techniques supports real-time information gathering for the agriculture sector and decreases the burden on farmers. Many solutions have been presented to automate the agriculture system using IoT networks; however, the identification of redundant data traffic is one of the most significant research problems. Additionally, farmers do not obtain the information they need in time, such as data on water pressure and soil conditions. Thus, these solutions consequently reduce the production rates and increase costs for farmers. Moreover, controlling all agricultural operations in a controlled manner should also be considered in developing intelligent solutions. Therefore, this study proposes a framework for a system that combines fog computing with smart farming and effectively controls network traffic. Firstly, the proposed framework efficiently monitors redundant information and avoids the inefficient use of communication bandwidth. It also controls the number of re-transmissions in the case of malicious actions and efficiently utilizes the network’s resources. Second, a trustworthy chain is built between agricultural sensors by utilizing the fog nodes to address security issues and increase reliability by preventing malicious communication. Through extensive simulation-based experiments, the proposed framework revealed an improved performance for energy efficiency, security, and network connectivity in comparison to other related works.

In-vehicle communication has become an integral part of today’s driving environment considering the growing add-ons of sensor-centric communication and computing devices inside a vehicle for a range of purposes including vehicle monitoring, physical wiring reduction, and driving efficiency. However, related literature on cyber security for in-vehicle communication systems is still lacking potential dedicated solutions for in-vehicle cyber risks. Existing solutions are mainly relying on protocol-specific security techniques and lacking an overall security framework for in-vehicle communication. In this context, this paper critically explores the literature on cyber security for in-vehicle communication focusing on technical architecture, methodologies, challenges, and possible solutions. In-vehicle communication network architecture is presented considering key components, interfaces, and related technologies. The protocols for in-vehicle communication have been classified based on their characteristics, and usage type. Security solutions for in-vehicle communication have been critically reviewed considering machine learning, cryptography, and port-centric techniques. A multi-layer secure framework is also developed as a protocol and use case-independent in-vehicle communication solution. Finally, open challenges and future dimensions of research for in-vehicle communication cyber security are highlighted as observations and recommendations.

In recent years, with the rapid development of information technology and the increasing maturity of communication technology, embedded computing technology, sensing technology and automatic control technology have made tremendous impact on our lives. The perfect integration of computing process and physical process Internet-of-Things (IOT) has attracted much attention from government, academia and industry.

The variety of smart things connected to Internet hampers the possibility of having a stand-alone solution for service-centric provisioning in the Internet of Things (IoT). The different features of smart objects in processing capabilities, memory, and size make it difficult for final users to learn the installation and usage of all these devices in collaboration with other IoT objects, hindering the user experience. In this context, we propose a collaboration mechanism for IoT devices based on the multi-agent systems with mobile agents. This paper illustrates the current approach with smart cupboards for potentially tracking memory losses. The user study revealed that users found working products of this approach usable, easy-to-learn and useful, and they agreed that the current approach could provide a high quality of experience not only in the specific case of service-centric IoT devices for tracking memory losses but also in other domains. The learning capability by means of this approach was showed with significant reductions of reaction times and number of errors over the first and second tests with the current approach. System response times were appropriate for both continuous rendering and presenting the classification results. The usage of RAM memory was also adequate for the common actual devices.

The integration of cognitive radio (CR) technology with the future Internet-of-Things (IoT) architecture is expected to allow effective massive IoT deployment by providing huge spectrum opportunities to the IoT devices. Several communication protocols have been proposed for the CR networks while ignoring the adjacent channel interference (ACI) problem by assuming sharp filters at the transmit and receive chains of each CR device. However, in practice, such an assumption is not feasible for low-cost hardware-constrained CR-capable IoT (CR-IoT) devices. Specifically, when a large number of CR-IoT devices are operating in the same vicinity, guard-band channels (GBs) are needed to mitigate the ACI problem, introducing GB adds constraints on the efficient use of spectrum and protocol design. In this paper, we develop a channel assignment mechanism for the hardware-constrained CR-IoT networks under time-varying channel conditions with GB-awareness. The objective of our assignment is to serve the largest possible number of CR-IoT devices by assigning the least number of idle channels to each device subject to rate demand and interference constraints. The proposed channel assignment in this paper is conducted on a per-block basis for the contending CR-IoT devices while considering the time-varying channel conditions for each CRIoT transmission over each idle channel, such that spectrum efficiency is improved. Specifically, our channel assignment problem is formulated as a binary linear programming problem, which is NP-hard. Thus, we propose a polynomial-time solution using a sequential fixing algorithm that achieves a suboptimal solution. The simulation results demonstrate that our proposed assignment provides significant increase in the number of served IoT devices over existing assignment mechanisms.

Internet of Things (IoT) widely use analysis of data with artificial intelligence (AI) techniques in order to learn from user actions, support decisions, track relevant aspects of the user, and notify certain events when appropriate. However, most AI techniques are based on mathematical models that are difficult to understand by the general public, so most people use AI-based technology as a black box that they eventually start to trust based on their personal experience. This article proposes to go a step forward in the use of AI in IoT, and proposes a novel approach within the Human-centric AI field for generating explanations about the knowledge learned by a neural network (in particular a multilayer perceptron) from IoT environments. More concretely, this work proposes two techniques based on the analysis of artificial neuron weights, and another technique aimed at explaining each estimation based on the analysis of training cases. This approach has been illustrated in the context of a smart IoT kitchen that detects the user depression based on the food used for each meal, using a simulator for this purpose. The results revealed that most auto-generated explanations made sense in this context (i.e. 97.0%), and the execution times were low (i.e. 1.5 ms or lower) even considering the common configurations varying independently the number of neurons per hidden layer (up to 20), the number of hidden layers (up to 20) and the number of training cases (up to 4,000).

Recent developments in communication and information technologies, especially in the internet of things (IoT), have greatly changed and improved the human lifestyle. Due to the easy access to, and increasing demand for, smart devices, the IoT system faces new cyber-physical security and privacy attacks, such as denial of service, spoofing, phishing, obfuscations, jamming, eavesdropping, intrusions, and other unforeseen cyber threats to IoT systems. The traditional tools and techniques are not very efficient to prevent and protect against the new cyber-physical security challenges. Robust, dynamic, and up-to-date security measures are required to secure IoT systems. The machine learning (ML) technique is considered the most advanced and promising method, and opened up many research directions to address new security challenges in the cyber-physical systems (CPS). This research survey presents the architecture of IoT systems, investigates different attacks on IoT systems, and reviews the latest research directions to solve the safety and security of IoT systems based on machine learning techniques. Moreover, it discusses the potential future research challenges when employing security methods in IoT systems.

Recent years have witnessed rapid development and great indignation burgeoning in the unmanned aerial vehicles (UAV) field. This growth of UAV-related research contributes to several challenges, including inter-communication from vehicle to vehicle, transportation coverage, network information gathering, network interworking effectiveness, etc. Due to ease of usage, UAVs have found novel applications in various areas such as agriculture, defence, security, medicine, and observation for traffic-monitoring applications. This paper presents an innovative drone system by designing and developing a blended-wing-body (BWB)-based configuration for next-generation drone use cases. The proposed method has several benefits, including a very low interference drag, evenly distributed load inside the body, and less radar signature compared to the state-of-the-art configurations. During the entire procedure, a standard design approach was followed to optimise the BWB framework for next-generation use cases by considering the typically associated parameters such as vertical take-off and landing and drag and stability of the BWB. Extensive simulation experiments were performed to carry out a performance analysis of the proposed model in a software-based environment. To further confirm that the model design of the BWB-UAV is fit to execute the targeted missions, the real-time working environments were tested through advanced numerical simulation and focused on avoiding cost and unwanted wastages. To enhance the trustworthiness of this said computational fluid dynamics (CFD) analysis, grid convergence test-based validation was also conducted. Two different grid convergence tests were conducted on the induced velocity of the Version I UAV and equivalent stress of the Version II UAV. Finite element analysis-based computations were involved in estimating structural outcomes. Finally, the mesh quality was obtained as 0.984 out of 1. The proposed model is very cost-effective for performing a different kind of manoeuvring activities with the help of its unique design at reasonable mobility speed and hence can be modelled for high-speed-based complex next-generation use cases.

In the last few years, the Internet of things (IoT) has recently gained attention in developing various smart city applications such as smart healthcare, smart supply chain, smart home, smart grid, etc. The existing literature focuses on the smart healthcare system as a public emergency service (PES) to provide timely treatment to the patient. However, little attention is given to a distributed smart fire brigade system as a PES to protect human life and properties from severe fire damage. The traditional PES are developed on a centralised system, which requires high computation and does not ensure timely service fulfilment. Furthermore, these traditional PESs suffer from a lack of trust, transparency, data integrity, and a single point of failure issue. In this context, this paper proposes a Blockchain-Enabled Secure and Trusted (BEST) framework for PES in the smart city environment. The BEST framework focuses on providing a fire brigade service as a PES to the smart home based on IoT device information to protect it from serious fire damage. Further, we used two edge computing servers, an IoT controller and a service controller. The IoT and service controller are used for local storage and to enhance the data processing speed of PES requests and PES fulfilments, respectively. The IoT controller manages an access control list to keep track of registered IoT gateways and their IoT devices, avoiding misguiding the PES department. The service controller utilised the queue model to handle the PES requests based on the minimum service queue length. Further, various smart contracts are designed on the Hyperledger Fabric platform to automatically call a PES either in the presence or absence of the smart-home owner under uncertain environmental conditions. The performance evaluation of the proposed BEST framework indicates the benefits of utilising the distributed environment and the smart contract logic. The various simulation results are evaluated in terms of service queue length, utilisation, actual arrival time, expected arrival time, number of PES departments, number of PES providers, and end-to-end delay. These simulation results show the effectiveness and feasibility of the BEST framework.